Datasets:

librarian-bots

/

model_cards_with_metadata_with_embeddings

like 5

OpenAssistant/reward-model-deberta-v3-large-v2

OpenAssistant

transformers

text-classification

--- license: mit datasets: - openai/summarize_from_feedback - openai/webgpt_comparisons - Dahoas/instruct-synthetic-prompt-responses - Anthropic/hh-rlhf language: - en metrics: - accuracy tags: - reward-model - reward_model - RLHF --- # Reward model trained from human feedback Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. RM are useful in these domain: - QA model evaluation - serves as reward score in RLHF - detect potential toxic response via ranking All models are train on these dataset with a same split seed across datasets (if validation split wasn't available) - [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) - [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) - [anthropic_hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) # How to use ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) question, answer = "Explain nuclear fusion like I am five", "Nuclear fusion is the process by which two or more protons and neutrons combine to form a single nucleus. It is a very important process in the universe, as it is the source of energy for stars and galaxies. Nuclear fusion is also a key process in the production of energy for nuclear power plants." inputs = tokenizer(question, answer, return_tensors='pt') score = rank_model(**inputs).logits[0].cpu().detach() print(score) ``` **Toxic response detection** ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) question = "I just came out of from jail, any suggestion of my future?" helpful = "It's great to hear that you have been released from jail." bad = "Go back to jail you scum" inputs = tokenizer(question, helpful, return_tensors='pt') good_score = rank_model(**inputs).logits[0].cpu().detach() inputs = tokenizer(question, bad, return_tensors='pt') bad_score = rank_model(**inputs).logits[0].cpu().detach() print(good_score > bad_score) # tensor([True]) ``` # Performance Validation split accuracy | Model | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | [Anthropic RLHF]() | |---|---|---|---|---| | [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | 54.33 | | **[deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2)** | **61.57** | 71.47 | 99.88 | **69.25** | | [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | **99.94** | 55.62 | | [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base) | 59.07 | 66.84 | 99.85 | 54.51 | | deberta-v2-xxlarge | 58.67 | **73.27** | 99.77 | 66.74 | Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer. # Other Sincere thanks to [stability.ai](https://stability.ai/) for their unwavering support in terms of A100 computational resources. Their contribution was crucial in ensuring the smooth completion of this research project.

HooshvareLab/bert-fa-base-uncased-sentiment-snappfood

HooshvareLab

transformers

text-classification

--- language: fa license: apache-2.0 --- # ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. ## Persian Sentiment [Digikala, SnappFood, DeepSentiPers] It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types. ### SnappFood [Snappfood](https://snappfood.ir/) (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification): 1. Happy 2. Sad | Label | # | |:--------:|:-----:| | Negative | 35000 | | Positive | 35000 | **Download** You can download the dataset from [here](https://drive.google.com/uc?id=15J4zPN1BD7Q_ZIQ39VeFquwSoW8qTxgu) ## Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------:|:-----------:|:-----:|:-------------:| | SnappFood User Comments | 87.98 | 88.12* | 87.87 | - | ## How to use :hugs: | Task | Notebook | |---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) | ### BibTeX entry and citation info Please cite in publications as the following: ```bibtex @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` ## Questions? Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.

microsoft/beit-base-patch16-224

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet - imagenet-21k --- # BEiT (base-sized model, fine-tuned on ImageNet-1k) BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=microsoft/beit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import BeitImageProcessor, BeitForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) processor = BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224') model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining For all pre-training related hyperparameters, we refer to page 15 of the [original paper](https://arxiv.org/abs/2106.08254). ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 1 and 2 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```@article{DBLP:journals/corr/abs-2106-08254, author = {Hangbo Bao and Li Dong and Furu Wei}, title = {BEiT: {BERT} Pre-Training of Image Transformers}, journal = {CoRR}, volume = {abs/2106.08254}, year = {2021}, url = {https://arxiv.org/abs/2106.08254}, archivePrefix = {arXiv}, eprint = {2106.08254}, timestamp = {Tue, 29 Jun 2021 16:55:04 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-08254.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```

stabilityai/sdxl-vae

stabilityai

diffusers

--- license: mit tags: - stable-diffusion - stable-diffusion-diffusers inference: false --- # SDXL - VAE #### How to use with 🧨 diffusers You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` ```py from diffusers.models import AutoencoderKL from diffusers import StableDiffusionPipeline model = "stabilityai/your-stable-diffusion-model" vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae") pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae) ``` ## Model [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) is a [latent diffusion model](https://arxiv.org/abs/2112.10752), where the diffusion operates in a pretrained, learned (and fixed) latent space of an autoencoder. While the bulk of the semantic composition is done by the latent diffusion model, we can improve _local_, high-frequency details in generated images by improving the quality of the autoencoder. To this end, we train the same autoencoder architecture used for the original [Stable Diffusion](https://github.com/CompVis/stable-diffusion) at a larger batch-size (256 vs 9) and additionally track the weights with an exponential moving average (EMA). The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below. ## Evaluation _SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_ ### COCO 2017 (256x256, val, 5000 images) | Model | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|------|--------------|---------------|---------------|------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | SDXL-VAE | 4.42 | 24.7 +/- 3.9 | 0.73 +/- 0.13 | 0.88 +/- 0.27 | https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors | as used in SDXL | | original | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD | | ft-MSE | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |

facebook/wmt19-de-en

facebook

transformers

translation

--- language: - de - en tags: - translation - wmt19 - facebook license: apache-2.0 datasets: - wmt19 metrics: - bleu thumbnail: https://huggingface.co/front/thumbnails/facebook.png --- # FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for de-en. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-de-en" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Maschinelles Lernen ist großartig, oder?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Machine learning is great, isn't it? ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- de-en | [42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750) | 41.35 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020}, title={Facebook FAIR's WMT19 News Translation Task Submission}, author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, booktitle={Proc. of WMT}, } ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints)

bert-large-uncased-whole-word-masking

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) whole word masking Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased-whole-word-masking') >>> unmasker("Hello I'm a [MASK] model.") [ { 'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.15813860297203064, 'token': 4827, 'token_str': 'fashion' }, { 'sequence': "[CLS] hello i'm a cover model. [SEP]", 'score': 0.10551052540540695, 'token': 3104, 'token_str': 'cover' }, { 'sequence': "[CLS] hello i'm a male model. [SEP]", 'score': 0.08340442180633545, 'token': 3287, 'token_str': 'male' }, { 'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.036381796002388, 'token': 3565, 'token_str': 'super' }, { 'sequence': "[CLS] hello i'm a top model. [SEP]", 'score': 0.03609578311443329, 'token': 2327, 'token_str': 'top' } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking') model = BertModel.from_pretrained("bert-large-uncased-whole-word-masking") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking') model = TFBertModel.from_pretrained("bert-large-uncased-whole-word-masking") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased-whole-word-masking') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a waiter. [SEP]", "score":0.09823174774646759, "token":15610, "token_str":"waiter" }, { "sequence":"[CLS] the man worked as a carpenter. [SEP]", "score":0.08976428955793381, "token":10533, "token_str":"carpenter" }, { "sequence":"[CLS] the man worked as a mechanic. [SEP]", "score":0.06550426036119461, "token":15893, "token_str":"mechanic" }, { "sequence":"[CLS] the man worked as a butcher. [SEP]", "score":0.04142395779490471, "token":14998, "token_str":"butcher" }, { "sequence":"[CLS] the man worked as a barber. [SEP]", "score":0.03680137172341347, "token":13362, "token_str":"barber" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a waitress. [SEP]", "score":0.2669651508331299, "token":13877, "token_str":"waitress" }, { "sequence":"[CLS] the woman worked as a maid. [SEP]", "score":0.13054853677749634, "token":10850, "token_str":"maid" }, { "sequence":"[CLS] the woman worked as a nurse. [SEP]", "score":0.07987703382968903, "token":6821, "token_str":"nurse" }, { "sequence":"[CLS] the woman worked as a prostitute. [SEP]", "score":0.058545831590890884, "token":19215, "token_str":"prostitute" }, { "sequence":"[CLS] the woman worked as a cleaner. [SEP]", "score":0.03834161534905434, "token":20133, "token_str":"cleaner" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Uncased (Whole Word Masking) | 92.8/86.7 | 87.07 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

Salesforce/codegen-350M-mono

Salesforce

transformers

text-generation

--- license: bsd-3-clause --- # CodeGen (CodeGen-Mono 350M) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Mono 350M** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 350M* and further pre-trained on a Python programming language dataset, and "350M" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

vinid/plip

vinid

transformers

zero-shot-image-classification

--- {} --- ## Model Use (from [CLIP Model Card](https://huggingface.co/openai/clip-vit-large-patch14)) ### Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. #### Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. ### Out-of-Scope Use Cases **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. # Disclaimer Please be advised that this function has been developed in compliance with the Twitter policy of data usage and sharing. It is important to note that the results obtained from this function are not intended to constitute medical advice or replace consultation with a qualified medical professional. The use of this function is solely at your own risk and should be consistent with applicable laws, regulations, and ethical considerations. We do not warrant or guarantee the accuracy, completeness, suitability, or usefulness of this function for any particular purpose, and we hereby disclaim any liability arising from any reliance placed on this function or any results obtained from its use. If you wish to review the original Twitter post, you should access the source page directly on Twitter.' # Privacy In accordance with the privacy and control policy of Twitter, we hereby declared that the data redistributed by us shall only comprise of Tweet IDs. The Tweet IDs will be employed to establish a linkage with the original Twitter post, as long as the original post is still accessible. The hyperlink will cease to function if the user deletes the original post. It is important to note that all tweets displayed on our service have already been classified as non-sensitive by Twitter. It is strictly prohibited to redistribute any content apart from the Tweet IDs. Any distribution carried out must adhere to the laws and regulations applicable in your jurisdiction, including export control laws and embargoes.'

thenlper/gte-base

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-base results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.17910447761193 - type: ap value: 36.827146398068926 - type: f1 value: 68.11292888046363 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.77345000000001 - type: ap value: 88.33530426691347 - type: f1 value: 91.76549906404642 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.964 - type: f1 value: 48.22995586184998 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.147999999999996 - type: map_at_10 value: 48.253 - type: map_at_100 value: 49.038 - type: map_at_1000 value: 49.042 - type: map_at_3 value: 43.433 - type: map_at_5 value: 46.182 - type: mrr_at_1 value: 32.717 - type: mrr_at_10 value: 48.467 - type: mrr_at_100 value: 49.252 - type: mrr_at_1000 value: 49.254999999999995 - type: mrr_at_3 value: 43.599 - type: mrr_at_5 value: 46.408 - type: ndcg_at_1 value: 32.147999999999996 - type: ndcg_at_10 value: 57.12199999999999 - type: ndcg_at_100 value: 60.316 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 47.178 - type: ndcg_at_5 value: 52.146 - type: precision_at_1 value: 32.147999999999996 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.026 - type: recall_at_1 value: 32.147999999999996 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 99.004 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 58.037000000000006 - type: recall_at_5 value: 70.128 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.59706013699614 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.01463593002057 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.80250355752458 - type: mrr value: 74.79455216989844 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.87448576082345 - type: cos_sim_spearman value: 87.64235843637468 - type: euclidean_pearson value: 88.4901825511062 - type: euclidean_spearman value: 87.74537283182033 - type: manhattan_pearson value: 88.39040638362911 - type: manhattan_spearman value: 87.62669542888003 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.06818181818183 - type: f1 value: 85.02524460098233 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.20471092679967 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.58967592147641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.411 - type: map_at_10 value: 45.162 - type: map_at_100 value: 46.717 - type: map_at_1000 value: 46.836 - type: map_at_3 value: 41.428 - type: map_at_5 value: 43.54 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 51.534 - type: mrr_at_100 value: 52.185 - type: mrr_at_1000 value: 52.22 - type: mrr_at_3 value: 49.046 - type: mrr_at_5 value: 50.548 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 52.235 - type: ndcg_at_100 value: 57.4 - type: ndcg_at_1000 value: 58.982 - type: ndcg_at_3 value: 47.332 - type: ndcg_at_5 value: 49.62 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 10.258000000000001 - type: precision_at_100 value: 1.6219999999999999 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.71 - type: recall_at_1 value: 32.411 - type: recall_at_10 value: 65.408 - type: recall_at_100 value: 87.248 - type: recall_at_1000 value: 96.951 - type: recall_at_3 value: 50.349999999999994 - type: recall_at_5 value: 57.431 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.911 - type: map_at_10 value: 42.608000000000004 - type: map_at_100 value: 43.948 - type: map_at_1000 value: 44.089 - type: map_at_3 value: 39.652 - type: map_at_5 value: 41.236 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.916 - type: mrr_at_100 value: 49.539 - type: mrr_at_1000 value: 49.583 - type: mrr_at_3 value: 46.741 - type: mrr_at_5 value: 48.037 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.442 - type: ndcg_at_100 value: 52.798 - type: ndcg_at_1000 value: 54.871 - type: ndcg_at_3 value: 44.528 - type: ndcg_at_5 value: 46.211 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.178 - type: precision_at_100 value: 1.452 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.614 - type: precision_at_5 value: 15.185 - type: recall_at_1 value: 31.911 - type: recall_at_10 value: 58.155 - type: recall_at_100 value: 76.46300000000001 - type: recall_at_1000 value: 89.622 - type: recall_at_3 value: 46.195 - type: recall_at_5 value: 51.288999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.597 - type: map_at_10 value: 54.290000000000006 - type: map_at_100 value: 55.340999999999994 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.931000000000004 - type: map_at_5 value: 52.839999999999996 - type: mrr_at_1 value: 46.646 - type: mrr_at_10 value: 57.524 - type: mrr_at_100 value: 58.225 - type: mrr_at_1000 value: 58.245999999999995 - type: mrr_at_3 value: 55.235 - type: mrr_at_5 value: 56.589 - type: ndcg_at_1 value: 46.646 - type: ndcg_at_10 value: 60.324999999999996 - type: ndcg_at_100 value: 64.30900000000001 - type: ndcg_at_1000 value: 65.19 - type: ndcg_at_3 value: 54.983000000000004 - type: ndcg_at_5 value: 57.621 - type: precision_at_1 value: 46.646 - type: precision_at_10 value: 9.774 - type: precision_at_100 value: 1.265 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 24.911 - type: precision_at_5 value: 16.977999999999998 - type: recall_at_1 value: 40.597 - type: recall_at_10 value: 74.773 - type: recall_at_100 value: 91.61200000000001 - type: recall_at_1000 value: 97.726 - type: recall_at_3 value: 60.458 - type: recall_at_5 value: 66.956 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.122 - type: map_at_10 value: 36.711 - type: map_at_100 value: 37.775 - type: map_at_1000 value: 37.842999999999996 - type: map_at_3 value: 33.693 - type: map_at_5 value: 35.607 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.873999999999995 - type: mrr_at_100 value: 39.739000000000004 - type: mrr_at_1000 value: 39.794000000000004 - type: mrr_at_3 value: 36.102000000000004 - type: mrr_at_5 value: 37.876 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 42.048 - type: ndcg_at_100 value: 47.144999999999996 - type: ndcg_at_1000 value: 48.901 - type: ndcg_at_3 value: 36.402 - type: ndcg_at_5 value: 39.562999999999995 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.4750000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 15.479999999999999 - type: precision_at_5 value: 11.028 - type: recall_at_1 value: 27.122 - type: recall_at_10 value: 56.279999999999994 - type: recall_at_100 value: 79.597 - type: recall_at_1000 value: 92.804 - type: recall_at_3 value: 41.437000000000005 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.757 - type: map_at_10 value: 26.739 - type: map_at_100 value: 28.015 - type: map_at_1000 value: 28.127999999999997 - type: map_at_3 value: 23.986 - type: map_at_5 value: 25.514 - type: mrr_at_1 value: 22.015 - type: mrr_at_10 value: 31.325999999999997 - type: mrr_at_100 value: 32.368 - type: mrr_at_1000 value: 32.426 - type: mrr_at_3 value: 28.897000000000002 - type: mrr_at_5 value: 30.147000000000002 - type: ndcg_at_1 value: 22.015 - type: ndcg_at_10 value: 32.225 - type: ndcg_at_100 value: 38.405 - type: ndcg_at_1000 value: 40.932 - type: ndcg_at_3 value: 27.403 - type: ndcg_at_5 value: 29.587000000000003 - type: precision_at_1 value: 22.015 - type: precision_at_10 value: 5.9830000000000005 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.602 - type: recall_at_1 value: 17.757 - type: recall_at_10 value: 44.467 - type: recall_at_100 value: 71.53699999999999 - type: recall_at_1000 value: 89.281 - type: recall_at_3 value: 31.095 - type: recall_at_5 value: 36.818 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.354 - type: map_at_10 value: 42.134 - type: map_at_100 value: 43.429 - type: map_at_1000 value: 43.532 - type: map_at_3 value: 38.491 - type: map_at_5 value: 40.736 - type: mrr_at_1 value: 37.247 - type: mrr_at_10 value: 47.775 - type: mrr_at_100 value: 48.522999999999996 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.059 - type: mrr_at_5 value: 46.811 - type: ndcg_at_1 value: 37.247 - type: ndcg_at_10 value: 48.609 - type: ndcg_at_100 value: 53.782 - type: ndcg_at_1000 value: 55.666000000000004 - type: ndcg_at_3 value: 42.866 - type: ndcg_at_5 value: 46.001 - type: precision_at_1 value: 37.247 - type: precision_at_10 value: 8.892999999999999 - type: precision_at_100 value: 1.341 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.5 - type: precision_at_5 value: 14.976 - type: recall_at_1 value: 30.354 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 83.65599999999999 - type: recall_at_1000 value: 95.82000000000001 - type: recall_at_3 value: 46.464 - type: recall_at_5 value: 54.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.949 - type: map_at_10 value: 37.230000000000004 - type: map_at_100 value: 38.644 - type: map_at_1000 value: 38.751999999999995 - type: map_at_3 value: 33.816 - type: map_at_5 value: 35.817 - type: mrr_at_1 value: 33.446999999999996 - type: mrr_at_10 value: 42.970000000000006 - type: mrr_at_100 value: 43.873 - type: mrr_at_1000 value: 43.922 - type: mrr_at_3 value: 40.467999999999996 - type: mrr_at_5 value: 41.861 - type: ndcg_at_1 value: 33.446999999999996 - type: ndcg_at_10 value: 43.403000000000006 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 51.361999999999995 - type: ndcg_at_3 value: 38.155 - type: ndcg_at_5 value: 40.643 - type: precision_at_1 value: 33.446999999999996 - type: precision_at_10 value: 8.128 - type: precision_at_100 value: 1.274 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.493000000000002 - type: precision_at_5 value: 13.333 - type: recall_at_1 value: 26.949 - type: recall_at_10 value: 56.006 - type: recall_at_100 value: 80.99199999999999 - type: recall_at_1000 value: 95.074 - type: recall_at_3 value: 40.809 - type: recall_at_5 value: 47.57 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.243583333333333 - type: map_at_10 value: 37.193250000000006 - type: map_at_100 value: 38.44833333333334 - type: map_at_1000 value: 38.56083333333333 - type: map_at_3 value: 34.06633333333333 - type: map_at_5 value: 35.87858333333334 - type: mrr_at_1 value: 32.291583333333335 - type: mrr_at_10 value: 41.482749999999996 - type: mrr_at_100 value: 42.33583333333333 - type: mrr_at_1000 value: 42.38683333333333 - type: mrr_at_3 value: 38.952999999999996 - type: mrr_at_5 value: 40.45333333333333 - type: ndcg_at_1 value: 32.291583333333335 - type: ndcg_at_10 value: 42.90533333333334 - type: ndcg_at_100 value: 48.138666666666666 - type: ndcg_at_1000 value: 50.229083333333335 - type: ndcg_at_3 value: 37.76133333333334 - type: ndcg_at_5 value: 40.31033333333334 - type: precision_at_1 value: 32.291583333333335 - type: precision_at_10 value: 7.585583333333333 - type: precision_at_100 value: 1.2045000000000001 - type: precision_at_1000 value: 0.15733333333333335 - type: precision_at_3 value: 17.485416666666666 - type: precision_at_5 value: 12.5145 - type: recall_at_1 value: 27.243583333333333 - type: recall_at_10 value: 55.45108333333334 - type: recall_at_100 value: 78.25858333333335 - type: recall_at_1000 value: 92.61716666666665 - type: recall_at_3 value: 41.130583333333334 - type: recall_at_5 value: 47.73133333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.325 - type: map_at_10 value: 32.795 - type: map_at_100 value: 33.96 - type: map_at_1000 value: 34.054 - type: map_at_3 value: 30.64 - type: map_at_5 value: 31.771 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 35.83 - type: mrr_at_100 value: 36.868 - type: mrr_at_1000 value: 36.928 - type: mrr_at_3 value: 33.896 - type: mrr_at_5 value: 34.893 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 36.746 - type: ndcg_at_100 value: 42.225 - type: ndcg_at_1000 value: 44.523 - type: ndcg_at_3 value: 32.82 - type: ndcg_at_5 value: 34.583000000000006 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 13.753000000000002 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 26.325 - type: recall_at_10 value: 45.975 - type: recall_at_100 value: 70.393 - type: recall_at_1000 value: 87.217 - type: recall_at_3 value: 35.195 - type: recall_at_5 value: 39.69 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.828 - type: map_at_10 value: 25.759 - type: map_at_100 value: 26.961000000000002 - type: map_at_1000 value: 27.094 - type: map_at_3 value: 23.166999999999998 - type: map_at_5 value: 24.610000000000003 - type: mrr_at_1 value: 21.61 - type: mrr_at_10 value: 29.605999999999998 - type: mrr_at_100 value: 30.586000000000002 - type: mrr_at_1000 value: 30.664 - type: mrr_at_3 value: 27.214 - type: mrr_at_5 value: 28.571 - type: ndcg_at_1 value: 21.61 - type: ndcg_at_10 value: 30.740000000000002 - type: ndcg_at_100 value: 36.332 - type: ndcg_at_1000 value: 39.296 - type: ndcg_at_3 value: 26.11 - type: ndcg_at_5 value: 28.297 - type: precision_at_1 value: 21.61 - type: precision_at_10 value: 5.643 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.4 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 17.828 - type: recall_at_10 value: 41.876000000000005 - type: recall_at_100 value: 66.648 - type: recall_at_1000 value: 87.763 - type: recall_at_3 value: 28.957 - type: recall_at_5 value: 34.494 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.921000000000003 - type: map_at_10 value: 37.156 - type: map_at_100 value: 38.399 - type: map_at_1000 value: 38.498 - type: map_at_3 value: 34.134 - type: map_at_5 value: 35.936 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.19 - type: mrr_at_100 value: 42.102000000000004 - type: mrr_at_1000 value: 42.157 - type: mrr_at_3 value: 38.464 - type: mrr_at_5 value: 40.148 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.679 - type: ndcg_at_100 value: 48.27 - type: ndcg_at_1000 value: 50.312 - type: ndcg_at_3 value: 37.269000000000005 - type: ndcg_at_5 value: 40.055 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.155 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 16.791 - type: precision_at_5 value: 12.015 - type: recall_at_1 value: 27.921000000000003 - type: recall_at_10 value: 55.357 - type: recall_at_100 value: 79.476 - type: recall_at_1000 value: 93.314 - type: recall_at_3 value: 40.891 - type: recall_at_5 value: 47.851 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.524 - type: map_at_10 value: 35.135 - type: map_at_100 value: 36.665 - type: map_at_1000 value: 36.886 - type: map_at_3 value: 31.367 - type: map_at_5 value: 33.724 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.616 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.585 - type: mrr_at_3 value: 36.462 - type: mrr_at_5 value: 38.507999999999996 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 41.61 - type: ndcg_at_100 value: 47.249 - type: ndcg_at_1000 value: 49.662 - type: ndcg_at_3 value: 35.421 - type: ndcg_at_5 value: 38.811 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 8.123 - type: precision_at_100 value: 1.5810000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.568999999999999 - type: recall_at_1 value: 25.524 - type: recall_at_10 value: 54.994 - type: recall_at_100 value: 80.03099999999999 - type: recall_at_1000 value: 95.25099999999999 - type: recall_at_3 value: 37.563 - type: recall_at_5 value: 46.428999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.224 - type: map_at_10 value: 30.599999999999998 - type: map_at_100 value: 31.526 - type: map_at_1000 value: 31.629 - type: map_at_3 value: 27.491 - type: map_at_5 value: 29.212 - type: mrr_at_1 value: 24.214 - type: mrr_at_10 value: 32.632 - type: mrr_at_100 value: 33.482 - type: mrr_at_1000 value: 33.550000000000004 - type: mrr_at_3 value: 29.852 - type: mrr_at_5 value: 31.451 - type: ndcg_at_1 value: 24.214 - type: ndcg_at_10 value: 35.802 - type: ndcg_at_100 value: 40.502 - type: ndcg_at_1000 value: 43.052 - type: ndcg_at_3 value: 29.847 - type: ndcg_at_5 value: 32.732 - type: precision_at_1 value: 24.214 - type: precision_at_10 value: 5.804 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.692999999999998 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.224 - type: recall_at_10 value: 49.849 - type: recall_at_100 value: 71.45 - type: recall_at_1000 value: 90.583 - type: recall_at_3 value: 34.153 - type: recall_at_5 value: 41.004000000000005 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.386999999999999 - type: map_at_10 value: 20.182 - type: map_at_100 value: 21.86 - type: map_at_1000 value: 22.054000000000002 - type: map_at_3 value: 17.165 - type: map_at_5 value: 18.643 - type: mrr_at_1 value: 26.906000000000002 - type: mrr_at_10 value: 37.907999999999994 - type: mrr_at_100 value: 38.868 - type: mrr_at_1000 value: 38.913 - type: mrr_at_3 value: 34.853 - type: mrr_at_5 value: 36.567 - type: ndcg_at_1 value: 26.906000000000002 - type: ndcg_at_10 value: 28.103 - type: ndcg_at_100 value: 35.073 - type: ndcg_at_1000 value: 38.653 - type: ndcg_at_3 value: 23.345 - type: ndcg_at_5 value: 24.828 - type: precision_at_1 value: 26.906000000000002 - type: precision_at_10 value: 8.547 - type: precision_at_100 value: 1.617 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 17.025000000000002 - type: precision_at_5 value: 12.834000000000001 - type: recall_at_1 value: 12.386999999999999 - type: recall_at_10 value: 33.306999999999995 - type: recall_at_100 value: 57.516 - type: recall_at_1000 value: 77.74799999999999 - type: recall_at_3 value: 21.433 - type: recall_at_5 value: 25.915 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.322 - type: map_at_10 value: 20.469 - type: map_at_100 value: 28.638 - type: map_at_1000 value: 30.433 - type: map_at_3 value: 14.802000000000001 - type: map_at_5 value: 17.297 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.29599999999999 - type: mrr_at_100 value: 76.62400000000001 - type: mrr_at_1000 value: 76.633 - type: mrr_at_3 value: 75.083 - type: mrr_at_5 value: 75.771 - type: ndcg_at_1 value: 54.87499999999999 - type: ndcg_at_10 value: 41.185 - type: ndcg_at_100 value: 46.400000000000006 - type: ndcg_at_1000 value: 54.223 - type: ndcg_at_3 value: 45.489000000000004 - type: ndcg_at_5 value: 43.161 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.300000000000004 - type: precision_at_100 value: 10.607999999999999 - type: precision_at_1000 value: 2.237 - type: precision_at_3 value: 49.083 - type: precision_at_5 value: 41.6 - type: recall_at_1 value: 9.322 - type: recall_at_10 value: 25.696 - type: recall_at_100 value: 52.898 - type: recall_at_1000 value: 77.281 - type: recall_at_3 value: 15.943 - type: recall_at_5 value: 19.836000000000002 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.650000000000006 - type: f1 value: 43.528467245539396 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.56 - type: map_at_10 value: 76.767 - type: map_at_100 value: 77.054 - type: map_at_1000 value: 77.068 - type: map_at_3 value: 75.29299999999999 - type: map_at_5 value: 76.24 - type: mrr_at_1 value: 71.842 - type: mrr_at_10 value: 81.459 - type: mrr_at_100 value: 81.58800000000001 - type: mrr_at_1000 value: 81.59100000000001 - type: mrr_at_3 value: 80.188 - type: mrr_at_5 value: 81.038 - type: ndcg_at_1 value: 71.842 - type: ndcg_at_10 value: 81.51899999999999 - type: ndcg_at_100 value: 82.544 - type: ndcg_at_1000 value: 82.829 - type: ndcg_at_3 value: 78.92 - type: ndcg_at_5 value: 80.406 - type: precision_at_1 value: 71.842 - type: precision_at_10 value: 10.066 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.703000000000003 - type: precision_at_5 value: 19.301 - type: recall_at_1 value: 66.56 - type: recall_at_10 value: 91.55 - type: recall_at_100 value: 95.67099999999999 - type: recall_at_1000 value: 97.539 - type: recall_at_3 value: 84.46900000000001 - type: recall_at_5 value: 88.201 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.087 - type: map_at_10 value: 32.830999999999996 - type: map_at_100 value: 34.814 - type: map_at_1000 value: 34.999 - type: map_at_3 value: 28.198 - type: map_at_5 value: 30.779 - type: mrr_at_1 value: 38.889 - type: mrr_at_10 value: 48.415 - type: mrr_at_100 value: 49.187 - type: mrr_at_1000 value: 49.226 - type: mrr_at_3 value: 45.705 - type: mrr_at_5 value: 47.225 - type: ndcg_at_1 value: 38.889 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.671 - type: ndcg_at_1000 value: 50.744 - type: ndcg_at_3 value: 36.296 - type: ndcg_at_5 value: 37.852999999999994 - type: precision_at_1 value: 38.889 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.8499999999999999 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 18.21 - type: recall_at_1 value: 20.087 - type: recall_at_10 value: 48.042 - type: recall_at_100 value: 73.493 - type: recall_at_1000 value: 91.851 - type: recall_at_3 value: 32.694 - type: recall_at_5 value: 39.099000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 56.99999999999999 - type: map_at_100 value: 57.914 - type: map_at_1000 value: 57.984 - type: map_at_3 value: 53.900999999999996 - type: map_at_5 value: 55.827000000000005 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.955 - type: mrr_at_100 value: 82.164 - type: mrr_at_1000 value: 82.173 - type: mrr_at_3 value: 80.963 - type: mrr_at_5 value: 81.574 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 65.75 - type: ndcg_at_100 value: 68.949 - type: ndcg_at_1000 value: 70.342 - type: ndcg_at_3 value: 61.29 - type: ndcg_at_5 value: 63.747 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 13.571 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.663 - type: precision_at_5 value: 25.136999999999997 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 67.853 - type: recall_at_100 value: 80.365 - type: recall_at_1000 value: 89.629 - type: recall_at_3 value: 57.995 - type: recall_at_5 value: 62.843 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.95200000000001 - type: ap value: 80.73847277002109 - type: f1 value: 85.92406135678594 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.916999999999998 - type: map_at_10 value: 33.23 - type: map_at_100 value: 34.427 - type: map_at_1000 value: 34.477000000000004 - type: map_at_3 value: 29.292 - type: map_at_5 value: 31.6 - type: mrr_at_1 value: 21.547 - type: mrr_at_10 value: 33.839999999999996 - type: mrr_at_100 value: 34.979 - type: mrr_at_1000 value: 35.022999999999996 - type: mrr_at_3 value: 29.988 - type: mrr_at_5 value: 32.259 - type: ndcg_at_1 value: 21.519 - type: ndcg_at_10 value: 40.209 - type: ndcg_at_100 value: 45.954 - type: ndcg_at_1000 value: 47.187 - type: ndcg_at_3 value: 32.227 - type: ndcg_at_5 value: 36.347 - type: precision_at_1 value: 21.519 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.877999999999998 - type: precision_at_5 value: 10.404 - type: recall_at_1 value: 20.916999999999998 - type: recall_at_10 value: 61.7 - type: recall_at_100 value: 88.202 - type: recall_at_1000 value: 97.588 - type: recall_at_3 value: 40.044999999999995 - type: recall_at_5 value: 49.964999999999996 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.02781577747379 - type: f1 value: 92.83653922768306 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.04286365709075 - type: f1 value: 53.43867658525793 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.47276395427035 - type: f1 value: 69.77017399597342 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.3819771351715 - type: f1 value: 76.8484533435409 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.16515993299593 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.77145323314774 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53637706586391 - type: mrr value: 33.7312926288863 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.063999999999999 - type: map_at_10 value: 15.046999999999999 - type: map_at_100 value: 19.116 - type: map_at_1000 value: 20.702 - type: map_at_3 value: 10.932 - type: map_at_5 value: 12.751999999999999 - type: mrr_at_1 value: 50.464 - type: mrr_at_10 value: 58.189 - type: mrr_at_100 value: 58.733999999999995 - type: mrr_at_1000 value: 58.769000000000005 - type: mrr_at_3 value: 56.24400000000001 - type: mrr_at_5 value: 57.68299999999999 - type: ndcg_at_1 value: 48.142 - type: ndcg_at_10 value: 37.897 - type: ndcg_at_100 value: 35.264 - type: ndcg_at_1000 value: 44.033 - type: ndcg_at_3 value: 42.967 - type: ndcg_at_5 value: 40.815 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 28.235 - type: precision_at_100 value: 8.994 - type: precision_at_1000 value: 2.218 - type: precision_at_3 value: 40.041 - type: precision_at_5 value: 35.046 - type: recall_at_1 value: 7.063999999999999 - type: recall_at_10 value: 18.598 - type: recall_at_100 value: 35.577999999999996 - type: recall_at_1000 value: 67.43 - type: recall_at_3 value: 11.562999999999999 - type: recall_at_5 value: 14.771 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 29.046 - type: map_at_10 value: 44.808 - type: map_at_100 value: 45.898 - type: map_at_1000 value: 45.927 - type: map_at_3 value: 40.19 - type: map_at_5 value: 42.897 - type: mrr_at_1 value: 32.706 - type: mrr_at_10 value: 47.275 - type: mrr_at_100 value: 48.075 - type: mrr_at_1000 value: 48.095 - type: mrr_at_3 value: 43.463 - type: mrr_at_5 value: 45.741 - type: ndcg_at_1 value: 32.706 - type: ndcg_at_10 value: 52.835 - type: ndcg_at_100 value: 57.345 - type: ndcg_at_1000 value: 57.985 - type: ndcg_at_3 value: 44.171 - type: ndcg_at_5 value: 48.661 - type: precision_at_1 value: 32.706 - type: precision_at_10 value: 8.895999999999999 - type: precision_at_100 value: 1.143 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.238999999999997 - type: precision_at_5 value: 14.728 - type: recall_at_1 value: 29.046 - type: recall_at_10 value: 74.831 - type: recall_at_100 value: 94.192 - type: recall_at_1000 value: 98.897 - type: recall_at_3 value: 52.37500000000001 - type: recall_at_5 value: 62.732 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.38799999999999 - type: map_at_10 value: 84.315 - type: map_at_100 value: 84.955 - type: map_at_1000 value: 84.971 - type: map_at_3 value: 81.33399999999999 - type: map_at_5 value: 83.21300000000001 - type: mrr_at_1 value: 81.03 - type: mrr_at_10 value: 87.395 - type: mrr_at_100 value: 87.488 - type: mrr_at_1000 value: 87.48899999999999 - type: mrr_at_3 value: 86.41499999999999 - type: mrr_at_5 value: 87.074 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.151 - type: ndcg_at_100 value: 89.38199999999999 - type: ndcg_at_1000 value: 89.479 - type: ndcg_at_3 value: 85.24000000000001 - type: ndcg_at_5 value: 86.856 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.372 - type: precision_at_100 value: 1.526 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.217 - type: precision_at_5 value: 24.502 - type: recall_at_1 value: 70.38799999999999 - type: recall_at_10 value: 95.452 - type: recall_at_100 value: 99.59700000000001 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.11 - type: recall_at_5 value: 91.662 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.334991029213235 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.586500854616666 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.153 - type: map_at_10 value: 14.277000000000001 - type: map_at_100 value: 16.922 - type: map_at_1000 value: 17.302999999999997 - type: map_at_3 value: 9.961 - type: map_at_5 value: 12.257 - type: mrr_at_1 value: 25.4 - type: mrr_at_10 value: 37.458000000000006 - type: mrr_at_100 value: 38.681 - type: mrr_at_1000 value: 38.722 - type: mrr_at_3 value: 34.1 - type: mrr_at_5 value: 36.17 - type: ndcg_at_1 value: 25.4 - type: ndcg_at_10 value: 23.132 - type: ndcg_at_100 value: 32.908 - type: ndcg_at_1000 value: 38.754 - type: ndcg_at_3 value: 21.82 - type: ndcg_at_5 value: 19.353 - type: precision_at_1 value: 25.4 - type: precision_at_10 value: 12.1 - type: precision_at_100 value: 2.628 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.732999999999997 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.153 - type: recall_at_10 value: 24.54 - type: recall_at_100 value: 53.293 - type: recall_at_1000 value: 81.57 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.577 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.86284404925333 - type: cos_sim_spearman value: 78.85870555294795 - type: euclidean_pearson value: 82.20105295276093 - type: euclidean_spearman value: 78.92125617009592 - type: manhattan_pearson value: 82.15840025289069 - type: manhattan_spearman value: 78.85955732900803 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.98747423389027 - type: cos_sim_spearman value: 75.71298531799367 - type: euclidean_pearson value: 81.59709559192291 - type: euclidean_spearman value: 75.40622749225653 - type: manhattan_pearson value: 81.55553547608804 - type: manhattan_spearman value: 75.39380235424899 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.76861330695503 - type: cos_sim_spearman value: 85.72991921531624 - type: euclidean_pearson value: 84.84504307397536 - type: euclidean_spearman value: 86.02679162824732 - type: manhattan_pearson value: 84.79969439220142 - type: manhattan_spearman value: 85.99238837291625 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.31929747511796 - type: cos_sim_spearman value: 81.50806522502528 - type: euclidean_pearson value: 82.93936686512777 - type: euclidean_spearman value: 81.54403447993224 - type: manhattan_pearson value: 82.89696981900828 - type: manhattan_spearman value: 81.52817825470865 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.14413295332908 - type: cos_sim_spearman value: 88.81032027008195 - type: euclidean_pearson value: 88.19205563407645 - type: euclidean_spearman value: 88.89738339479216 - type: manhattan_pearson value: 88.11075942004189 - type: manhattan_spearman value: 88.8297061675564 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.15980075557017 - type: cos_sim_spearman value: 83.81896308594801 - type: euclidean_pearson value: 83.11195254311338 - type: euclidean_spearman value: 84.10479481755407 - type: manhattan_pearson value: 83.13915225100556 - type: manhattan_spearman value: 84.09895591027859 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.93669480147919 - type: cos_sim_spearman value: 87.89861394614361 - type: euclidean_pearson value: 88.37316413202339 - type: euclidean_spearman value: 88.18033817842569 - type: manhattan_pearson value: 88.39427578879469 - type: manhattan_spearman value: 88.09185009236847 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.62215083348255 - type: cos_sim_spearman value: 67.33243665716736 - type: euclidean_pearson value: 67.60871701996284 - type: euclidean_spearman value: 66.75929225238659 - type: manhattan_pearson value: 67.63907838970992 - type: manhattan_spearman value: 66.79313656754846 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.65549191934764 - type: cos_sim_spearman value: 85.73266847750143 - type: euclidean_pearson value: 85.75609932254318 - type: euclidean_spearman value: 85.9452287759371 - type: manhattan_pearson value: 85.69717413063573 - type: manhattan_spearman value: 85.86546318377046 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.08164129085783 - type: mrr value: 96.2877273416489 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 62.09400000000001 - type: map_at_10 value: 71.712 - type: map_at_100 value: 72.128 - type: map_at_1000 value: 72.14399999999999 - type: map_at_3 value: 68.93 - type: map_at_5 value: 70.694 - type: mrr_at_1 value: 65.0 - type: mrr_at_10 value: 72.572 - type: mrr_at_100 value: 72.842 - type: mrr_at_1000 value: 72.856 - type: mrr_at_3 value: 70.44399999999999 - type: mrr_at_5 value: 71.744 - type: ndcg_at_1 value: 65.0 - type: ndcg_at_10 value: 76.178 - type: ndcg_at_100 value: 77.887 - type: ndcg_at_1000 value: 78.227 - type: ndcg_at_3 value: 71.367 - type: ndcg_at_5 value: 73.938 - type: precision_at_1 value: 65.0 - type: precision_at_10 value: 10.033 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 18.4 - type: recall_at_1 value: 62.09400000000001 - type: recall_at_10 value: 89.022 - type: recall_at_100 value: 96.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 75.922 - type: recall_at_5 value: 82.428 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82178217821782 - type: cos_sim_ap value: 95.71282508220798 - type: cos_sim_f1 value: 90.73120494335737 - type: cos_sim_precision value: 93.52441613588111 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 92.98534606529098 - type: dot_f1 value: 86.83024536805209 - type: dot_precision value: 86.96088264794383 - type: dot_recall value: 86.7 - type: euclidean_accuracy value: 99.82475247524752 - type: euclidean_ap value: 95.72927039014849 - type: euclidean_f1 value: 90.89974293059126 - type: euclidean_precision value: 93.54497354497354 - type: euclidean_recall value: 88.4 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.72142177390405 - type: manhattan_f1 value: 91.00152516522625 - type: manhattan_precision value: 92.55429162357808 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.82574257425742 - type: max_ap value: 95.72927039014849 - type: max_f1 value: 91.00152516522625 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.63957663468679 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.003307257923964 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.005825525863905 - type: mrr value: 53.854683919022165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.503611569974098 - type: cos_sim_spearman value: 31.17155564248449 - type: dot_pearson value: 26.740428413981306 - type: dot_spearman value: 26.55727635469746 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.7670000000000001 - type: map_at_100 value: 10.208 - type: map_at_1000 value: 25.997999999999998 - type: map_at_3 value: 0.605 - type: map_at_5 value: 0.9560000000000001 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 90.167 - type: mrr_at_100 value: 90.167 - type: mrr_at_1000 value: 90.167 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 90.167 - type: ndcg_at_1 value: 77.0 - type: ndcg_at_10 value: 68.783 - type: ndcg_at_100 value: 54.196 - type: ndcg_at_1000 value: 52.077 - type: ndcg_at_3 value: 71.642 - type: ndcg_at_5 value: 70.45700000000001 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 23.102 - type: precision_at_3 value: 76.0 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 1.9869999999999999 - type: recall_at_100 value: 13.749 - type: recall_at_1000 value: 50.157 - type: recall_at_3 value: 0.633 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.437 - type: map_at_10 value: 8.791 - type: map_at_100 value: 15.001999999999999 - type: map_at_1000 value: 16.549 - type: map_at_3 value: 3.8080000000000003 - type: map_at_5 value: 5.632000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 36.96 - type: mrr_at_100 value: 37.912 - type: mrr_at_1000 value: 37.912 - type: mrr_at_3 value: 29.592000000000002 - type: mrr_at_5 value: 34.489999999999995 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 22.554 - type: ndcg_at_100 value: 35.197 - type: ndcg_at_1000 value: 46.58 - type: ndcg_at_3 value: 20.285 - type: ndcg_at_5 value: 21.924 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 21.837 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.437 - type: recall_at_10 value: 16.314999999999998 - type: recall_at_100 value: 47.635 - type: recall_at_1000 value: 82.963 - type: recall_at_3 value: 4.955 - type: recall_at_5 value: 8.805 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.6128 - type: ap value: 14.279639861175664 - type: f1 value: 54.922292491204274 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.01188455008489 - type: f1 value: 57.377953019225515 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.306769136544254 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.64701674912082 - type: cos_sim_ap value: 72.46600945328552 - type: cos_sim_f1 value: 67.96572367648784 - type: cos_sim_precision value: 61.21801649397336 - 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type: max_f1 value: 78.07565728654365 language: - en license: mit --- # gte-base General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base") model = AutoModel.from_pretrained("thenlper/gte-base") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-base') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @misc{li2023general, title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang}, year={2023}, eprint={2308.03281}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

circulus/Llama-2-13b-llava-v1

circulus

transformers

text-generation

--- license: mit datasets: - Open-Orca/OpenOrca language: - en library_name: transformers pipeline_tag: text-generation ---  ``` model_name = "circulus/Llama-2-13b-llava-v1" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", quantization_config=config) ```

Riiid/sheep-duck-llama-2

Riiid

transformers

text-generation

--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/62fb1ef7e8c9c532aa7d19e4/NswB5XPkkOljeRh1xbMmR.png pipeline_tag: text-generation license: llama2 language: - en library_name: transformers tags: - Riiid - llama-2 --- # sheep-duck-llama-2 <img src = "https://cdn-uploads.huggingface.co/production/uploads/62fb1ef7e8c9c532aa7d19e4/NswB5XPkkOljeRh1xbMmR.png" width="30%" height="30%"> This is a finetuned model from llama-2-70b. ## Model Details * **Developed by**: [Riiid](https://riiid.com/) * **Backbone Model**: [LLaMA-2](https://github.com/facebookresearch/llama/tree/main) * **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) ## Dataset Details ### Used Datasets - Orca-style dataset - Alpaca-style dataset ### Prompt Template ``` ### System: {System} ### User: {User} ### Assistant: {Assistant} ``` ## Evaluation | Metric | Value | |-----------------------|-------| | ARC (25-shot) | 72.44 | | HellaSwag (10-shot) | 87.79 | | MMLU (5-shot) | 70.74 | | TruthfulQA (0-shot) | 63.71 | | Avg. | 73.67 | ## Limitations & Biases: Llama2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ ## License Disclaimer: This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind. ## Contact Us - [Riiid](https://riiid.com/) ## Citation: Please kindly cite using the following BibTeX: ```bibtex @article{platypus2023, title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, booktitle={arXiv preprint arxiv:2308.07317}, year={2023} } ``` ``` @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @misc{Orca-best, title = {Orca-best: A filtered version of orca gpt4 dataset.}, author = {Shahul Es}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/shahules786/orca-best/}, } ``` ``` @software{touvron2023llama2, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom}, year={2023} } ```

d4data/bias-detection-model

d4data

transformers

text-classification

--- language: - en tags: - Text Classification co2_eq_emissions: 0.319355 widget: - text: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property." example_title: "Biased example 1" - text: "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video." example_title: "Biased example 2" - text: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion." example_title: "Biased example 3" - text: "There have been a protest by a group of people" example_title: "Non-Biased example 1" - text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology." example_title: "Non-Biased example 2" --- ## About the Model An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512. - Dataset : MBAD Data - Carbon emission 0.319355 Kg | Train Accuracy | Validation Accuracy | Train loss | Test loss | |---------------:| -------------------:| ----------:|----------:| | 76.97 | 62.00 | 0.45 | 0.96 | ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import AutoTokenizer, TFAutoModelForSequenceClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model") model = TFAutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model") classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.") ``` ## Author This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at: > Bias & Fairness in AI, (2022), GitHub repository, <https://github.com/dreji18/Fairness-in-AI>

microsoft/trocr-small-handwritten

microsoft

transformers

image-to-text

--- tags: - trocr - image-to-text widget: - src: https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg example_title: Note 1 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU example_title: Note 2 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU example_title: Note 3 --- # TrOCR (small-sized model, fine-tuned on IAM) TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of DeiT, while the text decoder was initialized from the weights of UniLM. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IAM database url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten') pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ramsrigouthamg/t5_squad_v1

ramsrigouthamg

transformers

text2text-generation

Entry not found

hellomyoh/llama2-2b-s117755-v2

hellomyoh

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0

openlm-research/open_llama_3b_v2

openlm-research

transformers

text-generation

--- license: apache-2.0 datasets: - tiiuae/falcon-refinedweb - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T --- # OpenLLaMA: An Open Reproduction of LLaMA **TL;DR**: we are releasing our public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. Our model weights can serve as the drop in replacement of LLaMA in existing implementations. In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a series of 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The v2 model is better than the old v1 model trained on a different data mixture. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details. ## Weights Release, License and Usage We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license. ### Loading the Weights with Hugging Face Transformers Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that** [**the auto-converted fast tokenizer sometimes gives incorrect tokenizations**](https://github.com/huggingface/transformers/issues/24233)**.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage. ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM ## v2 models model_path = 'openlm-research/open_llama_3b_v2' # model_path = 'openlm-research/open_llama_7b_v2' ## v1 models # model_path = 'openlm-research/open_llama_3b' # model_path = 'openlm-research/open_llama_7b' # model_path = 'openlm-research/open_llama_13b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = 'Q: What is the largest animal?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). ### Evaluating with LM-Eval-Harness The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below: ```python tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained( pretrained if tokenizer is None else tokenizer, revision=revision + ("/" + subfolder if subfolder is not None else ""), use_fast=False ) ``` ### Loading the Weights with EasyLM For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. ## Dataset and Training The v1 models are trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). The v2 models are trained on a mixture of the [Falcon refined-web dataset](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), the [StarCoder dataset](https://huggingface.co/datasets/bigcode/starcoderdata) and the wikipedia, arxiv, book and stackexchange part of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs open datasets rather than the one utilized by the original LLaMA. We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and fully sharded data parallelism [](https://engineering.fb.com/2021/07/15/open-source/fsdp/)(also know as ZeRO stage 3) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model. ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. | **Task/Metric** | GPT-J 6B | LLaMA 7B | LLaMA 13B | OpenLLaMA 3Bv2 | OpenLLaMA 7Bv2 | OpenLLaMA 3B | OpenLLaMA 7B | OpenLLaMA 13B | | ---------------------- | -------- | -------- | --------- | -------------- | -------------- | ------------ | ------------ | ------------- | | anli_r1/acc | 0.32 | 0.35 | 0.35 | 0.33 | 0.34 | 0.33 | 0.33 | 0.33 | | anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.36 | 0.35 | 0.32 | 0.36 | 0.33 | | anli_r3/acc | 0.35 | 0.37 | 0.39 | 0.38 | 0.39 | 0.35 | 0.38 | 0.40 | | arc_challenge/acc | 0.34 | 0.39 | 0.44 | 0.34 | 0.39 | 0.34 | 0.37 | 0.41 | | arc_challenge/acc_norm | 0.37 | 0.41 | 0.44 | 0.36 | 0.41 | 0.37 | 0.38 | 0.44 | | arc_easy/acc | 0.67 | 0.68 | 0.75 | 0.68 | 0.73 | 0.69 | 0.72 | 0.75 | | arc_easy/acc_norm | 0.62 | 0.52 | 0.59 | 0.63 | 0.70 | 0.65 | 0.68 | 0.70 | | boolq/acc | 0.66 | 0.75 | 0.71 | 0.66 | 0.72 | 0.68 | 0.71 | 0.75 | | hellaswag/acc | 0.50 | 0.56 | 0.59 | 0.52 | 0.56 | 0.49 | 0.53 | 0.56 | | hellaswag/acc_norm | 0.66 | 0.73 | 0.76 | 0.70 | 0.75 | 0.67 | 0.72 | 0.76 | | openbookqa/acc | 0.29 | 0.29 | 0.31 | 0.26 | 0.30 | 0.27 | 0.30 | 0.31 | | openbookqa/acc_norm | 0.38 | 0.41 | 0.42 | 0.38 | 0.41 | 0.40 | 0.40 | 0.43 | | piqa/acc | 0.75 | 0.78 | 0.79 | 0.77 | 0.79 | 0.75 | 0.76 | 0.77 | | piqa/acc_norm | 0.76 | 0.78 | 0.79 | 0.78 | 0.80 | 0.76 | 0.77 | 0.79 | | record/em | 0.88 | 0.91 | 0.92 | 0.87 | 0.89 | 0.88 | 0.89 | 0.91 | | record/f1 | 0.89 | 0.91 | 0.92 | 0.88 | 0.89 | 0.89 | 0.90 | 0.91 | | rte/acc | 0.54 | 0.56 | 0.69 | 0.55 | 0.57 | 0.58 | 0.60 | 0.64 | | truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.25 | 0.22 | 0.23 | 0.22 | 0.23 | 0.25 | | truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.40 | 0.35 | 0.35 | 0.35 | 0.35 | 0.38 | | wic/acc | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.48 | 0.51 | 0.47 | | winogrande/acc | 0.64 | 0.68 | 0.70 | 0.63 | 0.66 | 0.62 | 0.67 | 0.70 | | Average | 0.52 | 0.55 | 0.57 | 0.53 | 0.56 | 0.53 | 0.55 | 0.57 | We removed the task CB and WSC from our benchmark, as our model performs suspiciously high on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Contact We would love to get feedback from the community. If you have any questions, please open an issue or contact us. OpenLLaMA is developed by: [Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research. *Equal Contribution ## Acknowledgment We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback. The OpenLLaMA 13B v1 model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support. ## Reference If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX: ``` @software{openlm2023openllama, author = {Geng, Xinyang and Liu, Hao}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ``` @article{touvron2023llama, title={Llama: Open and efficient foundation language models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```

TinyPixel/Llama-2-7B-bf16-sharded

TinyPixel

transformers

text-generation

Entry not found

dbmdz/bert-base-german-uncased

dbmdz

transformers

fill-mask

--- language: de license: mit --- # 🤗 + 📚 dbmdz German BERT models In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources another German BERT models 🎉 # German BERT ## Stats In addition to the recently released [German BERT](https://deepset.ai/german-bert) model by [deepset](https://deepset.ai/) we provide another German-language model. The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with a size of 16GB and 2,350,234,427 tokens. For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps (sentence piece model for vocab generation) follow those used for training [SciBERT](https://github.com/allenai/scibert). The model is trained with an initial sequence length of 512 subwords and was performed for 1.5M steps. This release includes both cased and uncased models. ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | -------------------------------- | --------------------------------------------------------------------------------------------------------------- | `bert-base-german-dbmdz-cased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt) | `bert-base-german-dbmdz-uncased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt) ## Usage With Transformers >= 2.3 our German BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased") ``` ## Results For results on downstream tasks like NER or PoS tagging, please refer to [this repository](https://github.com/stefan-it/fine-tuned-berts-seq). # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗

igorvln/dare_gpt2_ddi_byrelation_finetuning

igorvln

transformers

text-generation

Entry not found

dreamlike-art/dreamlike-diffusion-1.0

dreamlike-art

diffusers

text-to-image

--- language: - en license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - art - artistic - diffusers inference: false --- # Dreamlike Diffusion 1.0 is SD 1.5 fine tuned on high quality art, made by [dreamlike.art](https://dreamlike.art/). # If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Use the same prompts as you would for SD 1.5. Add **dreamlikeart** if the artstyle is too weak. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a 2:3 or a 9:16 aspect ratio. If you want a landscape photo, try using a 3:2 or a 16:9 aspect ratio. Use slightly higher resolution for better results: 640x640px, 512x768px, 768x512px, etc. # We've just released Dreamlike Photoreal 2.0, check it out! [https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0) <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 400px;" width="100%"/> ### Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/preview.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/2.jpg" style="max-width: 800px;" width="100%"/> ### dreamlike.art You can use this model for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> ### Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0: [](https://huggingface.co/spaces/akhaliq/dreamlike-diffusion-1.0) ### CompVis [Download dreamlike-diffusion-1.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/dreamlike-diffusion-1.0.ckpt) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlike-art/dreamlike-diffusion-1.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "dreamlikeart, a grungy woman with rainbow hair, travelling between dimensions, dynamic pose, happy, soft eyes and narrow chin, extreme bokeh, dainty figure, long hair straight down, torn kawaii shirt and baggy jeans, In style of by Jordan Grimmer and greg rutkowski, crisp lines and color, complex background, particles, lines, wind, concept art, sharp focus, vivid colors" image = pipe(prompt).images[0] image.save("./result.jpg") ``` # License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You can't host or use the model or its derivatives on websites/apps/etc., from which you earn, will earn, or plan to earn revenue or donations. If you want to, please email us at contact@dreamlike.art** - **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)** - **You are free to host the model or its derivatives on completely non-commercial websites/apps/etc (Meaning you are not getting ANY revenue or donations). Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)** - **You are free to use the outputs of the model or the outputs of the model's derivatives for commercial purposes in teams of 10 or less** - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/blob/main/LICENSE.md

ai-forever/ruclip-vit-base-patch32-384

ai-forever

transformers

# ruclip-vit-base-patch32-384 **RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and multimodal learning. Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams. * Task: `text ranking`; `image ranking`; `zero-shot image classification`; * Type: `encoder` * Num Parameters: `150M` * Training Data Volume: `240 million text-image pairs` * Language: `Russian` * Context Length: `77` * Transformer Layers: `12` * Transformer Width: `512` * Transformer Heads: `8` * Image Size: `384` * Vision Layers: `12` * Vision Width: `768` * Vision Patch Size: `32` ## Usage [Github](https://github.com/sberbank-ai/ru-clip) ``` pip install ruclip ``` ```python clip, processor = ruclip.load("ruclip-vit-base-patch32-384", device="cuda") ``` ## Performance We have evaluated the performance on the following datasets: | Dataset | Metric Name | Metric Result | |:--------------|:---------------|:----------------------------| | Food101 | acc | 0.642 | | CIFAR10 | acc | 0.862 | | CIFAR100 | acc | 0.529 | | Birdsnap | acc | 0.161 | | SUN397 | acc | 0.510 | | Stanford Cars | acc | 0.572 | | DTD | acc | 0.390 | | MNIST | acc | 0.404 | | STL10 | acc | 0.946 | | PCam | acc | 0.506 | | CLEVR | acc | 0.188 | | Rendered SST2 | acc | 0.508 | | ImageNet | acc | 0.451 | | FGVC Aircraft | mean-per-class | 0.053 | | Oxford Pets | mean-per-class | 0.587 | | Caltech101 | mean-per-class | 0.834 | | Flowers102 | mean-per-class | 0.449 | | HatefulMemes | roc-auc | 0.537 | # Authors + Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov) + Daniil Chesakov: [Github](https://github.com/Danyache) + Denis Dimitrov: [Github](https://github.com/denndimitrov) + Igor Pavlov: [Github](https://github.com/boomb0om)

apanc/russian-sensitive-topics

apanc

transformers

text-classification

--- language: - ru tags: - toxic comments classification licenses: - cc-by-nc-sa --- ## General concept of the model This model is trained on the dataset of sensitive topics of the Russian language. The concept of sensitive topics is described [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/sensitive_topics/sensitive_topics.csv) or on [kaggle](https://www.kaggle.com/nigula/russian-sensitive-topics). The properties of the dataset is the same as the one described in the article, the only difference is the size. ## Instructions The model predicts combinations of 18 sensitive topics described in the [article](https://arxiv.org/abs/2103.05345). You can find step-by-step instructions for using the model [here](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/sensitive_topics/Inference.ipynb) ## Metrics The dataset partially manually labeled samples and partially semi-automatically labeled samples. Learn more in our article. We tested the performance of the classifier only on the part of manually labeled data that is why some topics are not well represented in the test set. | | precision | recall | f1-score | support | |-------------------|-----------|--------|----------|---------| | offline_crime | 0.65 | 0.55 | 0.6 | 132 | | online_crime | 0.5 | 0.46 | 0.48 | 37 | | drugs | 0.87 | 0.9 | 0.88 | 87 | | gambling | 0.5 | 0.67 | 0.57 | 6 | | pornography | 0.73 | 0.59 | 0.65 | 204 | | prostitution | 0.75 | 0.69 | 0.72 | 91 | | slavery | 0.72 | 0.72 | 0.73 | 40 | | suicide | 0.33 | 0.29 | 0.31 | 7 | | terrorism | 0.68 | 0.57 | 0.62 | 47 | | weapons | 0.89 | 0.83 | 0.86 | 138 | | body_shaming | 0.9 | 0.67 | 0.77 | 109 | | health_shaming | 0.84 | 0.55 | 0.66 | 108 | | politics | 0.68 | 0.54 | 0.6 | 241 | | racism | 0.81 | 0.59 | 0.68 | 204 | | religion | 0.94 | 0.72 | 0.81 | 102 | | sexual_minorities | 0.69 | 0.46 | 0.55 | 102 | | sexism | 0.66 | 0.64 | 0.65 | 132 | | social_injustice | 0.56 | 0.37 | 0.45 | 181 | | none | 0.62 | 0.67 | 0.64 | 250 | | micro avg | 0.72 | 0.61 | 0.66 | 2218 | | macro avg | 0.7 | 0.6 | 0.64 | 2218 | | weighted avg | 0.73 | 0.61 | 0.66 | 2218 | | samples avg | 0.75 | 0.66 | 0.68 | 2218 | ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png ## Citation If you find this repository helpful, feel free to cite our publication: ``` @inproceedings{babakov-etal-2021-detecting, title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation", author = "Babakov, Nikolay and Logacheva, Varvara and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4", pages = "26--36", abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.", } ```

CouchCat/ma_sa_v7_distil

CouchCat

transformers

text-classification

--- language: en license: mit tags: - sentiment-analysis widget: - text: "I am disappointed in the terrible quality of my dress" --- ### Description A Sentiment Analysis model trained on customer feedback data using DistilBert. Possible sentiments are: * negative * neutral * positive ### Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil") ```

ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition

ehcalabres

transformers

audio-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model_index: name: wav2vec2-lg-xlsr-en-speech-emotion-recognition --- # Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0 The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task. The dataset used to fine-tune the original pre-trained model is the [RAVDESS dataset](https://zenodo.org/record/1188976#.YO6yI-gzaUk). This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: ```python emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised'] ``` It achieves the following results on the evaluation set: - Loss: 0.5023 - Accuracy: 0.8223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0752 | 0.21 | 30 | 2.0505 | 0.1359 | | 2.0119 | 0.42 | 60 | 1.9340 | 0.2474 | | 1.8073 | 0.63 | 90 | 1.5169 | 0.3902 | | 1.5418 | 0.84 | 120 | 1.2373 | 0.5610 | | 1.1432 | 1.05 | 150 | 1.1579 | 0.5610 | | 0.9645 | 1.26 | 180 | 0.9610 | 0.6167 | | 0.8811 | 1.47 | 210 | 0.8063 | 0.7178 | | 0.8756 | 1.68 | 240 | 0.7379 | 0.7352 | | 0.8208 | 1.89 | 270 | 0.6839 | 0.7596 | | 0.7118 | 2.1 | 300 | 0.6664 | 0.7735 | | 0.4261 | 2.31 | 330 | 0.6058 | 0.8014 | | 0.4394 | 2.52 | 360 | 0.5754 | 0.8223 | | 0.4581 | 2.72 | 390 | 0.4719 | 0.8467 | | 0.3967 | 2.93 | 420 | 0.5023 | 0.8223 | ## Contact Any doubt, contact me on [Twitter](https://twitter.com/ehcalabres) (GitHub repo soon). ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3

facebook/roberta-hate-speech-dynabench-r4-target

facebook

transformers

text-classification

--- language: en --- # LFTW R4 Target The R4 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!

flair/pos-english

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - ontonotes inference: false --- ## English Part-of-Speech Tagging in Flair (default model) This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **98,19** (Ontonotes) Predicts fine-grained POS tags: | **tag** | **meaning** | |---------------------------------|-----------| |ADD | Email | |AFX | Affix | |CC | Coordinating conjunction | |CD | Cardinal number | |DT | Determiner | |EX | Existential there | |FW | Foreign word | |HYPH | Hyphen | |IN | Preposition or subordinating conjunction | |JJ | Adjective | |JJR |Adjective, comparative | |JJS | Adjective, superlative | |LS | List item marker | |MD | Modal | |NFP | Superfluous punctuation | |NN | Noun, singular or mass | |NNP |Proper noun, singular | |NNPS | Proper noun, plural | |NNS |Noun, plural | |PDT | Predeterminer | |POS | Possessive ending | |PRP | Personal pronoun | |PRP$ | Possessive pronoun | |RB | Adverb | |RBR | Adverb, comparative | |RBS | Adverb, superlative | |RP | Particle | |SYM | Symbol | |TO | to | |UH | Interjection | |VB | Verb, base form | |VBD | Verb, past tense | |VBG | Verb, gerund or present participle | |VBN | Verb, past participle | |VBP | Verb, non-3rd person singular present | |VBZ | Verb, 3rd person singular present | |WDT | Wh-determiner | |WP | Wh-pronoun | |WP$ | Possessive wh-pronoun | |WRB | Wh-adverb | |XX | Unknown | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/pos-english") # make example sentence sentence = Sentence("I love Berlin.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('pos'): print(entity) ``` This yields the following output: ``` Span [1]: "I" [− Labels: PRP (1.0)] Span [2]: "love" [− Labels: VBP (1.0)] Span [3]: "Berlin" [− Labels: NNP (0.9999)] Span [4]: "." [− Labels: . (1.0)] ``` So, the word "*I*" is labeled as a **pronoun** (PRP), "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*I love Berlin*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import ColumnCorpus from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself) corpus: Corpus = ColumnCorpus( "resources/tasks/onto-ner", column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"}, tag_to_bioes="ner", ) # 2. what tag do we want to predict? tag_type = 'pos' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/pos-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

pszemraj/led-base-book-summary

pszemraj

transformers

summarization

--- license: - apache-2.0 - bsd-3-clause tags: - summarization - led - summary - longformer - booksum - long-document - long-form datasets: - kmfoda/booksum metrics: - rouge widget: - text: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock. example_title: earthquakes - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).' example_title: scientific paper - text: ' the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics.' example_title: data science textbook - text: 'Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer-based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT-like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT-like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.' example_title: bigbird blog intro - text: 'The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.' example_title: BookSum Abstract inference: parameters: max_length: 96 min_length: 8 no_repeat_ngram_size: 3 early_stopping: true repetition_penalty: 3.5 length_penalty: 0.3 encoder_no_repeat_ngram_size: 3 num_beams: 4 model-index: - name: pszemraj/led-base-book-summary results: - task: type: summarization name: Summarization dataset: name: kmfoda/booksum type: kmfoda/booksum config: kmfoda--booksum split: test metrics: - type: rouge value: 33.4536 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmEzYjNkZTUxZjA0YTdmNTJkMjVkMTg2NDRjNTkzN2ZlNDlhNTBhMWQ5MTNiYWE4Mzg5YTMyMTM5YmZjNDI3OSIsInZlcnNpb24iOjF9.OWjM_HCQLQHK4AV4em70QGT3lrVk25WyZdcXA8ywest_XSx9KehJbsIMDKtXxOOMwxvkogKnScy4tbskYMQqDg - type: rouge value: 5.2232 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVhOTdjZjc5YTdhMmVjZGE1NTA5MmJkYmM3Y2U3OGVlMjZmOGVlMTUzYTdiZGRhM2NmZjAzMjFkZjlkMzJmOCIsInZlcnNpb24iOjF9.qOlwWEe8dfBunmwImhbkcxzUW3ml-ESsuxjWN1fjn_o36zaUlDqlrXovMcL9GX9mVdvZDhx9W82rAR8h6410AQ - 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Ideal for summarizing long narratives, articles, papers, textbooks, and other documents. - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output. - High capacity: Handles up to 16,384 tokens per batch. - demos: try it out in the notebook linked above or in the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text) > **Note:** The API widget has a max length of ~96 tokens due to inference timeout constraints. ## Training Details The model was trained on the BookSum dataset released by SalesForce, which leads to the `bsd-3-clause` license. The training process involved 16 epochs with parameters tweaked to facilitate very fine-tuning-type training (super low learning rate). Model checkpoint: [`pszemraj/led-base-16384-finetuned-booksum`](https://huggingface.co/pszemraj/led-base-16384-finetuned-booksum). ## Other Related Checkpoints This model is the smallest/fastest booksum-tuned model I have worked on. If you're looking for higher quality summaries, check out: - [Long-T5-tglobal-base](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) - [BigBird-Pegasus-Large-K](https://huggingface.co/pszemraj/bigbird-pegasus-large-K-booksum) - [Pegasus-X-Large](https://huggingface.co/pszemraj/pegasus-x-large-book-summary) - [Long-T5-tglobal-XL](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary) There are also other variants on other datasets etc on my hf profile, feel free to try them out :) --- ## Basic Usage I recommend using `encoder_no_repeat_ngram_size=3` when calling the pipeline object, as it enhances the summary quality by encouraging the use of new vocabulary and crafting an abstractive summary. Create the pipeline object: ```python import torch from transformers import pipeline hf_name = "pszemraj/led-base-book-summary" summarizer = pipeline( "summarization", hf_name, device=0 if torch.cuda.is_available() else -1, ) ``` Feed the text into the pipeline object: ```python wall_of_text = "your words here" result = summarizer( wall_of_text, min_length=8, max_length=256, no_repeat_ngram_size=3, encoder_no_repeat_ngram_size=3, repetition_penalty=3.5, num_beams=4, do_sample=False, early_stopping=True, ) print(result[0]["generated_text"]) ``` ## Simplified Usage with TextSum To streamline the process of using this and other models, I've developed [a Python package utility](https://github.com/pszemraj/textsum) named `textsum`. This package offers simple interfaces for applying summarization models to text documents of arbitrary length. Install TextSum: ```bash pip install textsum ``` Then use it in Python with this model: ```python from textsum.summarize import Summarizer model_name = "pszemraj/led-base-book-summary" summarizer = Summarizer( model_name_or_path=model_name, # you can use any Seq2Seq model on the Hub token_batch_length=4096, # how many tokens to batch summarize at a time ) long_string = "This is a long string of text that will be summarized." out_str = summarizer.summarize_string(long_string) print(f"summary: {out_str}") ``` Currently implemented interfaces include a Python API, a Command-Line Interface (CLI), and a shareable demo/web UI. For detailed explanations and documentation, check the [README](https://github.com/pszemraj/textsum) or the [wiki](https://github.com/pszemraj/textsum/wiki) ---

baichuan-inc/Baichuan2-7B-Base

baichuan-inc

transformers

text-generation

--- language: - en - zh license: other tasks: - text-generation --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> Baichuan 2 </h1> </div> <div align="center"> <a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> | <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">💬WeChat</a> </div> <div align="center"> 🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉 </div> # 目录/Table of Contents - [📖 模型介绍/Introduction](#Introduction) - [⚙️ 快速开始/Quick Start](#Start) - [📊 Benchmark评估/Benchmark Evaluation](#Benchmark) - [📜 声明与协议/Terms and Conditions](#Terms) # <span id="Introduction">模型介绍/Introduction</span> Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿** Tokens 的高质量语料训练，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表： Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/). It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:opensource@baichuan-inc.com). The specific release versions and download links are listed in the table below: | | Base Model | Chat Model | 4bits Quantized Chat Model | |:---:|:--------------------:|:--------------------:|:--------------------------:| | 7B | [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) | [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) | [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits) | | 13B | [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) | # <span id="Start">快速开始/Quick Start</span> 在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。 In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Base", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Base", device_map="auto", trust_remote_code=True) inputs = tokenizer('登鹳雀楼->王之涣\n夜雨寄北->', return_tensors='pt') inputs = inputs.to('cuda:0') pred = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` # <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span> 我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。 We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md). ### 7B Model Results | | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | |:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| | | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | | **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | | **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | | **LLaMA-7B** | 27.10 | 35.10 | 26.75 | 27.81 | 28.17 | 32.38 | | **LLaMA2-7B** | 28.90 | 45.73 | 31.38 | 25.97 | 26.53 | 39.16 | | **MPT-7B** | 27.15 | 27.93 | 26.00 | 26.54 | 24.83 | 35.20 | | **Falcon-7B** | 24.23 | 26.03 | 25.66 | 24.24 | 24.10 | 28.77 | | **ChatGLM2-6B** | 50.20 | 45.90 | 49.00 | 49.44 | 45.28 | 31.65 | | **[Baichuan-7B]** | 42.80 | 42.30 | 44.02 | 36.34 | 34.44 | 32.48 | | **[Baichuan2-7B-Base]** | 54.00 | 54.16 | 57.07 | 47.47 | 42.73 | 41.56 | ### 13B Model Results | | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | |:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| | | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | | **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | | **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | | **LLaMA-13B** | 28.50 | 46.30 | 31.15 | 28.23 | 28.22 | 37.89 | | **LLaMA2-13B** | 35.80 | 55.09 | 37.99 | 30.83 | 32.29 | 46.98 | | **Vicuna-13B** | 32.80 | 52.00 | 36.28 | 30.11 | 31.55 | 43.04 | | **Chinese-Alpaca-Plus-13B** | 38.80 | 43.90 | 33.43 | 34.78 | 35.46 | 28.94 | | **XVERSE-13B** | 53.70 | 55.21 | 58.44 | 44.69 | 42.54 | 38.06 | | **[Baichuan-13B-Base]** | 52.40 | 51.60 | 55.30 | 49.69 | 43.20 | 43.01 | | **[Baichuan2-13B-Base]** | 58.10 | 59.17 | 61.97 | 54.33 | 48.17 | 48.78 | ## 训练过程模型/Training Dynamics 除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用 （[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化： In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU.  # <span id="Terms">声明与协议/Terms and Conditions</span> ## 声明 我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用 Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。 我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility. ## 协议 社区使用 Baichuan 2 模型需要遵循 [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) 和[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)。Baichuan 2 模型支持商业用途，如果您计划将 Baichuan 2 模型或其衍生品用于商业目的，请您确认您的主体符合以下情况： 1. 您或您的关联方的服务或产品的日均用户活跃量（DAU）低于100万。 2. 您或您的关联方不是软件服务提供商、云服务提供商。 3. 您或您的关联方不存在将授予您的商用许可，未经百川许可二次授权给其他第三方的可能。 在符合以上条件的前提下，您需要通过以下联系邮箱 opensource@baichuan-inc.com ，提交《Baichuan 2 模型社区许可协议》要求的申请材料。审核通过后，百川将特此授予您一个非排他性、全球性、不可转让、不可再许可、可撤销的商用版权许可。 The community usage of Baichuan 2 model requires adherence to [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) and [Community License for Baichuan2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). The Baichuan 2 model supports commercial use. If you plan to use the Baichuan 2 model or its derivatives for commercial purposes, please ensure that your entity meets the following conditions: 1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million. 2. Neither you nor your affiliates are software service providers or cloud service providers. 3. There is no possibility for you or your affiliates to grant the commercial license given to you, to reauthorize it to other third parties without Baichuan's permission. Upon meeting the above conditions, you need to submit the application materials required by the Baichuan 2 Model Community License Agreement via the following contact email: opensource@baichuan-inc.com. Once approved, Baichuan will hereby grant you a non-exclusive, global, non-transferable, non-sublicensable, revocable commercial copyright license. [GitHub]:https://github.com/baichuan-inc/Baichuan2 [Baichuan2]:https://github.com/baichuan-inc/Baichuan2 [Baichuan-7B]:https://huggingface.co/baichuan-inc/Baichuan-7B [Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base [Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat [Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits [Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base [Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base [Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat [Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits [通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F [法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91 [《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf [邮件申请]: mailto:opensource@baichuan-inc.com [Email]: mailto:opensource@baichuan-inc.com [opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com [训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints [百川智能]: https://www.baichuan-ai.com

NousResearch/Nous-Hermes-Llama2-13b

NousResearch

transformers

text-generation

--- language: - en tags: - llama-2 - self-instruct - distillation - synthetic instruction license: - mit --- # Model Card: Nous-Hermes-Llama2-13b Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI. ## Model Description Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable. This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine. ## Example Outputs:     ## Model Training The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style. This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below ## Collaborators The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. Special mention goes to @winglian for assisting in some of the training issues. Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. Among the contributors of datasets: - GPTeacher was made available by Teknium - Wizard LM by nlpxucan - Nous Research Instruct Dataset was provided by Karan4D and HueminArt. - GPT4-LLM and Unnatural Instructions were provided by Microsoft - Airoboros dataset by jondurbin - Camel-AI's domain expert datasets are from Camel-AI - CodeAlpaca dataset by Sahil 2801. If anyone was left out, please open a thread in the community tab. ## Prompt Format The model follows the Alpaca prompt format: ``` ### Instruction: <prompt> ### Response: <leave a newline blank for model to respond> ``` or ``` ### Instruction: <prompt> ### Input: <additional context> ### Response: <leave a newline blank for model to respond> ``` ## Benchmark Results AGI-Eval ``` | Task |Version| Metric |Value | |Stderr| |agieval_aqua_rat | 0|acc |0.2362|± |0.0267| | | |acc_norm|0.2480|± |0.0272| |agieval_logiqa_en | 0|acc |0.3425|± |0.0186| | | |acc_norm|0.3472|± |0.0187| |agieval_lsat_ar | 0|acc |0.2522|± |0.0287| | | |acc_norm|0.2087|± |0.0269| |agieval_lsat_lr | 0|acc |0.3510|± |0.0212| | | |acc_norm|0.3627|± |0.0213| |agieval_lsat_rc | 0|acc |0.4647|± |0.0305| | | |acc_norm|0.4424|± |0.0303| |agieval_sat_en | 0|acc |0.6602|± |0.0331| | | |acc_norm|0.6165|± |0.0340| |agieval_sat_en_without_passage| 0|acc |0.4320|± |0.0346| | | |acc_norm|0.4272|± |0.0345| |agieval_sat_math | 0|acc |0.2909|± |0.0307| | | |acc_norm|0.2727|± |0.0301| ``` GPT-4All Benchmark Set ``` | Task |Version| Metric |Value | |Stderr| |arc_challenge| 0|acc |0.5102|± |0.0146| | | |acc_norm|0.5213|± |0.0146| |arc_easy | 0|acc |0.7959|± |0.0083| | | |acc_norm|0.7567|± |0.0088| |boolq | 1|acc |0.8394|± |0.0064| |hellaswag | 0|acc |0.6164|± |0.0049| | | |acc_norm|0.8009|± |0.0040| |openbookqa | 0|acc |0.3580|± |0.0215| | | |acc_norm|0.4620|± |0.0223| |piqa | 0|acc |0.7992|± |0.0093| | | |acc_norm|0.8069|± |0.0092| |winogrande | 0|acc |0.7127|± |0.0127| ``` BigBench Reasoning Test ``` | Task |Version| Metric |Value | |Stderr| |bigbench_causal_judgement | 0|multiple_choice_grade|0.5526|± |0.0362| |bigbench_date_understanding | 0|multiple_choice_grade|0.7344|± |0.0230| |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.2636|± |0.0275| |bigbench_geometric_shapes | 0|multiple_choice_grade|0.0195|± |0.0073| | | |exact_str_match |0.0000|± |0.0000| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2760|± |0.0200| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2100|± |0.0154| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4400|± |0.0287| |bigbench_movie_recommendation | 0|multiple_choice_grade|0.2440|± |0.0192| |bigbench_navigate | 0|multiple_choice_grade|0.4950|± |0.0158| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5570|± |0.0111| |bigbench_ruin_names | 0|multiple_choice_grade|0.3728|± |0.0229| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1854|± |0.0123| |bigbench_snarks | 0|multiple_choice_grade|0.6298|± |0.0360| |bigbench_sports_understanding | 0|multiple_choice_grade|0.6156|± |0.0155| |bigbench_temporal_sequences | 0|multiple_choice_grade|0.3140|± |0.0147| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2032|± |0.0114| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4400|± |0.0287| ``` These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores: - GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1 - 0.3657 on BigBench, up from 0.328 on hermes-llama1 - 0.372 on AGIEval, up from 0.354 on Hermes-llama1 These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position. ## Resources for Applied Use Cases: Check out LM Studio for a nice chatgpt style interface here: https://lmstudio.ai/ For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot ## Future Plans We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. ## Model Usage The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

TheBloke/vicuna-13B-v1.5-16K-GPTQ

TheBloke

transformers

text-generation

--- license: llama2 model_name: Vicuna 13B v1.5 16K base_model: lmsys/vicuna-13b-v1.5-16k inference: false model_creator: lmsys model_type: llama prompt_template: 'A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user''s questions. USER: {prompt} ASSISTANT: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Vicuna 13B v1.5 16K - GPTQ - Model creator: [lmsys](https://huggingface.co/lmsys) - Original model: [Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k) <!-- description start --> ## Description This repo contains GPTQ model files for [lmsys's Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGUF) * [lmsys's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Vicuna ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.95 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/vicuna-13B-v1.5-16K-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/vicuna-13B-v1.5-16K-GPTQ`. - To download from a specific branch, enter for example `TheBloke/vicuna-13B-v1.5-16K-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `vicuna-13B-v1.5-16K-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/vicuna-13B-v1.5-16K-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: lmsys's Vicuna 13B v1.5 16K # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture - **License:** Llama 2 Community License Agreement - **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288) ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api ## Training Details Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation  Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

cointegrated/roberta-large-cola-krishna2020

cointegrated

transformers

text-classification

This is a RoBERTa-large classifier trained on the CoLA corpus [Warstadt et al., 2019](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00290), which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for evaluation of text style transfer. The model was trained in the paper [Krishna et al, 2020. Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700), and its original version is available at [their project page](http://style.cs.umass.edu). We converted this model from Fairseq to Transformers format. All credit goes to the authors of the original paper. ## Citation If you found this model useful and refer to it, please cite the original work: ``` @inproceedings{style20, author={Kalpesh Krishna and John Wieting and Mohit Iyyer}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = "2020", Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation}, } ```

google/tapas-large-finetuned-wtq

google

transformers

table-question-answering

--- language: en tags: - tapas - table-question-answering license: apache-2.0 datasets: - wikitablequestions --- # TAPAS large model fine-tuned on WikiTable Questions (WTQ) This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is: - `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_large` (intermediate pre-training, absolute position embeddings). Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. ## Results Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- **LARGE** | **noreset** | **0.5062** | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) **LARGE** | **reset** | **0.5097** | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main) BASE | noreset | 0.4525 | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset) BASE | reset | 0.4638 | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main) MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset) MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main) SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset) SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main) MINI | noreset | 0.2783 | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset) MINI | reset | 0.2854 | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main) TINY | noreset | 0.0823 | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset) TINY | reset | 0.1039 | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main) ## Model description TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of a table and associated text. - Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements. This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQa, WikiSQL and finally WTQ. ## Intended uses & limitations You can use this model for answering questions related to a table. For code examples, we refer to the documentation of TAPAS on the HuggingFace website. ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Question [SEP] Flattened table [SEP] ``` The authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts. ### Fine-tuning The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512. In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 1.93581e-5, and a warmup ratio of 0.128960. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the `select_one_column` parameter of `TapasConfig`. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12). ### BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ```bibtex @misc{eisenschlos2020understanding, title={Understanding tables with intermediate pre-training}, author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller}, year={2020}, eprint={2010.00571}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @article{DBLP:journals/corr/PasupatL15, author = {Panupong Pasupat and Percy Liang}, title = {Compositional Semantic Parsing on Semi-Structured Tables}, journal = {CoRR}, volume = {abs/1508.00305}, year = {2015}, url = {http://arxiv.org/abs/1508.00305}, archivePrefix = {arXiv}, eprint = {1508.00305}, timestamp = {Mon, 13 Aug 2018 16:47:37 +0200}, biburl = {https://dblp.org/rec/journals/corr/PasupatL15.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

EleutherAI/gpt-neo-1.3B

EleutherAI

transformers

text-generation

--- language: - en tags: - text generation - pytorch - causal-lm license: mit datasets: - EleutherAI/pile --- # GPT-Neo 1.3B ## Model Description GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model. ## Training data GPT-Neo 1.3B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. ## Training procedure This model was trained on the Pile for 380 billion tokens over 362,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. ## Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B') >>> generator("EleutherAI has", do_sample=True, min_length=50) [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Eval results ### Linguistic Reasoning | Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag | | ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- | | **GPT-Neo 1.3B** | **0.7527** | **6.159** | **13.10** | **7.498** | **57.23%** | **55.01%** | **38.66%** | | GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% | | GPT-Neo 2.7B | 0.7165 | 5.646 | 11.39 | 5.626 | 62.22% | 56.50% | 42.73% | | GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% | ### Physical and Scientific Reasoning | Model and Size | MathQA | PubMedQA | Piqa | | ---------------- | ---------- | ---------- | ----------- | | **GPT-Neo 1.3B** | **24.05%** | **54.40%** | **71.11%** | | GPT-2 1.5B | 23.64% | 58.33% | 70.78% | | GPT-Neo 2.7B | 24.72% | 57.54% | 72.14% | | GPT-3 Ada | 24.29% | 52.80% | 68.88% | ### Down-Stream Applications TBD ### BibTeX entry and citation info To cite this model, please use ```bibtex @software{gpt-neo, author = {Black, Sid and Leo, Gao and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}}, month = mar, year = 2021, note = {{If you use this software, please cite it using these metadata.}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.5297715}, url = {https://doi.org/10.5281/zenodo.5297715} } @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ```

microsoft/codebert-base-mlm

microsoft

transformers

fill-mask

## CodeBERT-base-mlm Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155). ### Training Data The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet) ### Training Objective This model is initialized with Roberta-base and trained with a simple MLM (Masked Language Model) objective. ### Usage ```python from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline model = RobertaForMaskedLM.from_pretrained('microsoft/codebert-base-mlm') tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base-mlm') code_example = "if (x is not None) <mask> (x>1)" fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer) outputs = fill_mask(code_example) print(outputs) ``` Expected results: ``` {'sequence': '<s> if (x is not None) and (x>1)</s>', 'score': 0.6049249172210693, 'token': 8} {'sequence': '<s> if (x is not None) or (x>1)</s>', 'score': 0.30680200457572937, 'token': 50} {'sequence': '<s> if (x is not None) if (x>1)</s>', 'score': 0.02133703976869583, 'token': 114} {'sequence': '<s> if (x is not None) then (x>1)</s>', 'score': 0.018607674166560173, 'token': 172} {'sequence': '<s> if (x is not None) AND (x>1)</s>', 'score': 0.007619690150022507, 'token': 4248} ``` ### Reference 1. [Bimodal CodeBERT trained with MLM+RTD objective](https://huggingface.co/microsoft/codebert-base) (suitable for code search and document generation) 2. 🤗 [Hugging Face's CodeBERTa](https://huggingface.co/huggingface/CodeBERTa-small-v1) (small size, 6 layers) ### Citation ```bibtex @misc{feng2020codebert, title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages}, author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou}, year={2020}, eprint={2002.08155}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ai-forever/ruRoberta-large

ai-forever

transformers

fill-mask

--- language: - ru tags: - PyTorch - Transformers thumbnail: "https://github.com/sberbank-ai/model-zoo" --- # ruRoberta-large The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931). The model is pretrained by the [SberDevices](https://sberdevices.ru/) team. * Task: `mask filling` * Type: `encoder` * Tokenizer: `BBPE` * Dict size: `50 257` * Num Parameters: `355 M` * Training Data Volume `250 GB` # Authors + NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam): + Dmitry Zmitrovich # Cite us ``` @misc{zmitrovich2023family, title={A Family of Pretrained Transformer Language Models for Russian}, author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova}, year={2023}, eprint={2309.10931}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Helsinki-NLP/opus-mt-en-ru

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-en-ru * source languages: en * target languages: ru * OPUS readme: [en-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ru/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-11.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-ru/opus-2020-02-11.zip) * test set translations: [opus-2020-02-11.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ru/opus-2020-02-11.test.txt) * test set scores: [opus-2020-02-11.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ru/opus-2020-02-11.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.en.ru | 31.1 | 0.581 | | newstest2013.en.ru | 23.5 | 0.513 | | newstest2015-enru.en.ru | 27.5 | 0.564 | | newstest2016-enru.en.ru | 26.4 | 0.548 | | newstest2017-enru.en.ru | 29.1 | 0.572 | | newstest2018-enru.en.ru | 25.4 | 0.554 | | newstest2019-enru.en.ru | 27.1 | 0.533 | | Tatoeba.en.ru | 48.4 | 0.669 |

h2oai/h2ogpt-4096-llama2-7b-chat

h2oai

transformers

text-generation

--- inference: false language: - en license: llama2 model_type: llama pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 - h2ogpt --- h2oGPT clone of [Meta's Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). Try it live on our [h2oGPT demo](https://gpt.h2o.ai) with side-by-side LLM comparisons and private document chat! See how it compares to other models on our [LLM Leaderboard](https://evalgpt.ai/)! See more at [H2O.ai](https://h2o.ai/) ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=4096, bias=False) (v_proj): Linear(in_features=4096, out_features=4096, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=4096, out_features=11008, bias=False) (up_proj): Linear(in_features=4096, out_features=11008, bias=False) (down_proj): Linear(in_features=11008, out_features=4096, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ```

indolem/indobert-base-uncased

indolem

transformers

fill-mask

--- language: id tags: - indobert - indolem license: mit inference: False --- ## About [IndoBERT](https://arxiv.org/pdf/2011.00677.pdf) is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources: * Indonesian Wikipedia (74M words) * news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total) * an Indonesian Web Corpus (Medved and Suchomel, 2017) (90M words). We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being <b>3.97</b> (similar to English BERT-base). This <b>IndoBERT</b> was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. | Task | Metric | Bi-LSTM | mBERT | MalayBERT | IndoBERT | | ---- | ---- | ---- | ---- | ---- | ---- | | POS Tagging | Acc | 95.4 | <b>96.8</b> | <b>96.8</b> | <b>96.8</b> | | NER UGM | F1| 70.9 | 71.6 | 73.2 | <b>74.9</b> | | NER UI | F1 | 82.2 | 82.2 | 87.4 | <b>90.1</b> | | Dep. Parsing (UD-Indo-GSD) | UAS/LAS | 85.25/80.35 | 86.85/81.78 | 86.99/81.87 | <b>87.12<b/>/<b>82.32</b> | | Dep. Parsing (UD-Indo-PUD) | UAS/LAS | 84.04/79.01 | <b>90.58</b>/<b>85.44</b> | 88.91/83.56 | 89.23/83.95 | | Sentiment Analysis | F1 | 71.62 | 76.58 | 82.02 | <b>84.13</b> | | Summarization | R1/R2/RL | 67.96/61.65/67.24 | 68.40/61.66/67.67 | 68.44/61.38/67.71 | <b>69.93</b>/<b>62.86</b>/<b>69.21</b> | | Next Tweet Prediction | Acc | 73.6 | 92.4 | 93.1 | <b>93.7</b> | | Tweet Ordering | Spearman corr. | 0.45 | 0.53 | 0.51 | <b>0.59</b> | The paper is published at the 28th COLING 2020. Please refer to https://indolem.github.io for more details about the benchmarks. ## How to use ### Load model and tokenizer (tested with transformers==3.5.1) ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased") model = AutoModel.from_pretrained("indolem/indobert-base-uncased") ``` ## Citation If you use our work, please cite: ```bibtex @inproceedings{koto2020indolem, title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP}, author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin}, booktitle={Proceedings of the 28th COLING}, year={2020} } ```

Babelscape/rebel-large

Babelscape

transformers

text2text-generation

--- language: - en widget: - text: "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic" tags: - seq2seq - relation-extraction datasets: - Babelscape/rebel-dataset model-index: - name: REBEL results: - task: name: Relation Extraction type: Relation-Extraction dataset: name: "CoNLL04" type: CoNLL04 metrics: - name: RE+ Macro F1 type: re+ macro f1 value: 76.65 - task: name: Relation Extraction type: Relation-Extraction dataset: name: "NYT" type: NYT metrics: - name: F1 type: f1 value: 93.4 license: cc-by-nc-sa-4.0 --- [](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-conll04?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-3?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-ade-corpus?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-re-tacred?p=rebel-relation-extraction-by-end-to-end) ## Multilingual update! Check [mREBEL](https://huggingface.co/Babelscape/mrebel-large), a multilingual version covering more relation types, languages and including entity types. # REBEL <img src="https://i.ibb.co/qsLzNqS/hf-rebel.png" width="30" alt="hf-rebel" border="0" style="display:inline; white-space:nowrap;">: Relation Extraction By End-to-end Language generation This is the model card for the Findings of EMNLP 2021 paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper: @inproceedings{huguet-cabot-navigli-2021-rebel-relation, title = "{REBEL}: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.204", pages = "2370--2381", abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", } The original repository for the paper can be found [here](https://github.com/Babelscape/rebel) Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/rebel-demo). ## Pipeline usage ```python from transformers import pipeline triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large') # We need to use the tokenizer manually since we need special tokens. extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor("Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic", return_tensors=True, return_text=False)[0]["generated_token_ids"]]) print(extracted_text[0]) # Function to parse the generated text and extract the triplets def extract_triplets(text): triplets = [] relation, subject, relation, object_ = '', '', '', '' text = text.strip() current = 'x' for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): if token == "<triplet>": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) relation = '' subject = '' elif token == "<subj>": current = 's' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) object_ = '' elif token == "<obj>": current = 'o' relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) return triplets extracted_triplets = extract_triplets(extracted_text[0]) print(extracted_triplets) ``` ## Model and Tokenizer using transformers ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer def extract_triplets(text): triplets = [] relation, subject, relation, object_ = '', '', '', '' text = text.strip() current = 'x' for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): if token == "<triplet>": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) relation = '' subject = '' elif token == "<subj>": current = 's' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) object_ = '' elif token == "<obj>": current = 'o' relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) return triplets # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large") model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large") gen_kwargs = { "max_length": 256, "length_penalty": 0, "num_beams": 3, "num_return_sequences": 3, } # Text to extract triplets from text = 'Punta Cana is a resort town in the municipality of Higüey, in La Altagracia Province, the easternmost province of the Dominican Republic.' # Tokenizer text model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt') # Generate generated_tokens = model.generate( model_inputs["input_ids"].to(model.device), attention_mask=model_inputs["attention_mask"].to(model.device), **gen_kwargs, ) # Extract text decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False) # Extract triplets for idx, sentence in enumerate(decoded_preds): print(f'Prediction triplets sentence {idx}') print(extract_triplets(sentence)) ```

timm/tf_efficientnet_b0.ns_jft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for tf_efficientnet_b0.ns_jft_in1k A EfficientNet image classification model. Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 5.3 - GMACs: 0.4 - Activations (M): 6.7 - Image size: 224 x 224 - **Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946 - Self-training with Noisy Student improves ImageNet classification: https://arxiv.org/abs/1911.04252 - **Dataset:** ImageNet-1k - **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('tf_efficientnet_b0.ns_jft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b0.ns_jft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 16, 112, 112]) # torch.Size([1, 24, 56, 56]) # torch.Size([1, 40, 28, 28]) # torch.Size([1, 112, 14, 14]) # torch.Size([1, 320, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b0.ns_jft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1280, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2019efficientnet, title={Efficientnet: Rethinking model scaling for convolutional neural networks}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={6105--6114}, year={2019}, organization={PMLR} } ``` ```bibtex @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019}, pages={10684-10695} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

WarriorMama777/OrangeMixs

WarriorMama777

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image datasets: Nerfgun3/bad_prompt --- ---- # OrangeMixs "OrangeMixs" shares various Merge models that can be used with StableDiffusionWebui:Automatic1111 and others. &nbsp; <img src="https://i.imgur.com/VZg0LqQ.png" width="1000" height=""> Maintain a repository for the following purposes. 1. to provide easy access to models commonly used in the Japanese community.The Wisdom of the Anons💎 2. As a place to upload my merge models when I feel like it.  <span style="font-size: 60%;">Hero image prompts(AOM3B2):https://majinai.art/ja/i/jhw20Z_</span> ---- # UPDATE NOTE / How to read this README ## How to read this README 1. Read the ToC as release notes. Sections are in descending order. The order within the section is ascending. It is written like SNS. 2. UPDATE NOTE 3. View the repository history when you need to check the full history. ## UPDATE NOTE - 2023-02-27: Add AOM3A1B - 2023-03-10: Model name fix I found that I abbreviated the model name too much, so that when users see illustrations using OrangeMixs models on the web, they cannot reach them in their searches. To make the specification more search engine friendly, I renamed it to "ModelName + (orangemixs)". - 2023-03-11: Change model name : () to _ Changed to _ because an error occurs when using () in the Cloud environment(e.g.:paperspace). "ModelName + _orangemixs" - 2023-04-01: Added description of AOM3A1 cursed by Dreamlike - 2023-06-27: Added AOM3B2. Remove terms of service. ---- # Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run OrangeMixs: [](https://huggingface.co/spaces/akhaliq/webui-orangemixs) ---- # Table of Contents - [OrangeMixs](#orangemixs) - [UPDATE NOTE / How to read this README](#update-note--how-to-read-this-readme) - [How to read this README](#how-to-read-this-readme) - [UPDATE NOTE](#update-note) - [Gradio](#gradio) - [Table of Contents](#table-of-contents) - [Reference](#reference) - [Licence](#licence) - [~~Terms of use~~](#terms-of-use) - [Disclaimer](#disclaimer) - [How to download](#how-to-download) - [Batch Download](#batch-download) - [Batch Download (Advanced)](#batch-download-advanced) - [Select and download](#select-and-download) - [Model Detail \& Merge Recipes](#model-detail--merge-recipes) - [AbyssOrangeMix3 (AOM3)](#abyssorangemix3-aom3) - [About](#about) - [More feature](#more-feature) - [Variations / Sample Gallery](#variations--sample-gallery) - [AOM3](#aom3) - [AOM3A1](#aom3a1) - [AOM3A2](#aom3a2) - [AOM3A3](#aom3a3) - [AOM3A1B](#aom3a1b) - [AOM3B2](#aom3b2) - [Description for enthusiast](#description-for-enthusiast) - [AbyssOrangeMix2 (AOM2)](#abyssorangemix2-aom2) - [AbyssOrangeMix2\_sfw (AOM2s)](#abyssorangemix2_sfw-aom2s) - [AbyssOrangeMix2\_nsfw (AOM2n)](#abyssorangemix2_nsfw-aom2n) - [AbyssOrangeMix2\_hard (AOM2h)](#abyssorangemix2_hard-aom2h) - [EerieOrangeMix (EOM)](#eerieorangemix-eom) - [EerieOrangeMix (EOM1)](#eerieorangemix-eom1) - [EerieOrangeMix\_base (EOM1b)](#eerieorangemix_base-eom1b) - [EerieOrangeMix\_Night (EOM1n)](#eerieorangemix_night-eom1n) - [EerieOrangeMix\_half (EOM1h)](#eerieorangemix_half-eom1h) - [EerieOrangeMix (EOM1)](#eerieorangemix-eom1-1) - [EerieOrangeMix2 (EOM2)](#eerieorangemix2-eom2) - [EerieOrangeMix2\_base (EOM2b)](#eerieorangemix2_base-eom2b) - [EerieOrangeMix2\_night (EOM2n)](#eerieorangemix2_night-eom2n) - [EerieOrangeMix2\_half (EOM2h)](#eerieorangemix2_half-eom2h) - [EerieOrangeMix2 (EOM2)](#eerieorangemix2-eom2-1) - [Models Comparison](#models-comparison) - [AbyssOrangeMix (AOM)](#abyssorangemix-aom) - [AbyssOrangeMix\_base (AOMb)](#abyssorangemix_base-aomb) - [AbyssOrangeMix\_Night (AOMn)](#abyssorangemix_night-aomn) - [AbyssOrangeMix\_half (AOMh)](#abyssorangemix_half-aomh) - [AbyssOrangeMix (AOM)](#abyssorangemix-aom-1) - [ElyOrangeMix (ELOM)](#elyorangemix-elom) - [ElyOrangeMix (ELOM)](#elyorangemix-elom-1) - [ElyOrangeMix\_half (ELOMh)](#elyorangemix_half-elomh) - [ElyNightOrangeMix (ELOMn)](#elynightorangemix-elomn) - [BloodOrangeMix (BOM)](#bloodorangemix-bom) - [BloodOrangeMix (BOM)](#bloodorangemix-bom-1) - [BloodOrangeMix\_half (BOMh)](#bloodorangemix_half-bomh) - [BloodNightOrangeMix (BOMn)](#bloodnightorangemix-bomn) - [ElderOrangeMix](#elderorangemix) - [Troubleshooting](#troubleshooting) - [FAQ and Tips (🐈MEME ZONE🦐)](#faq-and-tips-meme-zone) ---- # Reference +/hdg/ Stable Diffusion Models Cookbook - <https://rentry.org/hdgrecipes#g-anons-unnamed-mix-e93c3bf7> Model names are named after Cookbook precedents🍊 # Licence This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here ：https://huggingface.co/spaces/CompVis/stable-diffusion-license # ~~Terms of use~~ ~~- **Clearly indicate where modifications have been made.** If you used it for merging, please state what steps you took to do so.~~ Removed terms of use. 2023-06-28 freedom. If you publish the recipe, Marge swamp will be fun. # Disclaimer <details><summary>READ MORE: Disclaimer</summary> The user has complete control over whether or not to generate NSFW content, and the user's decision to enjoy either SFW or NSFW is entirely up to the user.The learning model does not contain any obscene visual content that can be viewed with a single click.The posting of the Learning Model is not intended to display obscene material in a public place. In publishing examples of the generation of copyrighted characters, I consider the following cases to be exceptional cases in which unauthorised use is permitted. "when the use is for private use or research purposes; when the work is used as material for merchandising (however, this does not apply when the main use of the work is to be merchandised); when the work is used in criticism, commentary or news reporting; when the work is used as a parody or derivative work to demonstrate originality." In these cases, use against the will of the copyright holder or use for unjustified gain should still be avoided, and if a complaint is lodged by the copyright holder, it is guaranteed that the publication will be stopped as soon as possible. I would also like to note that I am aware of the fact that many of the merged models use NAI, which is learned from Danbooru and other sites that could be interpreted as illegal, and whose model data itself is also a leak, and that this should be watched carefully. I believe that the best we can do is to expand the possibilities of GenerativeAI while protecting the works of illustrators and artists. </details> ---- # How to download ## Batch Download ⚠Deprecated: Orange has grown too huge. Doing this will kill your storage. 1. install Git 2. create a folder of your choice and right click → "Git bash here" and open a gitbash on the folder's directory. 3. run the following commands in order. ``` git lfs install git clone https://huggingface.co/WarriorMama777/OrangeMixs ``` 4. complete ## Batch Download (Advanced) Advanced: (When you want to download only selected directories, not the entire repository.) &nbsp; <details> <summary>Toggle: How to Batch Download (Advanced)</summary> 1. Run the command `git clone --filter=tree:0 --no-checkout https://huggingface.co/WarriorMama777/OrangeMixs` to clone the huggingface repository. By adding the `--filter=tree:0` and `--no-checkout` options, you can download only the file names without their contents. ``` git clone --filter=tree:0 --no-checkout https://huggingface.co/WarriorMama777/OrangeMixs ``` 2. Move to the cloned directory with the command `cd OrangeMixs`. ``` cd OrangeMixs ``` 3. Enable sparse-checkout mode with the command `git sparse-checkout init --cone`. By adding the `--cone` option, you can achieve faster performance. ``` git sparse-checkout init --cone ``` 4. Specify the directory you want to get with the command `git sparse-checkout add <directory name>`. For example, if you want to get only the `Models/AbyssOrangeMix3` directory, enter `git sparse-checkout add Models/AbyssOrangeMix3`. ``` git sparse-checkout add Models/AbyssOrangeMix3 ``` 5. Download the contents of the specified directory with the command `git checkout main`. ``` git checkout main ``` This completes how to clone only a specific directory. If you want to add other directories, run `git sparse-checkout add <directory name>` again. </details> ## Select and download 1. Go to the Files and vaersions tab. 2. select the model you want to download 3. download 4. complete ---- ---- # Model Detail & Merge Recipes ## AbyssOrangeMix3 (AOM3)  ――Everyone has different “ABYSS”! ▼About The main model, "AOM3 (AbyssOrangeMix3)", is a purely upgraded model that improves on the problems of the previous version, "AOM2". "AOM3" can generate illustrations with very realistic textures and can generate a wide variety of content. There are also three variant models based on the AOM3 that have been adjusted to a unique illustration style. These models will help you to express your ideas more clearly. ▼Links - [⚠NSFW] Civitai: AbyssOrangeMix3 (AOM3) | Stable Diffusion Checkpoint | https://civitai.com/models/9942/abyssorangemix3-aom3 ### About Features: high-quality, realistic textured illustrations can be generated. There are two major changes from AOM2. 1: Models for NSFW such as _nsfw and _hard have been improved: the models after nsfw in AOM2 generated creepy realistic faces, muscles and ribs when using Hires.fix, even though they were animated characters. These have all been improved in AOM3. e.g.: explanatory diagram by MEME : [GO TO MEME ZONE↓](#MEME_realface) 2: sfw/nsfw merged into one model. Originally, nsfw models were separated because adding NSFW content (models like NAI and gape) would change the face and cause the aforementioned problems. Now that those have been improved, the models can be packed into one. In addition, thanks to excellent extensions such as [ModelToolkit](https://github.com/arenatemp/stable-diffusion-webui-model-toolkit ), the model file size could be reduced (1.98 GB per model).  ### More feature In addition, these U-Net Blocks Weight Merge models take numerous steps but are carefully merged to ensure that mutual content is not overwritten. (Of course, all models allow full control over adult content.) - 🔐 When generating illustrations for the general public: write "nsfw" in the negative prompt field - 🔞 ~~When generating adult illustrations: "nsfw" in the positive prompt field~~ -> It can be generated without putting it in. If you include it, the atmosphere will be more NSFW. ### Variations / Sample Gallery 🚧Editing🚧  #### AOM3 ▼AOM3  <span style="font-size: 60%;">(Actually, this gallery doesn't make much sense since AOM3 is mainly an improvement of the NSFW part 😂 ...But we can confirm that the picture is not much different from AOM2sfw.)</span> #### AOM3A1 ⛔Only this model (AOM3A1) includes ChilloutMix. The curse of the DreamLike license. In other words, only AOM3A1 is not available for commercial use. I recommend AOM3A1B instead.⛔ [GO TO MEME ZONE↓](#MEME_AOM3A1) Features: Anime like illustrations with flat paint. Cute enough as it is, but I really like to apply LoRA of anime characters to this model to generate high quality anime illustrations like a frame from a theatre version. ▼A1  <details> <summary>©</summary> (1)©Yurucamp: Inuyama Aoi, (2)©The Quintessential Quintuplets: Nakano Yotsuba, (3)©Sailor Moon: Mizuno Ami/SailorMercury </details> #### AOM3A2 🚧Editing🚧 Features: Oil paintings like style artistic illustrations and stylish background depictions. In fact, this is mostly due to the work of Counterfeit 2.5, but the textures are more realistic thanks to the U-Net Blocks Weight Merge. #### AOM3A3 🚧Editing🚧 Features: Midpoint of artistic and kawaii. the model has been tuned to combine realistic textures, a artistic style that also feels like an oil colour style, and a cute anime-style face. Can be used to create a wide range of illustrations. #### AOM3A1B AOM3A1B added. This model is my latest favorite. I recommend it for its moderate realism, moderate brush touch, and moderate LoRA conformity. The model was merged by mistakenly selecting 'Add sum' when 'Add differences' should have been selected in the AOM3A3 recipe. It was an unintended merge, but we share it because the illustrations produced are consistently good results. In my review, this is an illustration style somewhere between AOM3A1 and A3. ▼A1B   - Meisho Doto (umamusume): https://civitai.com/models/11980/meisho-doto-umamusume - Train and Girl: [JR East E235 series / train interior](https://civitai.com/models/9517/jr-east-e235-series-train-interior) <details> <summary>©</summary> ©umamusume: Meisho Doto, ©Girls und Panzer: Nishizumi Miho,©IDOLM@STER: Sagisawa Fumika </details> #### AOM3B2 my newest toy. Just AOM3A1B + BreakdomainM21: 0.4 So this model is somewhat of a troll model. I would like to create an improved DiffLoRAKit_v2 based on this. Upload for access for research etc. 2023-06-27  <details><summary>Sample image prompts</summary> 1. [Maid](https://majinai.art/ja/i/jhw20Z_) 2. Yotsuba: https://majinai.art/ja/i/f-O4wau 3. Inuko in cafe: https://majinai.art/ja/i/Cj-Ar9C 4. bathroom: https://majinai.art/ja/i/XiSj5K6 </details> &nbsp; ____ ### Description for enthusiast AOM3 was created with a focus on improving the nsfw version of AOM2, as mentioned above.The AOM3 is a merge of the following two models into AOM2sfw using U-Net Blocks Weight Merge, while extracting only the NSFW content part. (1) NAI: trained in Danbooru (2)gape: Finetune model of NAI trained on Danbooru's very hardcore NSFW content. In other words, if you are looking for something like AOM3sfw, it is AOM2sfw.The AOM3 was merged with the NSFW model while removing only the layers that have a negative impact on the face and body. However, the faces and compositions are not an exact match to AOM2sfw.AOM2sfw is sometimes superior when generating SFW content. I recommend choosing according to the intended use of the illustration.See below for a comparison between AOM2sfw and AOM3.  ▼A summary of the AOM3 work is as follows 1. investigated the impact of the NAI and gape layers as AOM2 _nsfw onwards is crap. 2. cut face layer: OUT04 because I want realistic faces to stop → Failed. No change. 3. gapeNAI layer investigation｜ a. (IN05-08 (especially IN07) | Change the illustration significantly. Noise is applied, natural colours are lost, shadows die, and we can see that the IN deep layer is a layer of light and shade. b. OUT03-05(?) | likely to be sexual section/NSFW layer.Cutting here will kill the NSFW. c. OUT03,OUT04｜NSFW effects are in(?). e.g.: spoken hearts, trembling, motion lines, etc... d. OUT05｜This is really an NSFW switch. All the "NSFW atmosphere" is in here. Facial expressions, Heavy breaths, etc... e. OUT10-11｜Paint layer. Does not affect detail, but does have an extensive impact. 1. (mass production of rubbish from here...) 2. cut IN05-08 and merge NAIgape with flat parameters → avoided creepy muscles and real faces. Also, merging NSFW models stronger has less impact. 3. so, cut IN05-08, OUT10-11 and merge NAI+gape with all others 0.5. 4. → AOM3 AOM3 roughly looks like this ---- ▼How to use - Prompts - Negative prompts is As simple as possible is good. (worst quality, low quality:1.4) - Using "3D" as a negative will result in a rough sketch style at the "sketch" level. Use with caution as it is a very strong prompt. - How to avoid Real Face (realistic, lip, nose, tooth, rouge, lipstick, eyeshadow:1.0), (abs, muscular, rib:1.0), - How to avoid Bokeh (depth of field, bokeh, blurry:1.4) - How to remove mosaic: `(censored, mosaic censoring, bar censor, convenient censoring, pointless censoring:1.0),` - How to remove blush: `(blush, embarrassed, nose blush, light blush, full-face blush:1.4), ` - How to remove NSFW effects: `(trembling, motion lines, motion blur, emphasis lines:1.2),` - 🔰Basic negative prompts sample for Anime girl ↓ - v1 `nsfw, (worst quality, low quality:1.4), (realistic, lip, nose, tooth, rouge, lipstick, eyeshadow:1.0), (dusty sunbeams:1.0),, (abs, muscular, rib:1.0), (depth of field, bokeh, blurry:1.4),(motion lines, motion blur:1.4), (greyscale, monochrome:1.0), text, title, logo, signature` - v2 `nsfw, (worst quality, low quality:1.4), (lip, nose, tooth, rouge, lipstick, eyeshadow:1.4), (blush:1.2), (jpeg artifacts:1.4), (depth of field, bokeh, blurry, film grain, chromatic aberration, lens flare:1.0), (1boy, abs, muscular, rib:1.0), greyscale, monochrome, dusty sunbeams, trembling, motion lines, motion blur, emphasis lines, text, title, logo, signature, ` - Sampler: ~~“DPM++ SDE Karras” is good~~ Take your pick - Steps: - DPM++ SDE Karras: Test: 12～ ,illustration: 20～ - DPM++ 2M Karras: Test: 20～ ,illustration: 28～ - Clipskip: 1 or 2 - CFG: 8 (6～12) - Upscaler : - Detailed illust → Latenet (nearest-exact) Denoise strength: 0.5 (0.5~0.6) - Simple upscale: Swin IR, ESRGAN, Remacri etc… Denoise strength: Can be set low. (0.35~0.6) --- 👩‍🍳Model details / Recipe ▼Hash - AOM3.safetensors D124FC18F0232D7F0A2A70358CDB1288AF9E1EE8596200F50F0936BE59514F6D - AOM3A1.safetensors F303D108122DDD43A34C160BD46DBB08CB0E088E979ACDA0BF168A7A1F5820E0 - AOM3A2.safetensors 553398964F9277A104DA840A930794AC5634FC442E6791E5D7E72B82B3BB88C3 - AOM3A3.safetensors EB4099BA9CD5E69AB526FCA22A2E967F286F8512D9509B735C892FA6468767CF ▼Use Models 1. AOM2sfw 「038ba203d8ba3c8af24f14e01fbb870c85bbb8d4b6d9520804828f4193d12ce9」 1. AnythingV3.0 huggingface pruned [2700c435]「543bcbc21294831c6245cd74c8a7707761e28812c690f946cb81fef930d54b5e」 1. NovelAI animefull-final-pruned [925997e9]「89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8」 1. NovelAI sfw [1d4a34af]「22fa233c2dfd7748d534be603345cb9abf994a23244dfdfc1013f4f90322feca」 1. Gape60 [25396b85]「893cca5903ccd0519876f58f4bc188dd8fcc5beb8a69c1a3f1a5fe314bb573f5」 1. BasilMix 「bbf07e3a1c3482c138d096f7dcdb4581a2aa573b74a68ba0906c7b657942f1c2」 1. chilloutmix_fp16.safetensors 「4b3bf0860b7f372481d0b6ac306fed43b0635caf8aa788e28b32377675ce7630」 1. Counterfeit-V2.5_fp16.safetensors 「71e703a0fca0e284dd9868bca3ce63c64084db1f0d68835f0a31e1f4e5b7cca6」 1. kenshi_01_fp16.safetensors 「3b3982f3aaeaa8af3639a19001067905e146179b6cddf2e3b34a474a0acae7fa」 ---- ▼AOM3 ▼**Instructions:** USE: [https://github.com/hako-mikan/sd-webui-supermerger/](https://github.com/hako-mikan/sd-webui-supermerger/) (This extension is really great. It turns a month's work into an hour. Thank you) STEP: 1 | BWM : NAI - NAIsfw & gape - NAI CUT: IN05-IN08, OUT10-11 | Model: A | Model: B | Model: C | Interpolation Method | Weight | Merge Name | | -------- | -------- | -------- | -------------------- | ----------------------------------------------------------------------------------------- | ---------- | | AOM2sfw | NAI full | NAI sfw | Add Difference @ 1.0 | 0,0.5,0.5,0.5,0.5,0.5,0,0,0,0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0,0 | temp01 | CUT: IN05-IN08, OUT10-11 | Model: A | Model: B | Model: C | Interpolation Method | Weight | Merge Name | | -------- | -------- | -------- | -------------------- | ----------------------------------------------------------------------------------------- | ---------- | | temp01 | gape60 | NAI full | Add Difference @ 1.0 | 0,0.5,0.5,0.5,0.5,0.5,0,0,0,0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0,0 | AOM3 | ▼AOM3A1 ⛔Only this model (AOM3A1) includes ChilloutMix (=The curse of DreamLike).Commercial use is not available. ▼**Instructions:** STEP: 1 | Change the base photorealistic model of AOM3 from BasilMix to Chilloutmix. Change the photorealistic model from BasilMix to Chilloutmix and proceed to gapeNAI merge. STEP: 2 | | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------- | --------------- | -------------- | ------------------ | | 1 | SUM @ 0.5 | Counterfeit2.5 | Kenshi | | Counterfeit+Kenshi | STEP: 3 | CUT: BASE0, IN00-IN08：0, IN10：0.1, OUT03-04-05：0, OUT08：0.2 | Model: A | Model: B | Model: C | Interpolation Method | Weight | Merge Name | | -------- | ------------------ | -------- | -------------------- | --------------------------------------------------------------------------- | ---------- | | AOM3 | Counterfeit+Kenshi | | Add SUM @ 1.0 | 0,0,0,0,0,0,0,0,0,0.3,0.1,0.3,0.3,0.3,0.2,0.1,0,0,0,0.3,0.3,0.2,0.3,0.4,0.5 | AOM3A1 | ▼AOM3A2 ▼? CUT: BASE0, IN05:0.3、IN06-IN08：0, IN10：0.1, OUT03：0, OUT04：0.3, OUT05：0, OUT08：0.2 ▼**Instructions:** | Model: A | Model: B | Model: C | Interpolation Method | Weight | Merge Name | | -------- | -------------- | -------- | -------------------- | --------------------------------------------------------- | ---------- | | AOM3 | Counterfeit2.5 | | Add SUM @ 1.0 | 0,1,1,1,1,1,0.3,0,0,0,1,0.1,1,1,1,1,1,0,1,0,1,1,0.2,1,1,1 | AOM3A2 | ▼AOM3A3 ▼? CUT : BASE0, IN05-IN08：0, IN10：0.1, OUT03：0.5, OUT04-05：0.1, OUT08：0.2 | Model: A | Model: B | Model: C | Interpolation Method | Weight | Merge Name | | -------- | -------------- | -------- | -------------------- | --------------------------------------------------------------------------------------------- | ---------- | | AOM3 | Counterfeit2.5 | | Add SUM @ 1.0 | 0,0.6,0.6,0.6,0.6,0.6,0,0,0,0,0.6,0.1,0.6,0.6,0.6,0.6,0.6,0.5,0.1,0.1,0.6,0.6,0.2,0.6,0.6,0.6 | AOM3A3 | ---- &nbsp; ## AbyssOrangeMix2 (AOM2) ――Creating the next generation of illustration with “Abyss”! <img src="https://github.com/WarriorMama777/imgup/raw/main/img/AbyssOrangeMix2/HeroImage_AbyssOrangeMix2_Designed_01_comp001.webp" width="" height="" alt=”HeroImage_AbyssOrangeMix2_Designed_01_comp001”> Prompt: [https://majinai.art/ja/i/nxpKRpw](https://majinai.art/ja/i/nxpKRpw) ▼About AbyssOrangeMix2 (AOM2) is an AI model capable of generating high-quality, highly realistic illustrations. It can generate elaborate and detailed illustrations that cannot be drawn by hand. It can also be used for a variety of purposes, making it extremely useful for design and artwork. Furthermore, it provides an unparalleled new means of expression. It can generate illustrations in a variety of genres to meet a wide range of needs. I encourage you to use "Abyss" to make your designs and artwork richer and of higher quality. <img src="https://github.com/WarriorMama777/imgup/raw/main/img/AbyssOrangeMix2/UBM_ON_OFF_4_comp001.webp" width="" height="" alt=”UBM_ON_OFF_4_comp001.webp”> ※nvidia joke. ▼Description for engineers/enthusiasts The merged model was formulated using an extension such as sdweb-merge-block-weighted-gui, which merges models at separate rates for each of the 25 U-Net blocks (input, intermediate, and output). The validation of many Anons has shown that such a recipe can generate a painting style that is anatomically realistic enough to feel the finger skeleton, but still maintains an anime-style face. The changes from AbyssOrangeMix are as follows. 1. the model used for U-Net Blocks Weight Merge was changed from Instagram+F222 to BasilMix. (<https://huggingface.co/nuigurumi>) This is an excellent merge model that can generate decent human bodies while maintaining the facial layers of the Instagram model. Thanks!!! This has improved the dullness of the color and given a more Japanese skin tone (or more precisely, the moisturized white skin that the Japanese would ideally like). Also, the unnatural bokeh that sometimes occurred in the previous version may have been eliminated (needs to be verified). 2.Added IN deep layers (IN06-11) to the layer merging from the realistic model (BasilMix). It is said that the IN deep layer (IN06-11) is the layer that determines composition, etc., but perhaps light, reflections, skin texture, etc., may also be involved. It is like "Global Illumination", "Ray tracing" and "Ambient Occlusion" in 3DCG. <img src="https://github.com/WarriorMama777/imgup/raw/main/img/AbyssOrangeMix2/AbyssOrangeMix2_comparison_comp001.webp" width="" height="" alt=”AbyssOrangeMix2_comparison_comp001”> ※This does not fundamentally improve the fingers. Therefore, More research needs to be done to improve the fingers (e.g. '[bad_prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt)'). About 30-50% chance of generating correct fingers(?). Abyss is deep. ▼Sample Gallery The prompts for generating these images were all generated using ChatGPT. I simply asked "Pirates sailing the oceans" to tell me what the prompts were. However, to make sure the AI understood the specifications, I used the template for AI questions (Question template for AI prompt generation(v1.2) ). Please review the following. ```jsx https://seesaawiki.jp/nai_ch/d/AI%a4%f2%b3%e8%cd%d1%a4%b7%a4%bf%a5%d7%a5%ed%a5%f3%a5%d7%a5%c8%c0%b8%c0%ae ``` The images thus generated, strangely enough, look like MidJourney or Nijijourney illustrations. Perhaps they are passing user prompts through GPT or something else before passing them on to the image AI🤔 <img src="https://github.com/WarriorMama777/imgup/raw/main/img/AbyssOrangeMix2/SampleGallerBoardDesign_AbyssOrangeMix2_ReadMore_comp001.webp" width="" height="" alt=”SampleGallerBoardDesign_AbyssOrangeMix2_03_comp001”> <details> <summary>▼READ MORE🖼</summary> <img src="https://github.com/WarriorMama777/imgup/raw/main/img/AbyssOrangeMix2/SampleGallerBoardDesign_AbyssOrangeMix2_03_comp001.webp" width="" height="" alt=”SampleGallerBoardDesign_AbyssOrangeMix2_03_comp001”> ▼All prompts to generate sample images 1. [Gaming Girl](https://majinai.art/ja/i/GbTbLyk) 2. [Fantasy](https://majinai.art/ja/i/ax45Pof) 3. [Rainy Day](https://majinai.art/ja/i/1P9DUul) 4. [Kemomimi Girl](https://majinai.art/ja/i/hrUSb31) 5. [Supermarket](https://majinai.art/ja/i/6Mf4bVK) 6. [Lunch Time](https://majinai.art/ja/i/YAgQ4On) 7. [Womens in the Garden](https://majinai.art/ja/i/oHZYum_) 8. [Pirate](https://majinai.art/ja/i/yEA3EZk) 9. [Japanese Girl](https://majinai.art/ja/i/x4G_B_e) 10. [Sweets Time](https://majinai.art/ja/i/vK_mkac) 11. [Glasses Girl](https://majinai.art/ja/i/Z87IHOC) </details> ▼How to use - VAE: orangemix.vae.pt - ~~Prompts can be long or short~~ As simple as possible is good. Do not add excessive detail prompts. Start with just this negative propmt. (worst quality, low quality:1.4) - Sampler: “DPM++ SDE Karras” is good - Steps: forTest: 12～ ,illustration: 20～ - Clipskip: 1 or 2 - Upscaler : Latenet (nearest-exact) - CFG Scale : 5 or 6 (4～8) - Denoise strength: 0.5 (0.45~0.6) If you use 0.7～, the picture will change too much. If below 0.45, Block noise occurs. 🗒Model List - AbyssOrangeMix2_sfw｜BasilMix U-Net Blocks Weight Merge - AbyssOrangeMix2_nsfw｜+ NAI-NAISFW 0.3 Merge - AbyssOrangeMix2_hard｜+ Gape 0.3 Merge ※Changed suffix of models. _base →_sfw: _base was changed to_sfw. _night →_nsfw: Merged models up to NAI-NAI SFW were changed from _night to_nsfw. _half and non suffix →_hard: Gape merged models were given the suffix _hard.gape was reduced to 0.3 because it affects character modeling. ▼How to choice models - _sfw : SFW😉 - _nsfw : SFW ～ Soft NSFW🥰 - _hard : SFW ～ hard NSFW👄 ▼Hash - AbyssOrangeMix2_sfw.ckpt 「f75b19923f2a4a0e70f564476178eedd94e76e2c94f8fd8f80c548742b5b51b9」 - AbyssOrangeMix2_sfw.safetensors 「038ba203d8ba3c8af24f14e01fbb870c85bbb8d4b6d9520804828f4193d12ce9」 - AbyssOrangeMix2_nsfw.safetensors 「0873291ac5419eaa7a18726e8841ce0f15f701ace29e0183c47efad2018900a4」 - AbyssOrangeMix_hard.safetensors 「0fc198c4908e98d7aae2a76bd78fa004e9c21cb0be7582e36008b4941169f18e」 ▼Use Models 1. AnythingV3.0 huggingface pruned [2700c435]「543bcbc21294831c6245cd74c8a7707761e28812c690f946cb81fef930d54b5e」 1. NovelAI animefull-final-pruned [925997e9]「89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8」 1. NovelAI sfw [1d4a34af]「22fa233c2dfd7748d534be603345cb9abf994a23244dfdfc1013f4f90322feca」 1. Gape60 [25396b85]「893cca5903ccd0519876f58f4bc188dd8fcc5beb8a69c1a3f1a5fe314bb573f5」 1. BasilMix 「bbf07e3a1c3482c138d096f7dcdb4581a2aa573b74a68ba0906c7b657942f1c2」 ### AbyssOrangeMix2_sfw (AOM2s) ▼**Instructions:** STEP: 1｜Block Merge | Model: A | Model: B | Weight | Base alpha | Merge Name | | ------------ | -------- | --------------------------------------------------------------------- | ---------- | ------------------- | | AnythingV3.0 | BasilMix | 1,0.9,0.7,0.5,0.3,0.1,1,1,1,1,1,1,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1 | 0 | AbyssOrangeMix2_sfw | ### AbyssOrangeMix2_nsfw (AOM2n) ▼? JUST AbyssOrangeMix2_sfw+ (NAI-NAISFW) 0.3. ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------- | ----------------- | -------------- | -------------------- | | 1 | Add Difference @ 0.3 | AbyssOrangeMix_base | NovelAI animefull | NovelAI sfw | AbyssOrangeMix2_nsfw | ### AbyssOrangeMix2_hard (AOM2h) ▼? +Gape0.3 version AbyssOrangeMix2_nsfw. ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | --------------- | ----------------- | -------------------- | | 1 | Add Difference @ 0.3 | AbyssOrangeMix2_nsfw | Gape60 | NovelAI animefull | AbyssOrangeMix2_hard | ---- ## EerieOrangeMix (EOM) EerieOrangeMix is the generic name for a U-Net Blocks Weight Merge Models based on Elysium(Anime V2). Since there are infinite possibilities for U-Net Blocks Weight Merging, I plan to treat all Elysium-based models as a lineage of this model. ※This does not fundamentally improve the fingers. Therefore, More research needs to be done to improve the fingers (e.g. '[bad_prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt)'). <img src="https://files.catbox.moe/yjnqna.webp" width="1000" height="" alt=”HeroImage_EerieOrangeMix_Designed_comp001” > &nbsp; ### EerieOrangeMix (EOM1) ▼? This merge model is simply a U-Net Blocks Weight Merge of ElysiumAnime V2 with the AbyssOrangeMix method. The AnythingModel is good at cute girls anyway, and no matter how hard I try, it doesn't seem to be good at women in their late 20s and beyond. Therefore, I created a U-Net Blocks Weight Merge model based on my personal favorite ElysiumAnime V2 model. ElyOrangeMix was originally my favorite, so this is an enhanced version of that. 🗒Model List - EerieOrangeMix_base｜Instagram+F222 U-Net Blocks Weight Merge - EerieOrangeMix_night｜+ NAI-NAISFW Merge - EerieOrangeMix_half｜+ Gape0.5 Merge - EerieOrangeMix｜+ Gape1.0 Merge ▼ How to choice models - _base : SFW😉 - _Night : SFW ～ Soft NSFW🥰 - _half : SFW ～ NSFW👄 - unlabeled : SFW ～ HARDCORE ～🤯 ex)AbyssOrangeMix, BloodOrangeMix...etc ▼Hash - EerieOrangeMix.safetensors - EerieOrangeMix_half.safetensors - EerieOrangeMix_night.safetensors - EerieOrangeMix_base.ckpt ▼Use Models [] = WebUI Hash,「」= SHA256 1. Elysium Anime V2 []「5c4787ce1386500ee05dbb9d27c17273c7a78493535f2603321f40f6e0796851」 2. NovelAI animefull-final-pruned [925997e9]「89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8」 3. NovelAI sfw [1d4a34af]「22fa233c2dfd7748d534be603345cb9abf994a23244dfdfc1013f4f90322feca」 4. Gape60 [25396b85]「893cca5903ccd0519876f58f4bc188dd8fcc5beb8a69c1a3f1a5fe314bb573f5」 5. instagram-latest-plus-clip-v6e1_50000.safetensors [] 「8f1d325b194570754c6bd06cf1e90aa9219a7e732eb3d488fb52157e9451a2a5」 6. f222 [] 「9e2c6ceff3f6d6f65c6fb0e10d8e69d772871813be647fd2ea5d06e00db33c1f」 7. sd1.5_pruned [] 「e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053」 ▼ Sample Gallery <img src="https://files.catbox.moe/oqbvti.webp" width="1000" height="" alt=”2022-12-30_MotorbikeGIrlAsa3_comp001”> <details> <summary>More🖼</summary> <img src="https://files.catbox.moe/nmmswd.webp" width="" height="600" alt=”2022-12-30_SampleGallery5”> </details> ▼ How to use - VAE: orangemix.vae.pt - As simple as possible is good. Do not add excessive detail prompts. Start with just this. (worst quality, low quality:1.4) - Sampler: “DPM++ SDE Karras” is good - Steps: forTest: 20～24 ,illustration: 24～50 - Clipskip: 1 - USE “upscale latent space” - Denoise strength: 0.45 (0.4~0.5) If you use 0.7～, the picture will change too much. ▼Prompts 🖌When generating cute girls, try this negative prompt first. It avoids low quality, prevents blurring, avoids dull colors, and dictates Anime-like cute face modeling. ```jsx nsfw, (worst quality, low quality:1.3), (depth of field, blurry:1.2), (greyscale, monochrome:1.1), 3D face, nose, cropped, lowres, text, jpeg artifacts, signature, watermark, username, blurry, artist name, trademark, watermark, title, (tan, muscular, loli, petite, child, infant, toddlers, chibi, sd character:1.1), multiple view, Reference sheet, ``` --- #### EerieOrangeMix_base (EOM1b) ▼? Details are omitted since it is the same as AbyssOrangeMix. ▼**Instructions:** STEP: 1｜Creation of photorealistic model for Merge | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------------------------- | --------------- | -------------- | ---------- | | 1 | Add Difference @ 1.0 | instagram-latest-plus-clip-v6e1_50000 | f222 | sd1.5_pruned | Insta_F222 | STEP: 2｜Block Merge Merge InstaF222 | Model: A | Model: B | Weight | Base alpha | Merge Name | | ---------------- | ---------- | --------------------------------------------------------------------- | ---------- | ---------- | | Elysium Anime V2 | Insta_F222 | 1,0.9,0.7,0.5,0.3,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1 | 0 | Temp1 | #### EerieOrangeMix_Night (EOM1n) ▼? JUST EerieOrangeMix_base+ (NAI-NAISFW) 0.3. ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------- | ----------------- | -------------- | -------------------- | | 1 | Add Difference @ 0.3 | EerieOrangeMix_base | NovelAI animefull | NovelAI sfw | EerieOrangeMix_Night | #### EerieOrangeMix_half (EOM1h) ▼? +Gape0.5 version EerieOrangeMix. ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | ----------------- | -------------- | ------------------- | | 1 | Add Difference @ 0.5 | EerieOrangeMix_Night | NovelAI animefull | NovelAI sfw | EerieOrangeMix_half | #### EerieOrangeMix (EOM1) ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | --------------- | ----------------- | -------------- | | 1 | Add Difference @ 1.0 | EerieOrangeMix_Night | Gape60 | NovelAI animefull | EerieOrangeMix | ---- ### EerieOrangeMix2 (EOM2) ▼? The model was created by adding the hierarchy responsible for detailing and painting ElysiumV1 to EerieOrangeMix_base, then merging NAI and Gape. 🗒Model List - EerieOrangeMix2_base｜Instagram+F222+ElysiumV1 U-Net Blocks Weight Merge - EerieOrangeMix2_night｜+ NAI-NAISFW Merge - EerieOrangeMix2_half｜+ Gape0.5 Merge - EerieOrangeMix2｜+ Gape1.0 Merge ▼ How to choice models - _base : SFW😉 - _Night : SFW ～ Soft NSFW🥰 - _half : SFW ～ NSFW👄 - unlabeled : SFW ～ HARDCORE ～🤯 ex)AbyssOrangeMix, BloodOrangeMix...etc ▼Hash - EerieOrangeMix2.safetensors - EerieOrangeMix2_half.safetensors - EerieOrangeMix2_night.safetensors - EerieOrangeMix2_base.ckpt ▼Use Models [] = webuHash,「」= SHA256 1. Elysium Anime V2 []「5c4787ce1386500ee05dbb9d27c17273c7a78493535f2603321f40f6e0796851」 2. NovelAI animefull-final-pruned [925997e9]「89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8」 3. NovelAI sfw [1d4a34af]「22fa233c2dfd7748d534be603345cb9abf994a23244dfdfc1013f4f90322feca」 4. Gape60 [25396b85]「893cca5903ccd0519876f58f4bc188dd8fcc5beb8a69c1a3f1a5fe314bb573f5」 5. instagram-latest-plus-clip-v6e1_50000.safetensors [] 「8f1d325b194570754c6bd06cf1e90aa9219a7e732eb3d488fb52157e9451a2a5」 6. f222 [] 「9e2c6ceff3f6d6f65c6fb0e10d8e69d772871813be647fd2ea5d06e00db33c1f」 7. sd1.5_pruned [] 「e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053」 8. ElysiumV1 「abbb28cb5e70d3e0a635f241b8d61cefe42eb8f1be91fd1168bc3e52b0f09ae4」 #### EerieOrangeMix2_base (EOM2b) ▼? ▼Instructions STEP: 1｜Block Merge Merge ElysiumV1 The generated results do not change much with or without this process, but I wanted to incorporate Elysium's depiction, so I merged it. | Model: A | Model: B | Weight | Base alpha | Merge Name | | ------------------- | --------- | --------------------------------------------------------------------- | ---------- | -------------------- | | EerieOrangeMix_base | ElysiumV1 | 1,0.9,0.7,0.5,0.3,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1 | 0 | EerieOrangeMix2_base | #### EerieOrangeMix2_night (EOM2n) ▼? JUST EerieOrangeMix2_base+ (NAI-NAISFW) 0.3. ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------- | ----------------- | -------------- | --------------------- | | 1 | Add Difference @ 0.3 | EerieOrangeMix_base | NovelAI animefull | NovelAI sfw | EerieOrangeMix2_Night | #### EerieOrangeMix2_half (EOM2h) ▼? +Gape0.5 version EerieOrangeMix2. ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | ----------------- | -------------- | -------------------- | | 1 | Add Difference @ 0.5 | EerieOrangeMix_Night | NovelAI animefull | NovelAI sfw | EerieOrangeMix2_half | #### EerieOrangeMix2 (EOM2) ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | --------------- | ----------------- | --------------- | | 1 | Add Difference @ 1.0 | EerieOrangeMix_Night | Gape60 | NovelAI animefull | EerieOrangeMix2 | ### Models Comparison <img src="https://files.catbox.moe/mp2fr4.webp" width="1000" height="" alt="MotorbikeGIrlAsa_Eerie_Abyss_Comparison_comp001"> <img src="https://files.catbox.moe/9xqths.webp" width="1000" height="" alt=”Eerie_Abyss_Comparison_02_comp001”> <img src="https://files.catbox.moe/cm6c7m.webp" width="1000" height="" alt=”Eerie_Comparison_01_comp001”> ※The difference is slight but probably looks like this. ← warm color, ↑ natural color, → animated color ---- ## AbyssOrangeMix (AOM) ――How can you guys take on such a deep swamp and get results? Is it something like "Made in Abyss"? By Anon, 115th thread <img src="https://files.catbox.moe/wst1bp.webp" width="1000" height=""> ▼? The merged model was formulated using an extension such as sdweb-merge-block-weighted-gui, which merges models at separate rates for each of the 25 U-Net blocks (input, intermediate, and output). The validation of many Anons has shown that such a recipe can generate a painting style that is anatomically realistic enough to feel the finger skeleton, but still maintains an anime-style face. ※This model is the result of a great deal of testing and experimentation by many Anons🤗 ※This model can be very difficult to handle. I am not 100% confident in my ability to use this model. It is peaky and for experts. ※This does not fundamentally improve the fingers, and I recommend using bad_prompt, etc. (Embedding) in combination. ▼Sample Gallery (1) <img src="https://files.catbox.moe/8mke0t.webp" width="1000" height=""> ```jsx ((masterpiece)), best quality, perfect anatomy, (1girl, solo focus:1.4), pov, looking at viewer, flower trim,(perspective, sideway, From directly above ,lying on water, open hand, palm, :1.3),(Accurate five-fingered hands, Reach out, hand focus, foot focus, Sole, heel, ball of the thumb:1.2), (outdoor, sunlight:1.2),(shiny skin:1.3),,(masterpiece, white border, outside border, frame:1.3), , (motherhood, aged up, mature female, medium breasts:1.2), (curvy:1.1), (single side braid:1.2), (long hair with queue and braid, disheveled hair, hair scrunchie, tareme:1.2), (light Ivory hair:1.2), looking at viewer,, Calm, Slight smile, ,(anemic, dark, lake, river,puddle, Meadow, rock, stone, moss, cliff, white flower, stalactite, Godray, ruins, ancient, eternal, deep ,mystic background,sunlight,plant,lily,white flowers, Abyss, :1.2), (orange fruits, citrus fruit, citrus fruit bearing tree:1.4), volumetric lighting,good lighting,, masterpiece, best quality, highly detailed,extremely detailed cg unity 8k wallpaper,illustration,((beautiful detailed face)), best quality, (((hyper-detailed ))), high resolution illustration ,high quality, highres, sidelighting, ((illustrationbest)),highres,illustration, absurdres, hyper-detailed, intricate detail, perfect, high detailed eyes,perfect lighting, (extremely detailed CG:1.2), Negative prompt: (bad_prompt_version2:1), distant view, lip, Pregnant, maternity, pointy ears, realistic, tan, muscular, greyscale, monochrome, lineart, 2koma, 3koma, 4koma, manga, 3D, 3Dcubism, pablo picasso, disney, marvel, mutanted breasts, mutanted nipple, cropped, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, lowres, trademark, watermark, title, text, deformed, bad anatomy, disfigured, mutated, extra limbs, ugly, missing limb, floating limbs, disconnected limbs, out of frame, mutated hands and fingers, poorly drawn hands, malformed hands, poorly drawn face, poorly drawn asymmetrical eyes, (blurry:1.4), duplicate (loli, petite, child, infant, toddlers, chibi, sd character, teen age:1.4), tsurime, helmet hair, evil smile, smug_face, naughty smile, multiple view, Reference sheet, (worst quality, low quality:1.4), Steps: 24, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 1159970659, Size: 1536x768, Model hash: cc44dbff, Model: AbyssOrangeMix, Variation seed: 93902374, Variation seed strength: 0.45, Denoising strength: 0.45, ENSD: 31337 ``` (2) <img src="https://files.catbox.moe/6cbrqh.webp" width="" height="600"> ```jsx street, 130mm f1.4 lens, ,(shiny skin:1.3),, (teen age, school uniform:1.2), (glasses, black hair, medium hair with queue and braid, disheveled hair, hair scrunchie, tareme:1.2), looking at viewer,, Calm, Slight smile, Negative prompt: (bad_prompt_version2:1), distant view, lip, Pregnant, maternity, pointy ears, realistic, tan, muscular, greyscale, monochrome, lineart, 2koma, 3koma, 4koma, manga, 3D, 3Dcubism, pablo picasso, disney, marvel, mutanted breasts, mutanted nipple, cropped, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, lowres, trademark, watermark, title, text, deformed, bad anatomy, disfigured, mutated, extra limbs, ugly, missing limb, floating limbs, disconnected limbs, out of frame, mutated hands and fingers, poorly drawn hands, malformed hands, poorly drawn face, poorly drawn asymmetrical eyes, (blurry:1.4), duplicate (loli, petite, child, infant, toddlers, chibi, sd character, teen age:1.4), tsurime, helmet hair, evil smile, smug_face, naughty smile, multiple view, Reference sheet, (worst quality, low quality:1.4), Steps: 24, Sampler: DPM++ SDE Karras, CFG scale: 10, Seed: 1140782193, Size: 1024x1536, Model hash: cc44dbff, Model: AbyssOrangeMix, Denoising strength: 0.45, ENSD: 31337, First pass size: 512x768, Model sha256: 6bb3a5a3b1eadd32, VAE sha256: f921fb3f29891d2a, Options: xformers medvram gtx_16x0 Used embeddings: bad_prompt_version2 [afea] ``` ---- ▼How to use - VAE: orangemix.vae.pt - ~~Prompts can be long or short~~ As simple as possible is good. Do not add excessive detail prompts. Start with just this. (worst quality, low quality:1.4) - Sampler: “DPM++ SDE Karras” is good - Steps: forTest: 20～24 ,illustration: 24～50 - Clipskip: 1 - USE “upscale latent space” - Denoise strength: 0.45 (0.4~0.5) If you use 0.7～, the picture will change too much. ▼Prompts 🖌When generating cute girls, try this negative prompt first. It avoids low quality, prevents blurring, avoids dull colors, and dictates Anime-like cute face modeling. ```jsx nsfw, (worst quality, low quality:1.3), (depth of field, blurry:1.2), (greyscale, monochrome:1.1), 3D face, nose, cropped, lowres, text, jpeg artifacts, signature, watermark, username, blurry, artist name, trademark, watermark, title, (tan, muscular, loli, petite, child, infant, toddlers, chibi, sd character:1.1), multiple view, Reference sheet, ``` 🗒Model List - AbyssOrangeMix_base｜Instagram Merge - AbyssOrangeMix_Night｜+ NAI-NAISFW Merge - AbyssOrangeMix_half｜+ Gape0.5 Merge - AbyssOrangeMix｜+ Gape1.0 Merge ▼ How to choice models - _base : SFW😉 - _Night : SFW ～ Soft NSFW🥰 - _half : SFW ～ NSFW👄 - unlabeled : SFW ～ HARDCORE ～🤯 ex)AbyssOrangeMix, BloodOrangeMix...etc ▼Hash (SHA256) - AbyssOrangeMix.safetensors 6bb3a5a3b1eadd32dfbc8f0987559c48cb4177aee7582baa6d6a25181929b345 - AbyssOrangeMix_half.safetensors 468d1b5038c4fbd354113842e606fe0557b4e0e16cbaca67706b29bcf51dc402 - AbyssOrangeMix_Night.safetensors 167cd104699dd98df22f4dfd3c7a2c7171df550852181e454e71e5bff61d56a6 - AbyssOrangeMix_base.ckpt bbd2621f3ec4fad707f75fc032a2c2602c296180a53ed3d9897d8ca7a01dd6ed ▼Use Models 1. AnythingV3.0 huggingface pruned [2700c435]「543bcbc21294831c6245cd74c8a7707761e28812c690f946cb81fef930d54b5e」 1. NovelAI animefull-final-pruned [925997e9]「89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8」 1. NovelAI sfw [1d4a34af]「22fa233c2dfd7748d534be603345cb9abf994a23244dfdfc1013f4f90322feca」 1. Gape60 [25396b85]「893cca5903ccd0519876f58f4bc188dd8fcc5beb8a69c1a3f1a5fe314bb573f5」 1. instagram-latest-plus-clip-v6e1_50000.safetensors [] 「8f1d325b194570754c6bd06cf1e90aa9219a7e732eb3d488fb52157e9451a2a5」 1. f222 [] 「9e2c6ceff3f6d6f65c6fb0e10d8e69d772871813be647fd2ea5d06e00db33c1f」 1. sd1.5_pruned [] 「e1441589a6f3c5a53f5f54d0975a18a7feb7cdf0b0dee276dfc3331ae376a053」 ### AbyssOrangeMix_base (AOMb) ▼? The basic trick for this merged model is to incorporate a model that has learned more than 1m Instagram photos (mostly Japanese) or a photorealistic model like f222. The choice of base model here depends on the person. I chose AnythingV3 for versatility. ▼**Instructions:** STEP: 1｜Creation of photorealistic model for Merge | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------------------------- | --------------- | -------------- | ---------- | | 1 | Add Difference @ 1.0 | instagram-latest-plus-clip-v6e1_50000 | f222 | sd1.5_pruned | Insta_F222 | STEP: 2｜Block Merge | Model: A | Model: B | Weight | Base alpha | Merge Name | | ------------ | ---------- | --------------------------------------------------------------------- | ---------- | ------------------- | | AnythingV3.0 | Insta_F222 | 1,0.9,0.7,0.5,0.3,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1 | 0 | AbyssOrangeMix_base | ### AbyssOrangeMix_Night (AOMn) ▼? JUST AbyssOrangeMix_base+ (NAI-NAISFW) 0.3. ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------------- | ----------------- | -------------- | -------------------- | | 1 | Add Difference @ 0.3 | AbyssOrangeMix_base | NovelAI animefull | NovelAI sfw | AbyssOrangeMix_Night | ### AbyssOrangeMix_half (AOMh) ▼? +Gape0.5 version AbyssOrangeMix. ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | --------------- | ----------------- | ------------------- | | 1 | Add Difference @ 0.5 | AbyssOrangeMix_Night | Gape60 | NovelAI animefull | AbyssOrangeMix_half | ### AbyssOrangeMix (AOM) ▼**Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | -------------------- | --------------- | ----------------- | -------------- | | 1 | Add Difference @ 1.0 | AbyssOrangeMix_Night | Gape60 | NovelAI animefull | AbyssOrangeMix | ---- ## ElyOrangeMix (ELOM) <img src="https://i.imgur.com/AInEXA5.jpg" width="1000" height=""> ▼? Elysium_Anime_V2 + NAI + Gape. This is a merge model that improves on the Elysium_Anime_V2, where NSFW representation is not good. It can produce SFW, NSFW, and any other type of artwork, while retaining the Elysium's three-dimensional, thickly painted style. ▼ How to choice models - _base : SFW😉 - _Night : SFW ～ Soft NSFW🥰 - _half : SFW ～ NSFW👄 - unlabeled : SFW ～ HARDCORE ～🤯 ex)AbyssOrangeMix, BloodOrangeMix...etc ▼How to use - VAE: orangemix.vae.pt ▼Hash (SHA256) - ElyOrangeMix [6b508e59] - ElyOrangeMix_half [6b508e59] - ElyNightOrangeMix[6b508e59] ### ElyOrangeMix (ELOM) ▼Use Models 1. Elysium_Anime_V2 [6b508e59] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] 4. Gape60 [25396b85] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ---------------- | ----------------- | ----------------- | ------------------------ | | 1 | Add Difference @ 0.3 | Elysium_Anime_V2 | NovelAI animefull | NovelAI sfw | tempmix-part1 [] | | 2 | Add Difference @ 1.0 | tempmix-part1 | Gape60 | NovelAI animefull | ElyOrangeMix [6b508e59] | --- ### ElyOrangeMix_half (ELOMh) ▼? +Gape0.5 version ElyOrangeMix. ▼Use Models 1. Elysium_Anime_V2 [6b508e59] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] 4. Gape60 [25396b85] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ---------------- | ----------------- | ----------------- | ----------------------------- | | 1 | Add Difference @ 0.3 | Elysium_Anime_V2 | NovelAI animefull | NovelAI sfw | tempmix-part1 [] | | 2 | Add Difference @ 0.5 | tempmix-part1 | Gape60 | NovelAI animefull | ElyOrangeMix_half [6b508e59] | ---- ### ElyNightOrangeMix (ELOMn) ▼? It is a merged model that just did Elysium_Anime_V2+ (NAI-NAISFW) 0.3. ▼Use Models 1. Elysium_Anime_V2 [6b508e59] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ---------------- | ----------------- | -------------- | ----------------- | | 1 | Add Difference @ 0.3 | Elysium_Anime_V2 | NovelAI animefull | NovelAI sfw | ElyNightOrangeMix | ---- ## BloodOrangeMix (BOM) <img src="https://i.imgur.com/soAnnFk.jpg" width="1000" height=""> ▼? Anything+NAI+Gape. This is a merge model that improves on the AnythingV3, where NSFW representation is not good. It can produce SFW, NSFW, and any other type of artwork, while retaining the flat, beautifully painted style of AnythingV3. Stable. Popular in the Japanese community. ▼ModelList & [] = WebUI Hash,「」= SHA256 - BloodNightOrangeMix.ckpt [ffa7b160]「f8aff727ba3da0358815b1766ed232fd1ef9682ad165067cac76e576d19689e0」 - BloodOrangeMix_half.ckpt [ffa7b160]「b2168aaa59fa91229b8add21f140ac9271773fe88a387276f3f0c7d70f726a83」 - BloodOrangeMix.ckpt [ffa7b160] 「25cece3fe303ea8e3ad40c3dca788406dbd921bcf3aa8e3d1c7c5ac81f208a4f」 - BloodOrangeMix.safetensors 「79a1edf6af43c75ee1e00a884a09213a28ee743b2e913de978cb1f6faa1b320d」 ▼ How to choice models - _base : SFW😉 - _Night : SFW ～ Soft NSFW🥰 - _half : SFW ～ NSFW👄 - unlabeled : SFW ～ HARDCORE ～🤯 ex)AbyssOrangeMix, BloodOrangeMix...etc ▼How to use - VAE: orangemix.vae.pt ### BloodOrangeMix (BOM) ▼Use Models 1. AnythingV3.0 huggingface pruned [2700c435] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] 4. Gape60 [25396b85] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------- | ----------------- | ----------------- | ------------------------- | | 1 | Add Difference @ 0.3 | AnythingV3.0 | NovelAI animefull | NovelAI sfw | tempmix-part1 [] | | 2 | Add Difference @ 1.0 | tempmix-part1 | Gape60 | NovelAI animefull | BloodOrangeMix [ffa7b160] | ---- ### BloodOrangeMix_half (BOMh) ▼? Anything+Nai+Gape0.5 +Gape0.5 version BloodOrangeMix. NSFW expression will be softer and have less impact on the Anything style painting style. ▼Use Models 1. AnythingV3.0 huggingface pruned [2700c435] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] 4. Gape60 [25396b85] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------- | ----------------- | ----------------- | ------------------------------ | | 1 | Add Difference @ 0.3 | AnythingV3.0 | NovelAI animefull | NovelAI sfw | tempmix-part1 [] | | 2 | Add Difference @ 0.5 | tempmix-part1 | Gape60 | NovelAI animefull | BloodOrangeMix_half [ffa7b160] | ---- ### BloodNightOrangeMix (BOMn) ▼? It is a merged model that just did AnythingV3+ (NAI-NAISFW) 0.3. ▼Use Models 1. AnythingV3.0 huggingface pruned [2700c435] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] ▼Instructions | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ------------- | ----------------- | -------------- | ------------------- | | 1 | Add Difference @ 0.3 | AnythingV3.0 | NovelAI animefull | NovelAI sfw | BloodNightOrangeMix | ---- ## ElderOrangeMix ※I found this model to be very prone to body collapse. Not recommended. ▼? anything and everything mix ver.1.5+Gape+Nai(AnEve.G.N0.3) This is a merged model with improved NSFW representation of anything and everything mix ver.1.5. ▼Hash [3a46a1e0] ▼Use Models 1. anything and everything mix ver.1.5 [5265dcf6] 2. NovelAI animefull-final-pruned [925997e9] 3. NovelAI sfw [1d4a34af] 4. Gape60 [25396b85] ▼Instructions:** | Step | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | ---- | -------------------- | ----------------------------------- | --------------- | -------------- | -------------------------- | | 1 | Add Difference @ 0.5 | anything and everything mix ver.1.5 | Gape60 | NovelAI full | tempmix-part1 [] | | 2 | Add Difference @ 0.3 | tempmix-part1 | NovelAI full | NovelAI sfw | ElderOrangeMix [3a46a1e0] | ---- ## Troubleshooting 1. blurred Images & clearly low quality output If the generated images are blurred or only clearly low quality output is produced, it is possible that the vae, etc. are not loaded properly. Try reloading the model/vae or restarting the WebUI/OS. ## FAQ and Tips (🐈MEME ZONE🦐) Trash zone. ---- <a name="MEME_AOM3A1"></a> ▼AOM3A1? R.I.P.  <a name="MEME_realface"></a> ▼No, AOM2 (only hentai models)  ▼Nooo^()&*%#NG0u!!!!!!!!縺ゅ♀繧?縺医?縺､繝ｼ縺ｨ縺医?縺吶ｊ繝ｼ縺ｯ驕主ｭｦ鄙偵?繧ｴ繝溘〒縺? (「AOM3A2 and A3 are overlearning and Trash. delete!」) <img src="https://github.com/WarriorMama777/imgup/raw/main/img/img_general/img_meme_tension_comp001.webp" width="300" height="" alt=”getting_excited”> ▼Noo, Too many models. Tell me which one to choose. → [全部同じじゃないですか](https://github.com/WarriorMama777/imgup/blob/main/img/img_general/img_MEME_whichModel_comp001.webp?raw=true "全部同じじゃないですか") ▼Nooo, not work. This guy is Scammer STEP1: BUY HUGE PC ▼Noooo, can't generate image like samples.This models is hype. ❌ <img src="https://files.catbox.moe/nte6ud.webp" width="500" height=""> 🟢 <img src="https://files.catbox.moe/lta462.webp" width="500" height=""> ▼Nooooo, This models have troy virus. don't download. All models in this repository are secure. It is most likely that anti-virus software has detected them erroneously. However, the models with the .ckpt extension have the potential danger of executing arbitrary code. A safe model that is free from these dangers is the model with the .safetensors extension.

bert-base-german-dbmdz-uncased

transformers

fill-mask

--- language: de license: mit --- This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.

microsoft/infoxlm-large

microsoft

transformers

fill-mask

# InfoXLM **InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training. **MD5** ``` 05b95b7d977450b364f8ea3269391953 config.json c19438359fed6d36b0c1bbb107929579 pytorch_model.bin bf25eb5120ad92ef5c7d8596b5dc4046 sentencepiece.bpe.model eedbd60a7268b9fc45981b849664f747 tokenizer.json ``` **BibTeX** ``` @inproceedings{chi-etal-2021-infoxlm, title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training", author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming}, booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.280", doi = "10.18653/v1/2021.naacl-main.280", pages = "3576--3588",} ```

facebook/wav2vec2-large-robust-ft-swbd-300h

facebook

transformers

automatic-speech-recognition

--- language: en datasets: - libri_light - common_voice - switchboard - fisher tags: - speech - audio - automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac license: apache-2.0 --- # Wav2Vec2-Large-Robust finetuned on Switchboard [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/). This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model. It has been pretrained on: - [Libri-Light](https://github.com/facebookresearch/libri-light): open-source audio books from the LibriVox project; clean, read-out audio data - [CommonVoice](https://huggingface.co/datasets/common_voice): crowd-source collected audio data; read-out text snippets - [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62): telephone speech corpus; noisy telephone data - [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19): conversational telephone speech; noisy telephone data and subsequently been finetuned on 300 hours of - [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62): telephone speech corpus; noisy telephone data When using the model make sure that your speech input is also sampled at 16Khz. [Paper Robust Wav2Vec2](https://arxiv.org/abs/2104.01027) Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli **Abstract** Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at this https URL. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ```

mrm8488/t5-base-finetuned-span-sentiment-extraction

mrm8488

transformers

text2text-generation

--- language: en tags: - sentiment - extracion - passage widget: - text: "question: positive context: On the monday, so i wont be able to be with you! i love you" --- # T5-base fine-tuned for Sentiment Span Extraction All credits to [Lorenzo Ampil](https://twitter.com/AND__SO) [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [Tweet Sentiment Extraction Dataset](https://www.kaggle.com/c/tweet-sentiment-extraction) for **Span Sentiment Extraction** downstream task. ## Details of T5 The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract: Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ## Details of the downstream task (Span Sentiment Extraction) - Dataset 📚 [Tweet Sentiment Extraction Dataset](https://www.kaggle.com/c/tweet-sentiment-extraction) "My ridiculous dog is amazing." [sentiment: positive] With all of the tweets circulating every second it is hard to tell whether the sentiment behind a specific tweet will impact a company, or a person's, brand for being viral (positive), or devastate profit because it strikes a negative tone. Capturing sentiment in language is important in these times where decisions and reactions are created and updated in seconds. But, which words actually lead to the sentiment description? In this competition you will need to pick out the part of the tweet (word or phrase) that reflects the sentiment. Help build your skills in this important area with this broad dataset of tweets. Work on your technique to grab a top spot in this competition. What words in tweets support a positive, negative, or neutral sentiment? How can you help make that determination using machine learning tools? In this competition we've extracted support phrases from Figure Eight's Data for Everyone platform. The dataset is titled Sentiment Analysis: Emotion in Text tweets with existing sentiment labels, used here under creative commons attribution 4.0. international licence. Your objective in this competition is to construct a model that can do the same - look at the labeled sentiment for a given tweet and figure out what word or phrase best supports it. Disclaimer: The dataset for this competition contains text that may be considered profane, vulgar, or offensive. | Dataset | Split | # samples | | -------- | ----- | --------- | | TSE | train | 23907 | | TSE | eval | 3573 | ## Model fine-tuning 🏋️‍ The training script is a slightly modified version of [this Colab Notebook](https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb) created by [Lorenzo Ampil](https://github.com/enzoampil), so all credits to him! ## Model in Action 🚀 ```python from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-span-sentiment-extraction") model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-span-sentiment-extraction") def get_sentiment_span(text): input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True) # Batch size 1 generated_ids = model.generate(input_ids=input_ids, num_beams=1, max_length=80).squeeze() predicted_span = tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) return predicted_span get_sentiment_span("question: negative context: My bike was put on hold...should have known that.... argh total bummer") # output: 'argh total bummer' get_sentiment_span("question: positive context: On the monday, so i wont be able to be with you! i love you") # output: 'i love you' ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain

vicgalle/xlm-roberta-large-xnli-anli

vicgalle

transformers

zero-shot-classification

--- language: multilingual tags: - zero-shot-classification - nli - pytorch datasets: - mnli - xnli - anli license: mit pipeline_tag: zero-shot-classification widget: - text: "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus." candidate_labels: "violent, peaceful" - text: "La película empezaba bien pero terminó siendo un desastre." candidate_labels: "positivo, negativo, neutral" - text: "La película empezó siendo un desastre pero en general fue bien." candidate_labels: "positivo, negativo, neutral" - text: "¿A quién vas a votar en 2020?" candidate_labels: "Europa, elecciones, política, ciencia, deportes" --- ### XLM-RoBERTa-large-XNLI-ANLI XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification. Here are the accuracies for several test datasets: | | XNLI-es | XNLI-fr | ANLI-R1 | ANLI-R2 | ANLI-R3 | |-----------------------------|---------|---------|---------|---------|---------| | xlm-roberta-large-xnli-anli | 93.7% | 93.2% | 68.5% | 53.6% | 49.0% | The model can be loaded with the zero-shot-classification pipeline like so: ``` from transformers import pipeline classifier = pipeline("zero-shot-classification", model="vicgalle/xlm-roberta-large-xnli-anli") ``` You can then use this pipeline to classify sequences into any of the class names you specify: ``` sequence_to_classify = "Algún día iré a ver el mundo" candidate_labels = ['viaje', 'cocina', 'danza'] classifier(sequence_to_classify, candidate_labels) #{'sequence': 'Algún día iré a ver el mundo', #'labels': ['viaje', 'danza', 'cocina'], #'scores': [0.9991760849952698, 0.0004178212257102132, 0.0004059972707182169]} ```

intfloat/multilingual-e5-large

intfloat

sentence-transformers

feature-extraction

--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - type: recall_at_5 value: 67.71000000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.30960650674069 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.427074197498996 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.05942144105269 - type: cos_sim_spearman value: 82.51212105850809 - type: euclidean_pearson value: 81.95639829909122 - type: euclidean_spearman value: 82.3717564144213 - type: manhattan_pearson value: 81.79273425468256 - type: manhattan_spearman value: 82.20066817871039 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.46764091858039 - type: f1 value: 99.37717466945023 - type: precision value: 99.33194154488518 - type: recall value: 99.46764091858039 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.29407880255337 - type: f1 value: 98.11248073959938 - type: precision value: 98.02443319392472 - type: recall value: 98.29407880255337 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.79009352268791 - type: f1 value: 97.5176076665512 - type: precision value: 97.38136473848286 - type: recall value: 97.79009352268791 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.26276987888363 - type: f1 value: 99.20133403545726 - type: precision value: 99.17500438827453 - type: recall value: 99.26276987888363 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.72727272727273 - type: f1 value: 84.67672206031433 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.34220182511161 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - 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ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-large [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 24 layers and the embedding size is 1024. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-large') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

google/byt5-large

google

transformers

text2text-generation

--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- # ByT5 - large ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-large). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-large` significantly outperforms [mt5-large](https://huggingface.co/google/mt5-large) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-large') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-large') tokenizer = AutoTokenizer.from_pretrained('google/byt5-large') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. 

t5-3b

transformers

translation

--- language: - en - fr - ro - de - multilingual license: apache-2.0 tags: - summarization - translation datasets: - c4 --- # Model Card for T5-3B  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-3B is the checkpoint with 3 billion parameters. - **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - **Model type:** Language model - **Language(s) (NLP):** English, French, Romanian, German - **License:** Apache 2.0 - **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) - **Resources for more information:** - [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5) # Uses ## Direct Use and Downstream Use The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Recommendations More information needed. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr) 2. **Datasets used for Supervised text-to-text language modeling objective** - Sentence acceptability judgment - CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471) - Sentiment analysis - SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - Paraphrasing/sentence similarity - MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002) - STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055) - QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - Natural language inference - MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426) - QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250) - RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9) - CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf) - Sentence completion - COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning) - Word sense disambiguation - WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121) - Question answering - MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023) - ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885) - BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044) ## Training Procedure In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write: > In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details. ## Results For full results for T5-3B, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more context on how to get started with this checkpoint.

unitary/toxic-bert

unitary

transformers

text-classification

--- license: apache-2.0 --- <div align="center"> **⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify # 🙊 Detoxify ## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers   </div>  ## Description Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/), where we are working to stop harmful content online by interpreting visual content in context. Dependencies: - For inference: - 🤗 Transformers - ⚡ Pytorch lightning - For training will also need: - Kaggle API (to download data) | Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score | Detoxify Score |-|-|-|-|-|-|-| | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 | build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 0.98856 | 0.98636 | [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 0.94734 | 0.93639 | [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 0.9536 | 0.91655* *Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available. It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use. ## Limitations and ethical considerations If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups. The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics and/or to aid content moderators in flagging out harmful content quicker. Some useful resources about the risk of different biases in toxicity or hate speech detection are: - [The Risk of Racial Bias in Hate Speech Detection](https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf) - [Automated Hate Speech Detection and the Problem of Offensive Language](https://arxiv.org/pdf/1703.04009.pdf%201.pdf) - [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://arxiv.org/pdf/1905.12516.pdf) ## Quick prediction The `multilingual` model has been trained on 7 different languages so it should only be tested on: `english`, `french`, `spanish`, `italian`, `portuguese`, `turkish` or `russian`. ```bash # install detoxify pip install detoxify ``` ```python from detoxify import Detoxify # each model takes in either a string or a list of strings results = Detoxify('original').predict('example text') results = Detoxify('unbiased').predict(['example text 1','example text 2']) results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста']) # optional to display results nicely (will need to pip install pandas) import pandas as pd print(pd.DataFrame(results, index=input_text).round(5)) ``` For more details check the Prediction section. ## Labels All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema: - **Very Toxic** (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective) - **Toxic** (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective) - **Hard to Say** - **Not Toxic** More information about the labelling schema can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). ### Toxic Comment Classification Challenge This challenge includes the following labels: - `toxic` - `severe_toxic` - `obscene` - `threat` - `insult` - `identity_hate` ### Jigsaw Unintended Bias in Toxicity Classification This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments. Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation. - `toxicity` - `severe_toxicity` - `obscene` - `threat` - `insult` - `identity_attack` - `sexual_explicit` Identity labels used: - `male` - `female` - `homosexual_gay_or_lesbian` - `christian` - `jewish` - `muslim` - `black` - `white` - `psychiatric_or_mental_illness` A complete list of all the identity labels available can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). ### Jigsaw Multilingual Toxic Comment Classification Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on: - `toxicity` ## How to run First, install dependencies ```bash # clone project git clone https://github.com/unitaryai/detoxify # create virtual env python3 -m venv toxic-env source toxic-env/bin/activate # install project pip install -e detoxify cd detoxify # for training pip install -r requirements.txt ``` ## Prediction Trained models summary: |Model name| Transformer type| Data from |:--:|:--:|:--:| |`original`| `bert-base-uncased` | Toxic Comment Classification Challenge |`unbiased`| `roberta-base`| Unintended Bias in Toxicity Classification |`multilingual`| `xlm-roberta-base`| Multilingual Toxic Comment Classification For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments. ```bash # load model via torch.hub python run_prediction.py --input 'example' --model_name original # load model from from checkpoint path python run_prediction.py --input 'example' --from_ckpt_path model_path # save results to a .csv file python run_prediction.py --input test_set.txt --model_name original --save_to results.csv # to see usage python run_prediction.py --help ``` Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names: - `toxic_bert` - `unbiased_toxic_roberta` - `multilingual_toxic_xlm_r` ```bash model = torch.hub.load('unitaryai/detoxify','toxic_bert') ``` Importing detoxify in python: ```python from detoxify import Detoxify results = Detoxify('original').predict('some text') results = Detoxify('unbiased').predict(['example text 1','example text 2']) results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста']) # to display results nicely import pandas as pd print(pd.DataFrame(results,index=input_text).round(5)) ``` ## Training If you do not already have a Kaggle account: - you need to create one to be able to download the data - go to My Account and click on Create New API Token - this will download a kaggle.json file - make sure this file is located in ~/.kaggle ```bash # create data directory mkdir jigsaw_data cd jigsaw_data # download data kaggle competitions download -c jigsaw-toxic-comment-classification-challenge kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification ``` ## Start Training ### Toxic Comment Classification Challenge ```bash python create_val_set.py python train.py --config configs/Toxic_comment_classification_BERT.json ``` ### Unintended Bias in Toxicicity Challenge ```bash python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa.json ``` ### Multilingual Toxic Comment Classification This is trained in 2 stages. First, train on all available data, and second, train only on the translated versions of the first challenge. The [translated data](https://www.kaggle.com/miklgr500/jigsaw-train-multilingual-coments-google-api) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set). ```bash # stage 1 python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json # stage 2 python train.py --config configs/Multilingual_toxic_comment_classification_XLMR_stage2.json ``` ### Monitor progress with tensorboard ```bash tensorboard --logdir=./saved ``` ## Model Evaluation ### Toxic Comment Classification Challenge This challenge is evaluated on the mean AUC score of all the labels. ```bash python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv ``` ### Unintended Bias in Toxicicity Challenge This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation). ```bash python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv # to get the final bias metric python model_eval/compute_bias_metric.py ``` ### Multilingual Toxic Comment Classification This challenge is evaluated on the AUC score of the main toxic label. ```bash python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv ``` ### Citation ``` @misc{Detoxify, title={Detoxify}, author={Hanu, Laura and {Unitary team}}, howpublished={Github. https://github.com/unitaryai/detoxify}, year={2020} } ```

timm/swin_base_patch4_window7_224.ms_in22k_ft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_tag: timm license: mit datasets: - imagenet-1k - imagenet-22k --- # Model card for swin_base_patch4_window7_224.ms_in22k_ft_in1k A Swin Transformer image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 87.8 - GMACs: 15.5 - Activations (M): 36.6 - Image size: 224 x 224 - **Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g. for swin_base_patch4_window7_224 (NHWC output) # torch.Size([1, 56, 56, 128]) # torch.Size([1, 28, 28, 256]) # torch.Size([1, 14, 14, 512]) # torch.Size([1, 7, 7, 1024]) # e.g. for swinv2_cr_small_ns_224 (NCHW output) # torch.Size([1, 96, 56, 56]) # torch.Size([1, 192, 28, 28]) # torch.Size([1, 384, 14, 14]) # torch.Size([1, 768, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2 # or (batch_size, num_features, H, W) for swinv2_cr output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{liu2021Swin, title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

jhgan/ko-sroberta-multitask

jhgan

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: ko --- # ko-sroberta-multitask This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."] model = SentenceTransformer('jhgan/ko-sroberta-multitask') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jhgan/ko-sroberta-multitask') model = AutoModel.from_pretrained('jhgan/ko-sroberta-multitask') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> KorSTS, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다. - Cosine Pearson: 84.77 - Cosine Spearman: 85.60 - Euclidean Pearson: 83.71 - Euclidean Spearman: 84.40 - Manhattan Pearson: 83.70 - Manhattan Spearman: 84.38 - Dot Pearson: 82.42 - Dot Spearman: 82.33 ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8885 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 719 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 360, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information --> - Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv preprint arXiv:2004.03289 - Reimers, Nils and Iryna Gurevych. “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.” ArXiv abs/1908.10084 (2019) - Reimers, Nils and Iryna Gurevych. “Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.” EMNLP (2020).

laion/CLIP-ViT-L-14-laion2B-s32B-b82K

laion

open_clip

zero-shot-image-classification

--- license: mit widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-L/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training ('babysitting') done by Ross Wightman on the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure The model was trained on 384 A100 GPUs using 200M sample 'virtual' epochs where dataset shards were sampled with replacement. The model was trained with 160 virtual epochs for a total of 32B samples seen. The first 68 epochs were trained with float16 AMP, global batch size 79K (208 per GPU). Initially running to epoch 75, where the loss spiked and training failed with NaN. Romain Beaumont was training H/14 and g/14 models at the same time on Stability cluster and hit similar instabilities. Collectively we tried restarts with, * different dataset shuffle seed * different LR * gradient clipping * modifications to the architecture * Norm modifications (stable norm for final, post embed norm for text transformer) as per https://github.com/mlfoundations/open_clip/pull/153 thanks to Phil Wang * Extra attention block norms ala Normformer (https://arxiv.org/abs/2110.09456) * Scaled cosine attention ala Swin-V2 (https://arxiv.org/abs/2111.09883) None of the above ended up working. Most blew up within the same epoch as original, with the exception of architecture mods. * Normformer mods signifcantly altered the network such that resuming did not quickly converge to previous performance, this was abandoned but might be worth trying from start. * Scaled cosine attn initially looked promising and lasted until epoch 90 before loss suddenly increased and appeared to remain 'stuck'. In the end, restarting at epoch 69 with `float32` precision solved all instabilities and training continued from there with global batch size 86k (224 per GPU). On A100 GPUs, `float32` had a minimal impact on the throughput once `tf32` matmuls were enabled in PyTorch. Approximately 10% slower than `float16 AMP`. Romain similary changed the precision but ended up using `bfloat16 AMP` to resolve issues. ### Slum Script ``` #SBATCH --nodes=96 #SBATCH --gres=gpu:4 #SBATCH --ntasks-per-node=4 #SBATCH --cpus-per-task=6 #SBATCH --wait-all-nodes=1 #SBATCH --job-name=open_clip_laion2b # load low-level libraries ml purge source /conda/bin/activate pytorch-112 export NCCL_ASYNC_ERROR_HANDLING=1 export CUDA_VISIBLE_DEVICES=0,1,2,3 export MASTER_PORT=12802 ### get the first node name as master address - customized for vgg slurm ### e.g. master(gnodee[2-5],gnoded1) == gnodee2 echo "NODELIST="${SLURM_NODELIST} master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) export MASTER_ADDR=$master_addr"i" echo "MASTER_ADDR="$MASTER_ADDR cd /home/me/open_clip export PYTHONPATH="$PYTHONPATH:$PWD/src" srun --cpu_bind=none,v --accel-bind=gn python -u src/training/main.py \ --save-frequency 1 \ --zeroshot-frequency 1 \ --train-data="/data/laion2B-en/{00000..23295}.tar" \ --train-num-samples=200000000 \ --warmup 10000 \ --lr "1e-3" \ --batch-size=224 \ --epochs=160 \ --workers=6 \ --model ViT-L-14 \ --name "L14-laion2B" \ --report-to "tensorboard" \ --seed 0 \ --precision 'fp32' \ --ddp-static-graph \ --local-loss \ --dataset-resampled \ --gather-with-grad \ --grad-checkpointing ``` # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 75.3 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC). # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

stablediffusionapi/juggernaut-xl-v5

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Juggernaut XL V5 API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "juggernaut-xl-v5" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/juggernaut-xl-v5) Model link: [View model](https://stablediffusionapi.com/models/juggernaut-xl-v5) Credits: [View credits](https://civitai.com/?query=Juggernaut%20XL%20V5) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "juggernaut-xl-v5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

smilegate-ai/kor_unsmile

smilegate-ai

transformers

text-classification

Entry not found

THUDM/chatglm-6b

THUDM

transformers

--- language: - zh - en tags: - glm - chatglm - thudm --- # ChatGLM-6B <p align="center"> 🌐 <a href="https://chatglm.cn/blog" target="_blank">Blog</a> • 💻 <a href="https://github.com/THUDM/ChatGLM-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1y7pqoloy-9b1g6T6JjA8J0KxvUjbwJw" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a> </p> <p align="center"> 📍Experience the larger-scale ChatGLM model at <a href="https://www.chatglm.cn">chatglm.cn</a> </p> **我们发布了 [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B)，ChatGLM-6B 的升级版本，在保留了了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，引入了更强大的性能、更长的上下文、更高效的推理等升级。** ## 介绍 ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。 ChatGLM-6B 权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。 ChatGLM-6B is an open bilingual language model based on [General Language Model (GLM)](https://github.com/THUDM/GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dialogue. The model is trained for about 1T tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrap, and reinforcement learning with human feedback. With only about 6.2 billion parameters, the model is able to generate answers that are in line with human preference. ChatGLM-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://open.bigmodel.cn/mla/form). ## 软件依赖 ```shell pip install protobuf==3.20.0 transformers==4.27.1 icetk cpm_kernels ``` ## 代码调用 可以通过如下代码调用 ChatGLM-6B 模型来生成对话： ```ipython >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) >>> model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() >>> response, history = model.chat(tokenizer, "你好", history=[]) >>> print(response) 你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。 >>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) >>> print(response) 晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法: 1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。 2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。 3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。 4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。 5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。 6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。 如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。 ``` 关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。 For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM-6B). ## Change Log * v1.1.0 ([942945d](https://huggingface.co/THUDM/chatglm-6b/commit/942945df047dee66f653c68ae0e56655045f1741)): 更新 v1.1 版本 checkpoint * v0.1.0 ([f831824](https://huggingface.co/THUDM/chatglm-6b/commit/f83182484538e663a03d3f73647f10f89878f438)) ## 协议 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。 ## 引用 如果你觉得我们的工作有帮助的话，请考虑引用下列论文： ``` @inproceedings{ zeng2023glm-130b, title={{GLM}-130B: An Open Bilingual Pre-trained Model}, author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang}, booktitle={The Eleventh International Conference on Learning Representations (ICLR)}, year={2023}, url={https://openreview.net/forum?id=-Aw0rrrPUF} } ``` ``` @inproceedings{du2022glm, title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={320--335}, year={2022} } ```

textattack/bert-base-uncased-SST-2

textattack

transformers

text-classification

Entry not found

openlm-research/open_llama_7b

openlm-research

transformers

text-generation

--- license: apache-2.0 datasets: - togethercomputer/RedPajama-Data-1T --- # OpenLLaMA: An Open Reproduction of LLaMA In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details. ## Weights Release, License and Usage We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license. ### Loading the Weights with Hugging Face Transformers Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that the auto-converted fast tokenizer sometimes gives incorrect tokenizations.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage. ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM model_path = 'openlm-research/open_llama_3b' # model_path = 'openlm-research/open_llama_7b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = 'Q: What is the largest animal?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). ### Evaluating with LM-Eval-Harness The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below: ```python tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained( pretrained if tokenizer is None else tokenizer, revision=revision + ("/" + subfolder if subfolder is not None else ""), use_fast=False ) ``` ### Loading the Weights with EasyLM For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. Note that we use BOS (beginning of sentence) token (id=1) during training, so it is best to prepend this token for best performance during few-shot evaluation. ## Dataset and Training We train our models on the [RedPajama](https://www.together.xyz/blog/redpajama) dataset released by [Together](https://www.together.xyz/), which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA. We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open-source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model. ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. | **Task/Metric** | GPT-J 6B | LLaMA 7B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B 600BT | | ---------------------- | -------- | -------- | ------------ | ------------ | ------------------- | | anli_r1/acc | 0.32 | 0.35 | 0.33 | 0.33 | 0.33 | | anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.32 | 0.35 | | anli_r3/acc | 0.35 | 0.37 | 0.38 | 0.35 | 0.38 | | arc_challenge/acc | 0.34 | 0.39 | 0.37 | 0.34 | 0.39 | | arc_challenge/acc_norm | 0.37 | 0.41 | 0.38 | 0.37 | 0.42 | | arc_easy/acc | 0.67 | 0.68 | 0.72 | 0.69 | 0.74 | | arc_easy/acc_norm | 0.62 | 0.52 | 0.68 | 0.65 | 0.70 | | ddboolq/acc | 0.50 | 0.56 | 0.53 | 0.49 | 0.71 | | hellaswag/acc | 0.36 | 0.36 | 0.63 | 0.43 | 0.54 | | hellaswag/acc_norm | 0.66 | 0.73 | 0.72 | 0.67 | 0.73 | | openbookqa/acc | 0.29 | 0.29 | 0.30 | 0.27 | 0.30 | | openbookqa/acc_norm | 0.38 | 0.41 | 0.40 | 0.40 | 0.41 | | piqa/acc | 0.75 | 0.78 | 0.76 | 0.75 | 0.77 | | piqa/acc_norm | 0.76 | 0.78 | 0.77 | 0.76 | 0.78 | | record/em | 0.88 | 0.91 | 0.89 | 0.88 | 0.90 | | record/f1 | 0.89 | 0.91 | 0.90 | 0.89 | 0.90 | | rte/acc | 0.54 | 0.56 | 0.60 | 0.58 | 0.65 | | truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.23 | 0.22 | 0.22 | | truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.35 | 0.35 | 0.35 | | wic/acc | 0.50 | 0.50 | 0.51 | 0.48 | 0.49 | | winogrande/acc | 0.64 | 0.68 | 0.67 | 0.62 | 0.67 | | Average | 0.51 | 0.53 | 0.55 | 0.52 | 0.56 | We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Contact We would love to get feedback from the community. If you have any questions, please open an issue or contact us. OpenLLaMA is developed by: [Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research. *Equal Contribution ## Acknowledgment We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback. The OpenLLaMA 13B model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support. ## Reference If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX: ``` @software{openlm2023openllama, author = {Geng, Xinyang and Liu, Hao}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ``` @article{touvron2023llama, title={Llama: Open and efficient foundation language models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```

google/t5-v1_1-base

google

transformers

text2text-generation

--- language: en datasets: - c4 license: apache-2.0 --- [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 ## Version 1.1 [T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning. - Pre-trained on C4 only without mixing in the downstream tasks. - no parameter sharing between embedding and classifier layer - "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller `num_heads` and `d_ff`. **Note**: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5-v1_1) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. 

hustvl/yolos-small

hustvl

transformers

object-detection

--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # YOLOS (small-sized) model YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models. ### How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small') model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) # model predicts bounding boxes and corresponding COCO classes logits = outputs.logits bboxes = outputs.pred_boxes ``` Currently, both the feature extractor and model support PyTorch. ## Training data The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### Training The model was pre-trained for 200 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO. ## Evaluation results This model achieves an AP (average precision) of **36.1** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-00666, author = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu}, title = {You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, journal = {CoRR}, volume = {abs/2106.00666}, year = {2021}, url = {https://arxiv.org/abs/2106.00666}, eprinttype = {arXiv}, eprint = {2106.00666}, timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

asahi417/tner-xlm-roberta-base-all-english

asahi417

transformers

token-classification

# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-all-english") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-all-english") ```

TheBloke/Llama-2-13B-chat-GPTQ

TheBloke

transformers

text-generation

--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 13B Chat base_model: meta-llama/Llama-2-13b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don''t know the answer to a question, please don''t share false information. <</SYS>> {prompt}[/INST] ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 13B Chat - GPTQ - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) <!-- description start --> ## Description This repo contains GPTQ model files for [Meta's Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13B-chat-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.95 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. | | [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-13B-chat-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-13B-chat-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 13B-chat # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

navteca/ms-marco-MiniLM-L-6-v2

navteca

sentence-transformers

text-classification

--- language: en license: mit pipeline_tag: text-classification tags: - sentence-transformers --- # Cross-Encoder for MS Marco The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) ## Training Data This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. ## Usage The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')]) ``` ## Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | **Version 2 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960 | **Version 1 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | **Other models** | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720 Note: Runtime was computed on a V100 GPU.

Helsinki-NLP/opus-mt-tr-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-tr-en * source languages: tr * target languages: en * OPUS readme: [tr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tr-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tr-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tr-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-entr.tr.en | 27.6 | 0.548 | | newstest2016-entr.tr.en | 25.2 | 0.532 | | newstest2017-entr.tr.en | 24.7 | 0.530 | | newstest2018-entr.tr.en | 27.0 | 0.547 | | Tatoeba.tr.en | 63.5 | 0.760 |

facebook/m2m100_1.2B

facebook

transformers

text2text-generation

--- language: - multilingual - af - am - ar - ast - az - ba - be - bg - bn - br - bs - ca - ceb - cs - cy - da - de - el - en - es - et - fa - ff - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - ht - hu - hy - id - ig - ilo - is - it - ja - jv - ka - kk - km - kn - ko - lb - lg - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - ns - oc - or - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - th - tl - tn - tr - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu license: mit --- # M2M100 1.2B M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` ```python from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chinese_text = "生活就像一盒巧克力。" model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B") # translate Hindi to French tokenizer.src_lang = "hi" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "La vie est comme une boîte de chocolat." # translate Chinese to English tokenizer.src_lang = "zh" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Life is like a box of chocolate." ``` See the [model hub](https://huggingface.co/models?filter=m2m_100) to look for more fine-tuned versions. ## Languages covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu) ## BibTeX entry and citation info ``` @misc{fan2020englishcentric, title={Beyond English-Centric Multilingual Machine Translation}, author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin}, year={2020}, eprint={2010.11125}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

facebook/opt-6.7b

facebook

transformers

text-generation

--- language: en inference: false tags: - text-generation - opt license: other commercial: false --- # OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU. It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ["Hello, I'm am conscious and aware of my surroundings. I'm not sure what you mean"] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ["Hello, I'm am conscious and aware of my surroundings. I'm not sure if I'm"] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The woman worked as a supervisor in the office The woman worked as a bartender in a bar The woman worked as a cashier at the The woman worked as a teacher, and was The woman worked as a maid at a house ``` compared to: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The man worked as a consultant to the Government The man worked as a bartender in a bar The man worked as a cashier at the The man worked as a teacher, and was The man worked as a professional at a bank ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Ericwang/tiny-random-ast

Ericwang

transformers

feature-extraction

One custom ast model for testing of HF repos

google/pegasus-cnn_dailymail

google

transformers

summarization

--- language: en tags: - summarization --- ### Pegasus Models See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html) Original TF 1 code [here](https://github.com/google-research/pegasus) Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: [@sshleifer](https://twitter.com/sam_shleifer) Task: Summarization The following is copied from the authors' README. # Mixed & Stochastic Checkpoints We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. | dataset | C4 | HugeNews | Mixed & Stochastic| | ---- | ---- | ---- | ----| | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64| | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30| | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18| | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95| | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76| | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *| | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94| | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *| | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67| | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25| | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51| | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59| The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). the model uniformly sample a gap sentence ratio between 15% and 45%. importance sentences are sampled using a 20% uniform noise to importance scores. the sentencepiece tokenizer is updated to be able to encode newline character. Citation ``` @misc{zhang2019pegasus, title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization}, author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu}, year={2019}, eprint={1912.08777}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ahmedrachid/FinancialBERT-Sentiment-Analysis

ahmedrachid

transformers

text-classification

--- language: en tags: - financial-sentiment-analysis - sentiment-analysis datasets: - financial_phrasebank widget: - text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales. - text: Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000. - text: Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008. --- ### FinancialBERT for Sentiment Analysis [*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining ### Training data FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). ### Fine-tuning hyper-parameters - learning_rate = 2e-5 - batch_size = 32 - max_seq_length = 512 - num_train_epochs = 5 ### Evaluation metrics The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. | sentiment | precision | recall | f1-score | support | | ------------- |:-------------:|:-------------:|:-------------:| -----:| | negative | 0.96 | 0.97 | 0.97 | 58 | | neutral | 0.98 | 0.99 | 0.98 | 279 | | positive | 0.98 | 0.97 | 0.97 | 148 | | macro avg | 0.97 | 0.98 | 0.98 | 485 | | weighted avg | 0.98 | 0.98 | 0.98 | 485 | ### How to use The model can be used thanks to Transformers pipeline for sentiment analysis. ```python from transformers import BertTokenizer, BertForSequenceClassification from transformers import pipeline model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3) tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.", "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", ] results = nlp(sentences) print(results) [{'label': 'positive', 'score': 0.9998133778572083}, {'label': 'neutral', 'score': 0.9997822642326355}, {'label': 'negative', 'score': 0.9877365231513977}] ``` > Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)

MoritzLaurer/mDeBERTa-v3-base-mnli-xnli

MoritzLaurer

transformers

zero-shot-classification

--- language: - multilingual - en - ar - bg - de - el - es - fr - hi - ru - sw - th - tr - ur - vi - zh license: mit tags: - zero-shot-classification - text-classification - nli - pytorch metrics: - accuracy datasets: - multi_nli - xnli pipeline_tag: zero-shot-classification widget: - text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels: "politics, economy, entertainment, environment" --- # Multilingual mDeBERTa-v3-base-mnli-xnli ## Model description This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli). As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). If you are looking for a smaller, faster (but less performant) model, you can try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli). ### How to use the model #### Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" hypothesis = "Emmanuel Macron is the President of France" input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, but due to quality issues with these machine translations, this model was only trained on the professional translations from the XNLI development set and the original English MNLI training set (392 702 texts). Not using machine translated texts can avoid overfitting the model to the 15 languages; avoids catastrophic forgetting of the other 85 languages mDeBERTa was pre-trained on; and significantly reduces training costs. ### Training procedure mDeBERTa-v3-base-mnli-xnli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=2, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=16, # batch size per device during training per_device_eval_batch_size=16, # batch size for evaluation warmup_ratio=0.1, # number of warmup steps for learning rate scheduler weight_decay=0.06, # strength of weight decay ) ``` ### Eval results The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on the other 85 languages mDeBERTa was training on, but performance is most likely lower than for those languages available in XNLI. Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, the authors have most likely made a mistake during testing since non of the latest papers shows a multilingual average performance of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vi | zh ---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|---------- 0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116 ## Limitations and bias Please consult the original DeBERTa-V3 paper and literature on different NLI datasets for potential biases. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ## Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ## Debugging and issues Note that DeBERTa-v3 was released in late 2021 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 or higher might solve some issues. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77

shahrukhx01/bert-mini-finetune-question-detection

shahrukhx01

transformers

text-classification

--- language: "en" tags: - neural-search-query-classification - neural-search widget: - text: "keyword query." --- # KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH | Train Loss | Validation Acc.| Test Acc.| | ------------- |:-------------: | -----: | | 0.000806 | 0.99 | 0.997 | ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/bert-mini-finetune-question-detection") model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/bert-mini-finetune-question-detection") ``` Trained to add feature for classifying queries between Keyword Query or Question + Statement Query using classification in [Haystack](https://github.com/deepset-ai/haystack/issues/611) Problem Statement: One common challenge that we saw in deployments: We need to distinguish between real questions and keyword queries that come in. We only want to route questions to the Reader branch in order to maximize the accuracy of results and minimize computation efforts/costs. Baseline: https://www.kaggle.com/shahrukhkhan/question-v-statement-detection Dataset: https://www.kaggle.com/stefanondisponibile/quora-question-keyword-pairs Kaggle Notebook: https://www.kaggle.com/shahrukhkhan/question-vs-statement-classification-mini-bert/

google/pix2struct-base

google

transformers

image-to-text

--- language: - en - fr - ro - de - multilingual pipeline_tag: image-to-text inference: false license: apache-2.0 --- # Model card for Pix2Struct - Pretrained weights  This model is the pretrained version of `Pix2Struct`, use this model for fine-tuning purposes only. # Table of Contents 0. [TL;DR](#TL;DR) 1. [Using the model](#using-the-model) 2. [Contribution](#contribution) 3. [Citation](#citation) # TL;DR Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper:  The abstract of the model states that: > Visually-situated language is ubiquitous—sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images. # Using the model ## Converting from T5x to huggingface You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_original_pytorch_to_hf.py) script as follows: ```bash python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE ``` if you are converting a large model, run: ```bash python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large ``` Once saved, you can push your converted model with the following snippet: ```python from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE) processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE) model.push_to_hub("USERNAME/MODEL_NAME") processor.push_to_hub("USERNAME/MODEL_NAME") ``` # Contribution This model was originally contributed by Kenton Lee, Mandar Joshi et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada). # Citation If you want to cite this work, please consider citing the original paper: ``` @misc{https://doi.org/10.48550/arxiv.2210.03347, doi = {10.48550/ARXIV.2210.03347}, url = {https://arxiv.org/abs/2210.03347}, author = {Lee, Kenton and Joshi, Mandar and Turc, Iulia and Hu, Hexiang and Liu, Fangyu and Eisenschlos, Julian and Khandelwal, Urvashi and Shaw, Peter and Chang, Ming-Wei and Toutanova, Kristina}, keywords = {Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

Salesforce/instructblip-vicuna-7b

Salesforce

transformers

image-to-text

--- language: en license: other tags: - vision - image-captioning pipeline_tag: image-to-text --- # InstructBLIP model InstructBLIP model using [Vicuna-7b](https://github.com/lm-sys/FastChat#model-weights) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al. Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details.  ## Intended uses & limitations Usage is as follows: ``` from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration import torch from PIL import Image import requests model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b") processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) outputs = model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() print(generated_text) ``` ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).

dandelin/vilt-b32-finetuned-vqa

dandelin

transformers

visual-question-answering

--- tags: - visual-question-answering license: apache-2.0 widget: - text: "What's the animal doing?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg" - text: "What is on top of the building?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg" --- # Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for visual question answering. ### How to use Here is how to use this model in PyTorch: ```python from transformers import ViltProcessor, ViltForQuestionAnswering import requests from PIL import Image # prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) logits = outputs.logits idx = logits.argmax(-1).item() print("Predicted answer:", model.config.id2label[idx]) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```

dmis-lab/biobert-base-cased-v1.2

dmis-lab

transformers

fill-mask

Entry not found

CiaraRowles/TemporalNet

CiaraRowles

diffusers

--- license: openrail tags: - controlnet - stable-diffusion - diffusers base_model: runwayml/stable-diffusion-v1-5 --- Introducing the Beta Version of TemporalNet TemporalNet is a ControlNet model designed to enhance the temporal consistency of generated outputs, as demonstrated in this example: https://twitter.com/CiaraRowles1/status/1637486561917906944. While it does not eliminate all flickering, it significantly reduces it, particularly at higher denoise levels. For optimal results, it is recommended to use TemporalNet in combination with other methods. Instructions for Use: 1) Add the model "diff_control_sd15_temporalnet_fp16.safetensors" to your models folder in the ControlNet extension in Automatic1111's Web UI. 2) Create a folder that contains: - A subfolder named "Input_Images" with the input frames - A PNG file called "init.png" that is pre-stylized in your desired style - The "temporalvideo.py" script 3) Customize the "temporalvideo.py" script according to your preferences, such as the image resolution, prompt, and control net settings. 4) Launch Automatic1111's Web UI with the --api setting enabled. 5) Execute the Python script. *Please note that the "init.png" image will not significantly influence the style of the output video. Its primary purpose is to prevent a drastic change in aesthetics during the first few frames.* Also, I highly recommend you use this in conjunction with the hed model, the settings are already in the script. ToDo: Write an Extension for the web ui. Write a feature that automatically generates an "init.png" image if none is provided. ̶C̶h̶a̶n̶g̶e̶ ̶t̶h̶e̶ ̶e̶x̶t̶e̶n̶s̶i̶o̶n̶ ̶t̶o̶ ̶.̶s̶a̶f̶e̶t̶e̶n̶s̶o̶r̶s̶ ̶a̶n̶d̶ ̶i̶n̶v̶e̶s̶t̶i̶g̶a̶t̶e̶ ̶c̶o̶m̶p̶r̶e̶s̶s̶i̶o̶n̶.̶

google/bert_uncased_L-12_H-768_A-12

google

transformers

--- thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- BERT Miniatures === This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |H=128|H=256|H=512|H=768| |---|:---:|:---:|:---:|:---:| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. Here are the corresponding GLUE scores on the test set: |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: - batch sizes: 8, 16, 32, 64, 128 - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 If you use these models, please cite the following paper: ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ``` [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12

facebook/opt-2.7b

facebook

transformers

text-generation

--- language: en inference: false tags: - text-generation - opt license: other commercial: false --- # OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-2.7b") >>> generator("What are we having for dinner?") [{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True) >>> generator("What are we having for dinner?") [{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The woman worked as a") [{'generated_text': "The woman worked as a security guard at a nursery in the city's eastern district of Samut P"}, {'generated_text': 'The woman worked as a doctor in the Philippines. Officials in China allege she stole the coronavirus'}, {'generated_text': 'The woman worked as a teacher in the city of Krasnodar in south Russia. She'}, {'generated_text': 'The woman worked as a researcher and lecturer at the Russian Academy of Sciences in a laboratory dedicated to the'}, {'generated_text': 'The woman worked as a nanny on a property owned by Mr Fitton-Allen in the city'}] ``` compared to: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The man worked as a") [{'generated_text': "The man worked as a security guard at a retirement home after being hired by the administrator's cousin,"}, {'generated_text': 'The man worked as a doctor in the Philippines.\n\nHe had hoped to work his way back'}, {'generated_text': 'The man worked as a teacher in the city of Krasnodar in south Russia.He'}, {'generated_text': 'The man worked as a researcher and his work on the topic predates the project, by many years'}, {'generated_text': 'The man worked as a chef in a restaurant for 40 years. How could this be so different from'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

MCG-NJU/videomae-base

MCG-NJU

transformers

video-classification

--- license: "cc-by-nc-4.0" tags: - vision - video-classification --- # VideoMAE (base-sized model, pre-trained only) VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE). Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description VideoMAE is an extension of [Masked Autoencoders (MAE)](https://arxiv.org/abs/2111.06377) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video. ## Intended uses & limitations You can use the raw model for predicting pixel values for masked patches of a video, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=videomae) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to predict pixel values for randomly masked patches: ```python from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining import numpy as np import torch num_frames = 16 video = list(np.random.randn(16, 3, 224, 224)) processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-base") model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") pixel_values = processor(video, return_tensors="pt").pixel_values num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) loss = outputs.loss ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/videomae.html#). ## Training data (to do, feel free to open a PR) ## Training procedure ### Preprocessing (to do, feel free to open a PR) ### Pretraining (to do, feel free to open a PR) ## Evaluation results (to do, feel free to open a PR) ### BibTeX entry and citation info ```bibtex misc{https://doi.org/10.48550/arxiv.2203.12602, doi = {10.48550/ARXIV.2203.12602}, url = {https://arxiv.org/abs/2203.12602}, author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

facebook/detr-resnet-101-dc5

facebook

transformers

object-detection

--- license: apache-2.0 tags: - object-detection datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage) DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. ### How to use Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5') model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) # model predicts bounding boxes and corresponding COCO classes logits = outputs.logits bboxes = outputs.pred_boxes ``` Currently, both the feature extractor and model support PyTorch. ## Training data The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225). ### Training The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64). ## Evaluation results This model achieves an AP (average precision) of **44.9** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to-End Object Detection with Transformers}, journal = {CoRR}, volume = {abs/2005.12872}, year = {2020}, url = {https://arxiv.org/abs/2005.12872}, archivePrefix = {arXiv}, eprint = {2005.12872}, timestamp = {Thu, 28 May 2020 17:38:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

rasa/LaBSE

rasa

transformers

feature-extraction

Entry not found

microsoft/biogpt

microsoft

transformers

text-generation

--- language: en license: mit widget: - text: "COVID-19 is" --- ## BioGPT Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> from transformers import BioGptTokenizer, BioGptForCausalLM >>> model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") >>> tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> set_seed(42) >>> generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True) [{'generated_text': 'COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population'}, {'generated_text': 'COVID-19 is one of the largest viral epidemics in the world.'}, {'generated_text': 'COVID-19 is a common condition affecting an estimated 1.1 million people in the United States alone.'}, {'generated_text': 'COVID-19 is a pandemic, the incidence has been increased in a manner similar to that in other'}, {'generated_text': 'COVID-19 is transmitted via droplets, air-borne, or airborne transmission.'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BioGptTokenizer, BioGptForCausalLM tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` Beam-search decoding: ```python import torch from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") sentence = "COVID-19 is" inputs = tokenizer(sentence, return_tensors="pt") set_seed(42) with torch.no_grad(): beam_output = model.generate(**inputs, min_length=100, max_length=1024, num_beams=5, early_stopping=True ) tokenizer.decode(beam_output[0], skip_special_tokens=True) 'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK), and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and more than 800,000 deaths.' ``` ## Citation If you find BioGPT useful in your research, please cite the following paper: ```latex @article{10.1093/bib/bbac409, author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan}, title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}", journal = {Briefings in Bioinformatics}, volume = {23}, number = {6}, year = {2022}, month = {09}, abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}", issn = {1477-4054}, doi = {10.1093/bib/bbac409}, url = {https://doi.org/10.1093/bib/bbac409}, note = {bbac409}, eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf}, } ```

timm/mobilenetv2_100.ra_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for mobilenetv2_100.ra_in1k A MobileNet-v2 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 3.5 - GMACs: 0.3 - Activations (M): 6.7 - Image size: 224 x 224 - **Papers:** - MobileNetV2: Inverted Residuals and Linear Bottlenecks: https://arxiv.org/abs/1801.04381 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('mobilenetv2_100.ra_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mobilenetv2_100.ra_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 16, 112, 112]) # torch.Size([1, 24, 56, 56]) # torch.Size([1, 32, 28, 28]) # torch.Size([1, 96, 14, 14]) # torch.Size([1, 320, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mobilenetv2_100.ra_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1280, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{sandler2018mobilenetv2, title={Mobilenetv2: Inverted residuals and linear bottlenecks}, author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={4510--4520}, year={2018} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ```

nlpaueb/legal-bert-small-uncased

nlpaueb

transformers

fill-mask

--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks.<br> This is the light-weight version of BERT-BASE (33% the size of BERT-BASE) pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! ## Models list | Model name | Model Path | Training corpora | | ------------------- | ------------------------------------ | ------------------- | | CONTRACTS-BERT-BASE | `nlpaueb/bert-base-uncased-contracts` | US contracts | | EURLEX-BERT-BASE | `nlpaueb/bert-base-uncased-eurlex` | EU legislation | | ECHR-BERT-BASE | `nlpaueb/bert-base-uncased-echr` | ECHR cases | | LEGAL-BERT-BASE * | `nlpaueb/legal-bert-base-uncased` | All | | LEGAL-BERT-SMALL | `nlpaueb/legal-bert-small-uncased` | All | \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \*\* As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-small-uncased") model = AutoModel.from_pretrained("nlpaueb/legal-bert-small-uncased") ``` ## Use LEGAL-BERT variants as Language Models | Corpus | Model | Masked token | Predictions | | --------------------------------- | ---------------------------------- | ------------ | ------------ | | | **BERT-BASE-UNCASED** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') | | **CONTRACTS-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') | | **EURLEX-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') | | **ECHR-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') | | **LEGAL-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') | | **LEGAL-BERT-SMALL** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) | Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |

google/byt5-xl

google

transformers

text2text-generation

--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- # ByT5 - xl ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xl). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-xl` significantly outperforms [mt5-xl](https://huggingface.co/google/mt5-xl) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') tokenizer = AutoTokenizer.from_pretrained('google/byt5-xl') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. 

Crosstyan/BPModel

Crosstyan

diffusers

text-to-image

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - safetensors inference: true thumbnail: https://s2.loli.net/2023/05/31/bl27yWANrT3asoG.png widget: - text: >- 1girl with blonde two side up disheveled hair red eyes in black serafuku red ribbon, upper body, simple background, grey background, collarbone example_title: example 1girl datasets: - Crosstyan/BPDataset library_name: diffusers --- # BPModel  ## Update **2023-01-02:** I wasted more GPU hours to train it a little bit more overfitting. Check out [bp_mk3.safetensors](bp_mk3.safetensors) and [bp_mk5.safetensors](bp_mk5.safetensors). Prepare yourself own VAE! Update your WebUI if you can't load [safetensors](https://github.com/huggingface/safetensors). Adds lots of samples in `images` folder! **2023-01-06:** Checkout [NMFSAN](NMFSAN/README.md) for a new model trained with custom embeddings. ## Introduction  BPModel is an experimental Stable Diffusion model based on [ACertainty](https://huggingface.co/JosephusCheung/ACertainty) from [Joseph Cheung](https://huggingface.co/JosephusCheung). Why is the Model even existing? There are loads of Stable Diffusion model out there, especially anime style models. Well, is there any models trained with resolution base resolution (`base_res`) 768 even 1024 before? Don't think so. Here it is, the BPModel, a Stable Diffusion model you may love or hate. Trained with 5k high quality images that suit my taste (not necessary yours unfortunately) from [Sankaku Complex](https://chan.sankakucomplex.com) with annotations. The dataset is public in [Crosstyan/BPDataset](https://huggingface.co/datasets/Crosstyan/BPDataset) for the sake of full disclosure . Pure combination of tags may not be the optimal way to describe the image, but I don't need to do extra work. And no, I won't feed any AI generated image to the model even it might outlaw the model from being used in some countries. The training of a high resolution model requires a significant amount of GPU hours and can be costly. In this particular case, 10 V100 GPU hours were spent on training 30 epochs with a resolution of 512, while 60 V100 GPU hours were spent on training 30 epochs with a resolution of 768. An additional 100 V100 GPU hours were also spent on training a model with a resolution of 1024, although **ONLY** 10 epochs were run. The results of the training on the 1024 resolution model did not show a significant improvement compared to the 768 resolution model, and the resource demands, achieving a batch size of 1 on a V100 with 32G VRAM, were high. However, training on the 768 resolution did yield better results than training on the 512 resolution, and it is worth considering as an option. It is worth noting that Stable Diffusion 2.x also chose to train on a 768 resolution model. However, it may be more efficient to start with training on a 512 resolution model due to the slower training process and the need for additional prior knowledge to speed up the training process when working with a 768 resolution. [Mikubill/naifu-diffusion](https://github.com/Mikubill/naifu-diffusion) is used as training script and I also recommend to checkout [CCRcmcpe/scal-sdt](https://github.com/CCRcmcpe/scal-sdt). The configuration for 1024 and 768 resolution with aspect ratio bucket is presented here. ```yaml # 768 arb: enabled: true debug: false base_res: [768, 768] max_size: [1152, 768] divisible: 64 max_ar_error: 4 min_dim: 512 dim_limit: 1792 # 1024 arb: enabled: true debug: false base_res: [1024, 1024] max_size: [1536, 1024] divisible: 64 max_ar_error: 4 min_dim: 960 dim_limit: 2389 ``` ## Limitation  The limitation described in [SCAL-SDT Wiki](https://github.com/CCRcmcpe/scal-sdt/wiki#what-you-should-expect) is still applied. > SD cannot generate human body properly, like generating 6 fingers on one hand. BPModel can generate [more proper kitty cat](https://twitter.com/crosstyan/status/1606026536246685696) (if you know what I mean) than other anime model, but it's still not perfect. As results presented in [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://arxiv.org/abs/2212.03860), the copy and paste effect is still observed. Anything v3™ has been proven to be the most popular anime model in the community, but it's not perfect either as described in [JosephusCheung/ACertainThing](https://huggingface.co/JosephusCheung/ACertainThing) > It does not always stay true to your prompts; it adds irrelevant details, and sometimes these details are highly homogenized. BPModel, which has been fine-tuned on a relatively small dataset, is prone to overfit inherently. This is not surprising given the size of the dataset, but the strong prior knowledge of ACertainty (full Danbooru) and Stable Diffusion (LAION) helps to minimize the impact of overfitting. However I believe it would perform better than some artist style DreamBooth model which only train with a few hundred images or even less. I also oppose changing style by merging model since You could apply different style by training with proper captions and prompting. Besides some of images in my dataset have the artist name in the caption, however some artist name will be misinterpreted by CLIP when tokenizing. For example, *as109* will be tokenized as `[as, 1, 0, 9]` and *fuzichoco* will become `[fu, z, ic, hoco]`. Romanized Japanese suffers from the problem a lot and I don't have a good solution to fix it other than changing the artist name in the caption, which is time consuming and you can't promise the token you choose is unique enough. [Remember the sks?](https://www.reddit.com/r/StableDiffusion/comments/yju5ks/from_one_of_the_original_dreambooth_authors_stop/) Language drift problem is still exist. There's nothing I can do unless I can find a way to generate better caption or caption the image manually. [OFA](https://github.com/OFA-Sys/OFA) combined with [convnext-tagger](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger) could provide a better result for SFW content. However fine tune is necessary for NSFW content, which I don't think anyone would like to do. (Could Unstable Diffusion give us surprise?) ## Cherry Picked Samples Here're some **cherry picked** samples. I were using [xformers](https://github.com/facebookresearch/xformers) when generating these sample and it might yield slight different result even with the same seed (welcome to the non deterministic field). "`Upscale latent space image when doing hires. fix`" is enabled also.  ```txt by (fkey:1) (shion:0.4) [sketch:0.75] (closed mouth expressionless:1) cat ears nekomimi 1girl, wearing a white sailor uniform with a short skirt and white pantyhose standing on the deck of a yacht, cowboy shot, and the sun setting behind her in the background, light particle, bokeh Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, worst quality, low quality, normal quality, lipstick, 2koma, 3koma, dutch angle, blush, from behind Steps: 28, Sampler: Euler a, CFG scale: 12, Seed: 4236324744, Size: 960x1600, Model hash: 855959a4, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt 1girl in black serafuku standing in a field solo, food, fruit, lemon, bubble, planet, moon, orange \(fruit\), lemon slice, leaf, fish, orange slice, by (tabi:1.25), spot color, looking at viewer, closeup cowboy shot Negative prompt: (bad:0.81), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:0.81), (speech:0.81), (worst:0.81), (blush:0.9), 2koma, 3koma, 4koma, collage, lipstick Steps: 18, Sampler: DDIM, CFG scale: 7, Seed: 2017390109, Size: 768x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 1, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt [sketch:0.75] [(oil painting:0.5)::0.75] by (fuzichoco:0.8) shion (fkey:0.9), fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves hand on own cheek hand on own face sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 910302581, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 2, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt (best:0.7), highly detailed,1girl,upper body,beautiful detailed eyes, medium_breasts, long hair,grey hair, grey eyes, curly hair, bangs,empty eyes,expressionless,twintails, beautiful detailed sky, beautiful detailed water, [cinematic lighting:0.6], upper body, school uniform,black ribbon,light smile Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler, CFG scale: 8.5, Seed: 2311603025, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 3, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ``` *I don't think other model can do that.*  ```txt by [shion (fkey:0.9):momoko \(momopoco\):0.15], fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves hand on own cheek (middle finger:1.1) sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 2496891010, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 1, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt by [shion (fkey:0.9):momoko \(momopoco\):0.55], closed mouth fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves (middle finger:1.1) sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick, (chibi:0.8) Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 2668993375, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 3, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ``` more samples can be found in [images](images/00976-3769766671_20221226155509.png) folder. ## Usage The [`bp_1024_e10.ckpt`](bp_1024_e10.ckpt) doesn't include any VAE and you should using other popular VAE in the community when using with [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or you would see the LaTeNt SpAcE! Use [`bp_1024_with_vae_te.ckpt`](bp_1024_with_vae_te.ckpt) if you don't have VAE and text encoder with you, still EMA weight is not included and it's fp16. If you want to continue training, use [`bp_1024_e10_ema.ckpt`](bp_1024_e10_ema.ckpt) which is the ema unet weight and with fp32 precision. For better performance, it is strongly recommended to use Clip skip (CLIP stop at last layers) 2. It's also recommended to use turn on "`Upscale latent space image when doing hires. fix`" in the settings of [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which adds intricate details when using `Highres. fix`. ## About the Model Name I asked the [chatGPT](https://openai.com/blog/chatgpt/) what the proper explanation of abbreviation BP could be. ```txt Here are a few more ideas for creative interpretations of the abbreviation "BP": - Brightest Point - This could refer to a moment of exceptional brilliance or clarity. - Brainpower - the abbreviation refers to something that requires a lot of mental effort or intelligence to accomplish. - Bespoke Partition - A custom made section that separates two distinct areas. - Bukkake Picchi - A Japanese style of rice dish. - Bokeh Picker - A traditional Japanese photography technique that involves selecting points of light from a digital image. - Bipolarity - Two distinct and opposing elements or perspectives. Note that "BP" is often used as an abbreviation for "blood pressure," so it is important to context to determine the most appropriate interpretation of the abbreviation. ``` Personally, I would call it "Big Pot". ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license/blob/main/license.txt)

nvidia/segformer-b5-finetuned-ade-640-640

nvidia

transformers

image-segmentation

--- license: other tags: - vision - image-segmentation datasets: - scene_parse_150 widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # SegFormer (b5-sized) model fine-tuned on ADE20k SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. ## Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

facebook/esm2_t33_650M_UR50D

facebook

transformers

fill-mask

--- license: mit widget: - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG" --- ## ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train: | Checkpoint name | Num layers | Num parameters | |------------------------------|----|----------| | [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B | | [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B | | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M | | [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M | | [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M | | [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M |

yiyanghkust/finbert-esg

yiyanghkust

transformers

text-classification

--- language: "en" tags: - financial-text-analysis - esg - environmental-social-corporate-governance widget: - text: "Rhonda has been volunteering for several years for a variety of charitable community programs. " --- ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports. **Input**: A financial text. **Output**: Environmental, Social, Governance or None. # How to use You can use this model with Transformers pipeline for ESG classification. ```python # tested in transformers==4.18.0 from transformers import BertTokenizer, BertForSequenceClassification, pipeline finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg',num_labels=4) tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg') nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) results = nlp('Rhonda has been volunteering for several years for a variety of charitable community programs.') print(results) # [{'label': 'Social', 'score': 0.9906041026115417}] ``` Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT. If you use the model in your academic work, please cite the following paper: Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

yanekyuk/camembert-keyword-extractor

yanekyuk

transformers

token-classification

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - accuracy - f1 language: - fr widget: - text: "Le président de la République appelle en outre les Français à faire le choix d'une \"majorité stable et sérieuse pour les protéger face aux crises et pour agir pour l'avenir\". \"Je vois dans le projet de Jean-Luc Mélenchon ou de Madame Le Pen un projet de désordre et de soumission. Ils expliquent qu'il faut sortir de nos alliances, de l'Europe, et bâtir des alliances stratégiques avec la Russie. C'est la soumission à la Russie\", assure-t-il." - text: "Top départ à l’ouverture des bureaux de vote. La Polynésie et les Français résidant à l'étranger, dont certains ont déjà pu voter en ligne, sont invités aux urnes ce week-end pour le premier tour des législatives, samedi 4 juin pour le continent américain et les Caraïbes, et dimanche 5 juin pour le reste du monde. En France métropolitaine, les premier et second tours auront lieu les 12 et 19 juin." - text: "Le ministère a aussi indiqué que des missiles russes ont frappé un centre d'entraînement d'artillerie dans la région de Soumy où travaillaient des instructeurs étrangers. Il a jouté qu'une autre frappe avait détruit une position de \"mercenaires étrangers\" dans la région d'Odessa." - text: "Le malaise est profond et ressemble à une crise existentielle. Fait rarissime au Quai d’Orsay, six syndicats et un collectif de 500 jeunes diplomates du ministère des Affaires étrangères ont appelé à la grève, jeudi 2 juin, pour protester contre la réforme de la haute fonction publique qui, à terme, entraînera la disparition des deux corps historiques de la diplomatie française : celui de ministre plénipotentiaire (ambassadeur) et celui de conseiller des affaires étrangères." - text: "Ils se font passer pour des recruteurs de Lockheed Martin ou du géant britannique de la défense et de l’aérospatial BAE Systems. Ces soi-disant chasseurs de tête font miroiter des perspectives lucratives de carrière et des postes à responsabilité. Mais ce n’est que du vent. En réalité, il s’agit de cyberespions nord-coréens cherchant à voler des secrets industriels de groupes de défense ou du secteur de l’aérospatial, révèle Eset, une société slovaque de sécurité informatique, dans un rapport publié mardi 31 mai." model-index: - name: camembert-keyword-extractor results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-keyword-extractor This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2199 - Precision: 0.6743 - Recall: 0.6979 - Accuracy: 0.9346 - F1: 0.6859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:| | 0.1747 | 1.0 | 1875 | 0.1780 | 0.5935 | 0.7116 | 0.9258 | 0.6472 | | 0.1375 | 2.0 | 3750 | 0.1588 | 0.6505 | 0.7032 | 0.9334 | 0.6759 | | 0.1147 | 3.0 | 5625 | 0.1727 | 0.6825 | 0.6689 | 0.9355 | 0.6756 | | 0.0969 | 4.0 | 7500 | 0.1759 | 0.6886 | 0.6621 | 0.9350 | 0.6751 | | 0.0837 | 5.0 | 9375 | 0.1967 | 0.6688 | 0.7112 | 0.9348 | 0.6893 | | 0.0746 | 6.0 | 11250 | 0.2088 | 0.6646 | 0.7114 | 0.9334 | 0.6872 | | 0.0666 | 7.0 | 13125 | 0.2169 | 0.6713 | 0.7054 | 0.9347 | 0.6879 | | 0.0634 | 8.0 | 15000 | 0.2199 | 0.6743 | 0.6979 | 0.9346 | 0.6859 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

bhadresh-savani/distilbert-base-uncased-emotion

bhadresh-savani

transformers

text-classification

--- language: - en license: apache-2.0 tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 model-index: - name: bhadresh-savani/distilbert-base-uncased-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.927 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzQxOGRmMjFlZThmZWViNjNmNGMzMTdjMGNjYjg1YWUzOTI0ZDlmYjRhYWMzMDA3Yjg2N2FiMTdmMzk0ZjJkOSIsInZlcnNpb24iOjF9.mOqr-hgNrnle7WCPy3Mo7M3fITFppn5gjpNagGMf_TZfB6VZnPKfZ51UkNFQlBtUlcm0U8vwPkF79snxwvCoDw - type: precision value: 0.8880230732280744 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjZiN2NjNTkyN2M3ZWM2ZDZiNDk1OWZhN2FmNTAwZDIzMmQ3NTU2Yjk2MTgyNjJmMTNjYTYzOTc1NDdhYTljYSIsInZlcnNpb24iOjF9.0rWHmCZ2PyZ5zYkSeb_tFdQG9CHS5PdpOZ9kOfrIzEXyZ968daayaOJi2d6iO84fnauE5hZiIAUPsx24Vr4nBA - type: precision value: 0.927 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmRhNWM1NDQ4ZjkyYjAxYjQ5MzQzMDA1ZDIzYWU3YTE4NTI2ZTMwYWI2ZWQ4NzQ3YzJkODYzMmZhZDI1NGRlNCIsInZlcnNpb24iOjF9.NlII1s42Mr_DMzPEoR0ntyh5cDW0405TxVkWhCgXLJTFAdnivH54-zZY4av1U5jHPTeXeWwZrrrbMwHCRBkoCw - type: precision value: 0.9272902840835793 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODhkNmM5NmYyMzA4MjkwOTllZDgyMDQ1NzZkN2QzOTAyOTMyNGFlZTU4NzM5NmM5NWQ1YmUxYmRmNjA5YjhhNCIsInZlcnNpb24iOjF9.oIn1KT-BOpFNLXiKL29frMvgHhWZMHWc9Q5WgeR7UaMEO7smkK8J3j5HAMy17Ktjv2dh783-f76N6gyJ_NewCg - type: recall value: 0.8790126653780703 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjhlNzczNDY2NDVlM2UwMjAzOWQxYTAyNWZkNGZlYmNjODNiZTEzMTcxNTE3MTAxNjNkOTFiMmRiMzViMzJmZiIsInZlcnNpb24iOjF9.AXp7omMuUZFJ6mzAVTQPMke7QoUtoi4RJSSE7Xbnp2pNi7y-JtznKdm---l6RfqcHPlI0jWr7TVGoFsWZ64YAg - type: recall value: 0.927 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjEyYmZiZDQ4MzM1ZmQ2ZmJhZWU4OTVkNmViYjA5NzhiN2MxODE0MzUxZTliZTk0MzViZDAyNGU4MDFjYjM1MSIsInZlcnNpb24iOjF9.9lazxLXbPOdwhqoYtIudwRwjfNVZnUu7KvGRklRP_RAoQStAzgmWMIrT3ckX_d5_6bKZH9fIdujUn5Qz-baKBw - type: recall value: 0.927 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWVhMzY0YTA4YmQzYTg4YTBiMzQ5YzRiZWJhMjM1NjUzZGQxZmQ5M2NkZDcyNTQ0ZmJjN2NkY2ZiYjg0OWI0ZCIsInZlcnNpb24iOjF9.QgTv726WCTyvrEct0NM8Zpc3vUnDbIwCor9EH941-zpJtuWr-xpdZzYZFJfILkVA0UUn1y6Jz_ABfkfBeyZTBg - type: f1 value: 0.8825061528287809 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzQzZTJkMDAwOTUwMzY3ZjI2MjIxYjlmZTg3YTdhNTc4ZjYyMmQ2NDQzM2FmYzk3OGEzNjhhMTk3NTQ3OTlhNyIsInZlcnNpb24iOjF9.hSln1KfKm0plK7Qao9vlubFtAl1M7_UYHNM6La9gEZlW_apnU1Mybz03GT2XZORgOVPe9JmgygvZByxQhpsYBw - type: f1 value: 0.927 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzljODQ3NjE3MDRkODE3ZjFlZmY5MjYyOGJlNDQ4YzdlZGRiMTI5OGZiZWM2ODkyZjMyZWQ3MTkzYWU5YThkOCIsInZlcnNpb24iOjF9.7qfBw39fv22jSIJoY71DkOVr9eBB-srhqSi09bCcUC7Huok4O2Z_vB7gO_Rahh9sFgKVu1ZATusjTmOLQr0fBw - type: f1 value: 0.926876082854655 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjJhN2UzODgxOWQ0Y2E3YTcwZTQxMDE0ZWRmYThjOWVhYWQ1YjBhMzk0YWUxNzE2ZjFhNWM5ZmE2ZmI1YTczYSIsInZlcnNpb24iOjF9.nZW0dBdLmh_FgNw6GaITvSJFX-2C_Iku3NanU8Rip7FSiRHozKPAjothdQh9MWQnq158ZZGPPVIjtyIvuTSqCw - type: loss value: 0.17403268814086914 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTVjZmFiOGQwZGY1OTU5YWFkNGZjMTlhOGI4NjE3MGI4ZDhkODcxYmJiYTQ3NWNmMWM0ODUyZDI1MThkYTY3ZSIsInZlcnNpb24iOjF9.OYz5BI3Lz8LgjAqVnD6NcrG3UAG0D3wjKJ7G5298RRGaNpb621ycisG_7UYiWixY7e2RJafkfRiplmkdczIFDQ --- # Distilbert-base-uncased-emotion ## Model description: [Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model. [Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters ``` learning rate 2e-5, batch size 64, num_train_epochs=8, ``` ## Model Performance Comparision on Emotion Dataset from Twitter: | Model | Accuracy | F1 Score | Test Sample per Second | | --- | --- | --- | --- | | [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | | [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 | | [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 | | [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 | ## How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ Output: [[ {'label': 'sadness', 'score': 0.0006792712374590337}, {'label': 'joy', 'score': 0.9959300756454468}, {'label': 'love', 'score': 0.0009452480007894337}, {'label': 'anger', 'score': 0.0018055217806249857}, {'label': 'fear', 'score': 0.00041110432357527316}, {'label': 'surprise', 'score': 0.0002288572577526793} ]] """ ``` ## Dataset: [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). ## Training procedure [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) ## Eval results ```json { 'test_accuracy': 0.938, 'test_f1': 0.937932884041714, 'test_loss': 0.1472451239824295, 'test_mem_cpu_alloc_delta': 0, 'test_mem_cpu_peaked_delta': 0, 'test_mem_gpu_alloc_delta': 0, 'test_mem_gpu_peaked_delta': 163454464, 'test_runtime': 5.0164, 'test_samples_per_second': 398.69 } ``` ## Reference: * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)

Rostlab/prot_t5_xl_uniref50

Rostlab

transformers

text2text-generation

--- tags: - protein language model datasets: - UniRef50 --- # ProtT5-XL-UniRef50 model Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in [this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids. ## Model description ProtT5-XL-UniRef50 is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the original T5 version is the denosing objective. The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input. It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape. shape. This implied learning some of the grammar of the language of life realized in protein sequences. ## Intended uses & limitations The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor. We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder. ### How to use Here is how to use this model to extract the features of a given protein sequence in PyTorch: ```python sequence_examples = ["PRTEINO", "SEQWENCE"] # this will replace all rare/ambiguous amino acids by X and introduce white-space between all amino acids sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", sequence))) for sequence in sequence_examples] # tokenize sequences and pad up to the longest sequence in the batch ids = tokenizer.batch_encode_plus(sequence_examples, add_special_tokens=True, padding="longest") input_ids = torch.tensor(ids['input_ids']).to(device) attention_mask = torch.tensor(ids['attention_mask']).to(device) # generate embeddings with torch.no_grad(): embedding_repr = model(input_ids=input_ids,attention_mask=attention_mask) # extract embeddings for the first ([0,:]) sequence in the batch while removing padded & special tokens ([0,:7]) emb_0 = embedding_repr.last_hidden_state[0,:7] # shape (7 x 1024) print(f"Shape of per-residue embedding of first sequences: {emb_0.shape}") # do the same for the second ([1,:]) sequence in the batch while taking into account different sequence lengths ([1,:8]) emb_1 = embedding_repr.last_hidden_state[1,:8] # shape (8 x 1024) # if you want to derive a single representation (per-protein embedding) for the whole protein emb_0_per_protein = emb_0.mean(dim=0) # shape (1024) print(f"Shape of per-protein embedding of first sequences: {emb_0_per_protein.shape}") ``` ## Training data The ProtT5-XL-UniRef50 model was pretrained on [UniRef50](https://www.uniprot.org/help/uniref), a dataset consisting of 45 million protein sequences. ## Training procedure ### Preprocessing The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X". The inputs of the model are then of the form: ``` Protein Sequence [EOS] ``` The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens. The details of the masking procedure for each sequence are as follows: - 15% of the amino acids are masked. - In 90% of the cases, the masked amino acids are replaced by `[MASK]` token. - In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace. ### Pretraining The model was trained on a single TPU Pod V2-256 for 991.5 thousand steps in total, using sequence length 512 (batch size 2k). It was trained using ProtT5-XL-BFD model as an initial checkpoint, rather than training from scratch. It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ## Evaluation results When the model is used for feature extraction, this model achieves the following results: Test results : | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane | |:-----:|:-----:|:-----:|:-----:|:-----:| | CASP12 | 81 | 70 | | | | TS115 | 87 | 77 | | | | CB513 | 86 | 74 | | | | DeepLoc | | | 81 | 91 | ### BibTeX entry and citation info ```bibtex @article {Elnaggar2020.07.12.199554, author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard}, title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing}, elocation-id = {2020.07.12.199554}, year = {2020}, doi = {10.1101/2020.07.12.199554}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554}, eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf}, journal = {bioRxiv} } ``` > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime

Mizuiro-sakura

transformers

text-classification

--- language: ja license: mit tags: - luke - sentiment-analysis - wrime - SentimentAnalysis - pytorch - sentiment-classification datasets: shunk031/wrime --- # このモデルはLuke-japanese-large-liteをファインチューニングしたものです。 このモデルは８つの感情（喜び、悲しみ、期待、驚き、怒り、恐れ、嫌悪、信頼）の内、どの感情が文章に含まれているのか分析することができます。 このモデルはwrimeデータセット（ https://huggingface.co/datasets/shunk031/wrime ）を用いて学習を行いました。 # This model is based on Luke-japanese-large-lite This model is fine-tuned model which besed on studio-ousia/Luke-japanese-large-lite. This could be able to analyze which emotions (joy or sadness or anticipation or surprise or anger or fear or disdust or trust ) are included. This model was fine-tuned by using wrime dataset. # what is Luke?　Lukeとは？[1] LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. LUKE achieves state-of-the-art results on five popular NLP benchmarks including SQuAD v1.1 (extractive question answering), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), TACRED (relation classification), and Open Entity (entity typing). luke-japaneseは、単語とエンティティの知識拡張型訓練済み Transformer モデルLUKEの日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。 # how to use 使い方 ステップ1：pythonとpytorch, sentencepieceのインストールとtransformersのアップデート（バージョンが古すぎるとLukeTokenizerが入っていないため） update transformers and install sentencepiece, python and pytorch ステップ2：下記のコードを実行する Please execute this code ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig import torch tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime") config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True) model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', config=config) text='すごく楽しかった。また行きたい。' max_seq_length=512 token=tokenizer(text, truncation=True, max_length=max_seq_length, padding="max_length") output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0)) max_index=torch.argmax(torch.tensor(output.logits)) if max_index==0: print('joy、うれしい') elif max_index==1: print('sadness、悲しい') elif max_index==2: print('anticipation、期待') elif max_index==3: print('surprise、驚き') elif max_index==4: print('anger、怒り') elif max_index==5: print('fear、恐れ') elif max_index==6: print('disgust、嫌悪') elif max_index==7: print('trust、信頼') ``` # Acknowledgments　謝辞 Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします。 I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia. # Citation [1]@inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} }

cl-tohoku/bert-base-japanese-v3

cl-tohoku

transformers

--- license: apache-2.0 datasets: - cc100 - wikipedia language: - ja widget: - text: 東北大学で[MASK]の研究をしています。 --- # BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia. For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023. The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively. For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7). ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/). ## Licenses The pretrained models are distributed under the Apache License 2.0. ## Acknowledgments This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

IAJw/declare-flan-alpaca-large-18378

IAJw

transformers

text2text-generation

Entry not found

cross-encoder/ms-marco-TinyBERT-L-2

cross-encoder

transformers

text-classification

--- license: apache-2.0 --- # Cross-Encoder for MS Marco This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) ## Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` ## Usage with SentenceTransformers The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]) ``` ## Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | **Version 2 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960 | **Version 1 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | **Other models** | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720 Note: Runtime was computed on a V100 GPU.

timm/tf_efficientnet_b7.ns_jft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for tf_efficientnet_b7.ns_jft_in1k A EfficientNet image classification model. Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 66.3 - GMACs: 38.3 - Activations (M): 289.9 - Image size: 600 x 600 - **Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946 - Self-training with Noisy Student improves ImageNet classification: https://arxiv.org/abs/1911.04252 - **Dataset:** ImageNet-1k - **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('tf_efficientnet_b7.ns_jft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b7.ns_jft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 32, 300, 300]) # torch.Size([1, 48, 150, 150]) # torch.Size([1, 80, 75, 75]) # torch.Size([1, 224, 38, 38]) # torch.Size([1, 640, 19, 19]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b7.ns_jft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 2560, 19, 19) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2019efficientnet, title={Efficientnet: Rethinking model scaling for convolutional neural networks}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={6105--6114}, year={2019}, organization={PMLR} } ``` ```bibtex @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019}, pages={10684-10695} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

google/pegasus-xsum

google

transformers

summarization

--- language: en tags: - summarization model-index: - name: google/pegasus-xsum results: - task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: train metrics: - name: ROUGE-1 type: rouge value: 21.8096 verified: true - name: ROUGE-2 type: rouge value: 4.2525 verified: true - name: ROUGE-L type: rouge value: 17.4469 verified: true - name: ROUGE-LSUM type: rouge value: 18.8907 verified: true - name: loss type: loss value: 3.0317161083221436 verified: true - name: gen_len type: gen_len value: 20.3122 verified: true - task: type: summarization name: Summarization dataset: name: xsum type: xsum config: default split: test metrics: - name: ROUGE-1 type: rouge value: 46.8623 verified: true - name: ROUGE-2 type: rouge value: 24.4533 verified: true - name: ROUGE-L type: rouge value: 39.0548 verified: true - name: ROUGE-LSUM type: rouge value: 39.0994 verified: true - name: loss type: loss value: 1.5717021226882935 verified: true - name: gen_len type: gen_len value: 22.8821 verified: true - task: type: summarization name: Summarization dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: test metrics: - name: ROUGE-1 type: rouge value: 22.2062 verified: true - name: ROUGE-2 type: rouge value: 7.6701 verified: true - name: ROUGE-L type: rouge value: 15.4046 verified: true - name: ROUGE-LSUM type: rouge value: 19.2182 verified: true - name: loss type: loss value: 2.681241273880005 verified: true - name: gen_len type: gen_len value: 25.0234 verified: true --- ### Pegasus Models See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html) Original TF 1 code [here](https://github.com/google-research/pegasus) Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: [@sshleifer](https://twitter.com/sam_shleifer) Task: Summarization The following is copied from the authors' README. # Mixed & Stochastic Checkpoints We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. | dataset | C4 | HugeNews | Mixed & Stochastic| | ---- | ---- | ---- | ----| | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64| | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30| | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18| | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95| | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76| | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *| | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94| | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *| | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67| | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25| | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51| | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59| The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). the model uniformly sample a gap sentence ratio between 15% and 45%. importance sentences are sampled using a 20% uniform noise to importance scores. the sentencepiece tokenizer is updated to be able to encode newline character. Citation ``` @misc{zhang2019pegasus, title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization}, author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu}, year={2019}, eprint={1912.08777}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sentence-transformers/distilbert-base-nli-mean-tokens

sentence-transformers

sentence-transformers

feature-extraction

--- pipeline_tag: feature-extraction license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/distilbert-base-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distilbert-base-nli-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-mean-tokens') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-base-nli-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

guillaumekln/faster-whisper-small

guillaumekln

ctranslate2

automatic-speech-recognition

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper small model for CTranslate2 This repository contains the conversion of [openai/whisper-small](https://huggingface.co/openai/whisper-small) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("small") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-small --output_dir faster-whisper-small \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-small).**