Datasets:

davanstrien

/

model_cards_with_readmes

like 1

espnet/kan-bayashi_vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_-truncated-69081b

espnet

espnet

text-to-speech

cc-by-4.0

['en']

['vctk']

['espnet', 'audio', 'text-to-speech']

## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036264/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

4899cfcf1302b7df734cd4d23c5582b5

jonatasgrosman/exp_w2v2t_et_unispeech_s605

jonatasgrosman

unispeech

transformers

automatic-speech-recognition

apache-2.0

['et']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'et']

# exp_w2v2t_et_unispeech_s605 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

2c04c4b3fc72a1f7e1280d8b1fbf70b8

id2223lab1/whisper-small

id2223lab1

whisper

transformers

automatic-speech-recognition

apache-2.0

['sv']

['mozilla-foundation/common_voice_11_0']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- 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. --> # Whisper Small Sv - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

5ea259c3678ebc0a97c56a339651fbff

CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment

CAMeL-Lab

bert

transformers

text-classification

apache-2.0

['ar']

[]

# CAMeLBERT Mix SA Model ## Model description **CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model. For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf), and [SemEval](https://aclanthology.org/S17-2088.pdf) datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). ## Intended uses You can use the CAMeLBERT Mix SA model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component (*recommended*) or as part of the transformers pipeline. #### How to use To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component: ```python >>> from camel_tools.sentiment import SentimentAnalyzer >>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment") >>> sentences = ['أنا بخير', 'أنا لست بخير'] >>> sa.predict(sentences) >>> ['positive', 'negative'] ``` You can also use the SA model directly with a transformers pipeline: ```python >>> from transformers import pipeline >>> sa = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment') >>> sentences = ['أنا بخير', 'أنا لست بخير'] >>> sa(sentences) [{'label': 'positive', 'score': 0.9616648554801941}, {'label': 'negative', 'score': 0.9779177904129028}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

517a929a3bb5c286d3aa0321a8c9dade

TransQuest/monotransquest-da-any_en

TransQuest

xlm-roberta

transformers

text-classification

apache-2.0

['multilingual-en']

['Quality Estimation', 'monotransquest', 'DA']

# TransQuest: Translation Quality Estimation with Cross-lingual Transformers The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest). ## Features - Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment. - Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps. - Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. - Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest) ## Installation ### From pip ```bash pip install transquest ``` ### From Source ```bash git clone https://github.com/TharinduDR/TransQuest.git cd TransQuest pip install -r requirements.txt ``` ## Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-any_en", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ``` ## Documentation For more details follow the documentation. 1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip. 2. **Architectures** - Checkout the architectures implemented in TransQuest 1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation. 2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks. 1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/) 2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/) 4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level 1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/) 2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/) 5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest ## Citations If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/). ```bash @InProceedings{ranasinghe2021, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics}, year = {2021} } ``` If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020. ```bash @InProceedings{transquest:2020a, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, year = {2020} } ``` ```bash @InProceedings{transquest:2020b, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest at WMT2020: Sentence-Level Direct Assessment}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, year = {2020} } ```

48c214e0d78fa9789b79a67aba1ca896

tftransformers/gpt2-medium

tftransformers

mit

['en']

['exbert']

# GPT-2 Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very 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 trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. 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. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use 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 tf_transformers.models import GPT2Model from transformers import GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium') model = GPT2Model.from_pretrained("gpt2-medium") text = "Replace me by any text you'd like." inputs_tf = {} inputs = tokenizer(text, return_tensors='tf') inputs_tf["input_ids"] = inputs["input_ids"] outputs_tf = model(inputs_tf) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 | ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

b22c73c4d7cae1cc8323f465b146fed8

Bhumika-kumaran/t5-small-finetuned-xsum

Bhumika-kumaran

t5

transformers

text2text-generation

apache-2.0

['xsum']

['generated_from_trainer']

<!-- 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. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4789 - Rouge1: 28.2786 - Rouge2: 7.6957 - Rougel: 22.1976 - Rougelsum: 22.2034 - Gen Len: 18.8238 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7189 | 1.0 | 12753 | 2.4789 | 28.2786 | 7.6957 | 22.1976 | 22.2034 | 18.8238 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

6529807d24f27e1f36f201e38771df35

qBob/t5-small_corrector_15

qBob

bart

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- 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. --> # t5-small_corrector_15 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3416 - Rouge1: 34.7998 - Rouge2: 9.0842 - Rougel: 27.8188 - Rougelsum: 27.839 - Gen Len: 18.5561 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 4.2274 | 1.0 | 2365 | 2.9386 | 10.1244 | 1.0024 | 9.1029 | 9.1104 | 18.5377 | | 2.7936 | 2.0 | 4730 | 2.0196 | 17.7168 | 3.0899 | 15.1305 | 15.1353 | 18.8883 | | 2.2678 | 3.0 | 7095 | 1.7072 | 26.8501 | 5.7804 | 22.0034 | 22.0213 | 18.839 | | 1.9029 | 4.0 | 9460 | 1.5254 | 32.9484 | 7.8531 | 26.4538 | 26.4749 | 18.502 | | 1.5936 | 5.0 | 11825 | 1.3416 | 34.7998 | 9.0842 | 27.8188 | 27.839 | 18.5561 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

537dc6c065f1123323969c6c4e0d9957

saltacc/anime-ai-detect

saltacc

beit

transformers

image-classification

apache-2.0

[]

# Anime AI Art Detect A BEiT classifier to see if anime art was made by an AI or a human. ### Disclaimer Like most AI models, this classifier is not 100% accurate. Please do not take the results of this model as fact. The best version had a 96% accuracy distinguishing aibooru and the images from the imageboard sites. However, the success you have with this model will vary based on the images you are trying to classify. Here are some biases I have noticed from my testing: - Images on aibooru, the site where the AI images were taken from, were high quality AI generations. Low quality AI generations have a higher chance of being misclassified - Textual inversions and hypernetworks increase the chance of misclassification ### Training This model was trained from microsoft/beit-base-patch16-224 for one epoch on 11 thousand images from imageboard sites, and 11 thousand images from aibooru. You can view the wandb run [here](https://wandb.ai/saltacc/huggingface/runs/2mp30x7j?workspace=user-saltacc). ### Use Case I don't intend for this model to be more accurate than humans for detecting AI art. I think the best use cases for this model would be for cases where misclassification isn't a big deal, such as removing AI art from a training dataset.

08e253f20fca6182189adcd9bc267c6b

Helsinki-NLP/opus-mt-vi-ru

Helsinki-NLP

marian

transformers

translation

apache-2.0

['vi', 'ru']

['translation']

### vie-rus * source group: Vietnamese * target group: Russian * OPUS readme: [vie-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-rus/README.md) * model: transformer-align * source language(s): vie * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-rus/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-rus/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-rus/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.vie.rus | 16.9 | 0.331 | ### System Info: - hf_name: vie-rus - source_languages: vie - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'ru'] - src_constituents: {'vie', 'vie_Hani'} - tgt_constituents: {'rus'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-rus/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-rus/opus-2020-06-17.test.txt - src_alpha3: vie - tgt_alpha3: rus - short_pair: vi-ru - chrF2_score: 0.331 - bleu: 16.9 - brevity_penalty: 0.878 - ref_len: 2207.0 - src_name: Vietnamese - tgt_name: Russian - train_date: 2020-06-17 - src_alpha2: vi - tgt_alpha2: ru - prefer_old: False - long_pair: vie-rus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

d78fae59a6a43229848e3db4e553d9e3

ghatgetanuj/distilbert-base-uncased_cls_bbc-news

ghatgetanuj

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # distilbert-base-uncased_cls_bbc-news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1140 - Accuracy: 0.976 ## 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: 4e-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: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 77 | 0.2531 | 0.944 | | No log | 2.0 | 154 | 0.0971 | 0.973 | | No log | 3.0 | 231 | 0.0951 | 0.977 | | No log | 4.0 | 308 | 0.1166 | 0.975 | | No log | 5.0 | 385 | 0.1140 | 0.976 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

3bfdd074783eaee8c994557bb6485bdc

sd-concepts-library/paolo-bonolis

sd-concepts-library

mit

[]

### paolo bonolis on Stable Diffusion This is the `<paolo-bonolis>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:    

d451c2fe65cd1a793abafeb885cc5162

Tahsin-Mayeesha/distilbert-finetuned-fakenews

Tahsin-Mayeesha

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # distilbert-finetuned-fakenews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Accuracy: 0.9995 - F1: 0.9995 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0392 | 1.0 | 500 | 0.0059 | 0.999 | 0.999 | | 0.002 | 2.0 | 1000 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 3.0 | 1500 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 4.0 | 2000 | 0.0049 | 0.9995 | 0.9995 | | 0.0 | 5.0 | 2500 | 0.0049 | 0.9995 | 0.9995 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0

2bf46d3bff64ff41189e2063fe829da2

anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2

anas-awadalla

bert

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- 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. --> # bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## 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: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

5920fa037692a92921c0d626d3e099a5

PrimeQA/squad-v1-roberta-large

PrimeQA

roberta

transformers

apache-2.0

['en']

['MRC', 'SQuAD 1.1', 'roberta-large']

# Model description An RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/). The model is initialized with [roberta-large](https://huggingface.co/roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad). ## Intended uses & limitations You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, roberta-large, that we used may be present in our fine-tuned model, squad-v1-roberta-large. ## Usage You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [squad.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/squad.ipynb). ```bibtex @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } ``` ```bibtex @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

10caaec961fab91dfb7d1f118001d04b

jed351/gpt2_base_zh-hk-shikoto

jed351

gpt2

transformers

text-generation

openrail

['jed351/shikoto_zh_hk']

['generated_from_trainer']

# gpt2-shikoto This model was trained on a dataset I obtained from an online novel site. **Please be aware that the stories (training data) might contain inappropriate content. This model is intended for research purposes only.** The base model can be found [here](https://huggingface.co/jed351/gpt2-base-zh-hk), which was obtained by patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-base-chinese) and its tokenizer with Cantonese characters. Refer to the base model for info on the patching process. Besides language modeling, another aim of this experiment was to test the accelerate library by offloading certain workloads to CPU as well as finding the optimal training iterations. The perplexity of this model is 16.12 after 400,000 steps. Comparing to the previous [attempt](https://huggingface.co/jed351/gpt2_tiny_zh-hk-shikoto) 27.02 after 400,000 steps. It took around the same time duration to train this model but I only used 1 GPU here. ## Training procedure Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) provided by Huggingface. The model was trained for 400,000 steps on 1 NVIDIA Quadro RTX6000 for around 30 hours at the Research Computing Services of Imperial College London. ### How to use it? ``` from transformers import AutoTokenizer from transformers import TextGenerationPipeline, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-base-zh-hk") model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-shikoto") # try messing around with the parameters generator = TextGenerationPipeline(model, tokenizer, max_new_tokens=200, no_repeat_ngram_size=3) #, device=0) #if you have a GPU input_string = "your input" output = generator(input_string) string = output[0]['generated_text'].replace(' ', '') print(string) ``` ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2

9e2e3f32ca83e03a44e707078fa51d85

lmqg/t5-base-squad-qg-ae

lmqg

t5

transformers

text2text-generation

cc-by-4.0

['en']

['lmqg/qg_squad']

['question generation', 'answer extraction']

# Model Card of `lmqg/t5-base-squad-qg-ae` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/t5-base-squad-qg-ae") # model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-qg-ae") # answer extraction answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") # question generation question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 58.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 42.6 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 32.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 26.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 27 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 64.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 53.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.35 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 64.33 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 92.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 64.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:---------------------------------------------------------------| | AnswerExactMatch | 58.9 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | AnswerF1Score | 70.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | BERTScore | 91.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 56.96 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 52.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 48.21 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 44.33 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 43.94 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 82.16 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 69.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

8adb4cd15fd68dde85bad6ec8bef864f

jhonparra18/wav2vec2-large-xls-r-300m-guarani-small

jhonparra18

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice', 'gn']

['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard']

<!-- 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. --> # wav2vec2-large-xls-r-300m-guarani-small This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4964 - Wer: 0.5957 ## 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 6.65 | 100 | 1.1326 | 1.0 | | 1.6569 | 13.32 | 200 | 0.5264 | 0.6478 | | 1.6569 | 19.97 | 300 | 0.5370 | 0.6261 | | 0.2293 | 26.65 | 400 | 0.4964 | 0.5957 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

89084ab4ada687b0d08974c0b690c2ff

umanlp/TOD-XLMR

umanlp

xlm-roberta

transformers

fill-mask

mit

['multilingual']

['exbert']

# TOD-XLMR TOD-XLMR is a conversationally specialized multilingual version based on [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base). It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://aclanthology.org/2020.emnlp-main.66.pdf) by Wu et al. and first released in [this repository](https://huggingface.co/TODBERT). The model is jointly trained with two objectives as proposed in TOD-BERT, including masked language modeling (MLM) and response contrastive loss (RCL). Masked language modeling is a common pretraining strategy utilized for BERT-based architectures, where a random sample of tokens in the input sequence is replaced with the special token [MASK] for predicting the original masked tokens. To further encourage the model to capture dialogic structure (i.e., dialog sequential order), response contrastive loss is implemented by using in-batch negative training with contrastive learning. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") model = AutoModelForMaskedLM.from_pretrained("umanlp/TOD-XLMR") # prepare input text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') # forward pass output = model(**encoded_input) ``` Or you can also use `AutoModel` to load the pretrained model and further apply to downstream tasks: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") model = AutoModel("umanlp/TOD-XLMR") # prepare input text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') # forward pass output = model(**encoded_input) ```

807a7568ac22dac3c1fed427331e6635

takizawa/xlm-roberta-base-finetuned-panx-all

takizawa

xlm-roberta

transformers

token-classification

mit

['generated_from_trainer']

<!-- 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## 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: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

a13bd67713eb6ed2e499fa4082dc6390

Axon/resnet50-v1

Axon

apache-2.0

['ImageNet']

['Axon', 'Elixir']

# ResNet This ResNet50 model was translated from the ONNX ResNetv1 model found at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx) The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code. ## Dataset Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset. ## References * **ResNetv1** [Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385) He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * **ONNX source model** [onnx/models vision/classification/resnet resnet50-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)

c955c219ad4d42cf0f281bbec9143501

Helsinki-NLP/opus-mt-lt-eo

Helsinki-NLP

marian

transformers

translation

apache-2.0

['lt', 'eo']

['translation']

### lit-epo * source group: Lithuanian * target group: Esperanto * OPUS readme: [lit-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-epo/README.md) * model: transformer-align * source language(s): lit * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.lit.epo | 13.0 | 0.313 | ### System Info: - hf_name: lit-epo - source_languages: lit - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'eo'] - src_constituents: {'lit'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.test.txt - src_alpha3: lit - tgt_alpha3: epo - short_pair: lt-eo - chrF2_score: 0.313 - bleu: 13.0 - brevity_penalty: 1.0 - ref_len: 70340.0 - src_name: Lithuanian - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: lt - tgt_alpha2: eo - prefer_old: False - long_pair: lit-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

3998f82e48c3f1c376d7c19c2b4f5bfd

jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s607

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fr']

# exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s607 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d177a512933125efe58013a57e1ca8a8

tomaccer/flan-t5-base-juraqanda

tomaccer

t5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- 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. --> # flan-t5-base-juraqanda This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0784 - Rouge1: 9.5491 - Rouge2: 1.4927 - Rougel: 8.828 - Rougelsum: 9.2708 - Gen Len: 18.5260 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 4.0303 | 1.0 | 712 | 3.3466 | 9.4455 | 1.2684 | 8.8558 | 9.1832 | 18.7577 | | 3.6049 | 2.0 | 1424 | 3.1931 | 10.0714 | 1.4116 | 9.4163 | 9.8024 | 18.6461 | | 3.3464 | 3.0 | 2136 | 3.1246 | 9.6542 | 1.4317 | 8.9441 | 9.36 | 18.5485 | | 3.2831 | 4.0 | 2848 | 3.0910 | 9.6676 | 1.4584 | 8.9533 | 9.3876 | 18.6706 | | 3.2176 | 5.0 | 3560 | 3.0784 | 9.5491 | 1.4927 | 8.828 | 9.2708 | 18.5260 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

de31b18e67a0f0c07135c5a640d9d6f3

virto/mt5-base-finetuned-rabbi-kook

virto

mt5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- 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. --> # mt5-base-finetuned-rabbi-kook This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3861 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2102 | 1.0 | 3567 | 2.4526 | | 3.0283 | 2.0 | 7134 | 2.3861 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.11.0

5fe8c303b36bdb894f9e870bcde4795a

bkim12/t5-small-finetuned-eli5

bkim12

t5

transformers

text2text-generation

apache-2.0

['eli5']

['generated_from_trainer']

<!-- 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. --> # t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.6782 - Rouge1: 13.0163 - Rouge2: 1.9263 - Rougel: 10.484 - Rougelsum: 11.8234 - Gen Len: 18.9951 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 3.8841 | 1.0 | 17040 | 3.6782 | 13.0163 | 1.9263 | 10.484 | 11.8234 | 18.9951 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1

b0f8c85527c9bf6243bc7b1ac6b05d27

sztanki/white-walker-style

sztanki

diffusers

text-to-image

creativeml-openrail-m

['text-to-image']

### white-walker-style Dreambooth model trained by sztanki with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: white (use that on your prompt) 

4b24d3bca1fd4927cc4cdd24b4c468ce

DeepaKrish/roberta-base-squad2-finetuned

DeepaKrish

roberta

transformers

question-answering

cc-by-4.0

['generated_from_trainer']

<!-- 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. --> # roberta-base-squad2-finetuned This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 0.0023 | | No log | 2.0 | 54 | 0.0010 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.9.0 - Datasets 2.5.1 - Tokenizers 0.13.2

1038ace3d7ed3cfd19b6cce32a56bcad

jraramhoej/whisper-small-lt-sr-v2

jraramhoej

whisper

transformers

automatic-speech-recognition

apache-2.0

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

# Whisper Small Lithuanian and Serbian sequentially trained This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: ### Lithuanian - Wer: >100 ### Serbian - Wer: 35.6131 ## Training procedure It was first trained 2000 steps on Lithuanian and then 2000 steps on Serbian, continuing from the last checkpoint for Lithuanian. ### Training hyperparameters per fine-tune The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

f079deb538048451c9a23166679d15f9

SherlockHolmes/ddpm-butterflies-128

SherlockHolmes

diffusers

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/SherlockHolmes/ddpm-butterflies-128/tensorboard?#scalars)

0d1ababa80f2d4d97660933f5b9d1e23

fathyshalab/all-roberta-large-v1-banking-16-16-5

fathyshalab

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # all-roberta-large-v1-banking-16-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

11c4bea0e6c0601bfc564b39760a81ab

tomekkorbak/dreamy_poitras

tomekkorbak

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

['generated_from_trainer']

<!-- 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. --> # dreamy_poitras This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 12588 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'}, 'num_additional_tokens': 2, 'path_or_name': 'tomekkorbak/nervous_wozniak'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'dreamy_poitras', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1b5oov3g

af2560deb5f15fa1dca9ef81ac72e0e8

gngpostalsrvc/BERiT_27000

gngpostalsrvc

roberta

transformers

fill-mask

mit

['generated_from_trainer']

<!-- 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. --> # BERiT_27000 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.3744 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.7297 | 0.19 | 500 | 8.5541 | | 8.5592 | 0.39 | 1000 | 8.5536 | | 8.4892 | 0.58 | 1500 | 8.5554 | | 8.5288 | 0.77 | 2000 | 8.4786 | | 8.5034 | 0.97 | 2500 | 8.4756 | | 8.3497 | 1.16 | 3000 | 8.4821 | | 8.4516 | 1.36 | 3500 | 8.4742 | | 8.4224 | 1.55 | 4000 | 8.3972 | | 8.3356 | 1.74 | 4500 | 8.4158 | | 8.3805 | 1.94 | 5000 | 8.3800 | | 8.2947 | 2.13 | 5500 | 8.4242 | | 8.2475 | 2.32 | 6000 | 8.4334 | | 8.2708 | 2.52 | 6500 | 8.3504 | | 8.2559 | 2.71 | 7000 | 8.4211 | | 8.3676 | 2.9 | 7500 | 8.3744 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

ff57e3b382b4f6de6a1d0a0810876e9d

ireneisdoomed/clinical_trial_stop_reasons_custom

ireneisdoomed

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- 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. --> # clinical_trial_stop_reasons_custom This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy Thresh: 0.9570 - F1 Micro: 0.5300 - F1 Macro: 0.1254 - Confusion Matrix: [[5940 15] [ 270 150]] ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh | F1 Micro | F1 Macro | Confusion Matrix | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:--------:|:--------:|:--------------------------:| | No log | 1.0 | 106 | 0.2812 | 0.8328 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 2.0 | 212 | 0.2189 | 0.9382 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 3.0 | 318 | 0.1840 | 0.9489 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 4.0 | 424 | 0.1638 | 0.9485 | 0.4940 | 0.0989 | [[5943 12] [ 288 132]] | | 0.239 | 5.0 | 530 | 0.1526 | 0.9533 | 0.5060 | 0.1018 | [[5943 12] [ 277 143]] | | 0.239 | 6.0 | 636 | 0.1467 | 0.9564 | 0.5077 | 0.1020 | [[5938 17] [ 275 145]] | | 0.239 | 7.0 | 742 | 0.1448 | 0.9570 | 0.5300 | 0.1254 | [[5940 15] [ 270 150]] | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2

05d074cacd121b04b4e0d879f6ae0be4

yuhuizhang/finetuned_gpt2_sst2_negation0.2_pretrainedFalse

yuhuizhang

gpt2

transformers

text-generation

mit

['sst2']

['generated_from_trainer']

<!-- 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. --> # finetuned_gpt2_sst2_negation0.2_pretrainedFalse This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 5.3370 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9034 | 1.0 | 1072 | 5.5636 | | 4.5404 | 2.0 | 2144 | 5.3854 | | 4.368 | 3.0 | 3216 | 5.3370 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.13.1+cu117 - Datasets 2.5.2 - Tokenizers 0.12.1

29f3b953704ebca44f2728eb8bc23c39

IDEA-CCNL/Randeng-TransformerXL-1.1B-Paraphrasing-Chinese

IDEA-CCNL

transformers

apache-2.0

['zh']

[]

# Randeng-Transformer-1.1B-Denoise - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 基于Transformer-XL的中文句子改写。 Paraphrase Chinese sentences based on Transformer-XL. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | Transformer | 1.1B | 中文-改写 Chinese-Paraphrase | ## 模型信息 Model Information 在悟道语料库(280G版本)和标注的相似句子对数据集上进行预训练。 The Transformer-XL model was pre-trained on the Wudao Corpus (with 280G samples) and annotated similar-sentence pair dataset. ## 使用 Usage ### 加载模型 Loading Models ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ```python from fengshen.models.transfo_xl_paraphrase import TransfoXLModel from transformers import T5Tokenizer as TransfoXLTokenizer model = TransfoXLModel.from_pretrained('IDEA-CCNL/Randeng-TransformerXL-1.1B-Paraphrasing-Chinese') tokenizer = TransfoXLTokenizer.from_pretrained('IDEA-CCNL/Randeng-TransformerXL-1.1B-Paraphrasing-Chinese', eos_token = '<|endoftext|>', extra_ids=0) ``` ### 使用示例 Usage Examples ```python from fengshen.models.transfo_xl_paraphrase import paraphrase_generatete input_text = "年轻教师选择农村学校，还是县城学校？" res = paraphrase_generate(model, tokenizer, input_text, device=0) print(res) # ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

848a8cf1d5ae5cbfba038f975c4bfc0b

espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave

espnet

espnet

text-to-speech

cc-by-4.0

['ja']

['jsut']

['espnet', 'audio', 'text-to-speech']

## Example ESPnet2 TTS model ### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4381098/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

81bd86633f8906da14ed721328936003

gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_wnli

gokuls

mobilebert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- 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. --> # mobilebert_add_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.5634 ## 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: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0 | 1.0 | 5 | nan | 0.5634 | | 0.0 | 2.0 | 10 | nan | 0.5634 | | 0.0 | 3.0 | 15 | nan | 0.5634 | | 0.0 | 4.0 | 20 | nan | 0.5634 | | 0.0 | 5.0 | 25 | nan | 0.5634 | | 0.0 | 6.0 | 30 | nan | 0.5634 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

f97fae05e8869f582f2acc4dc5a4dac9

voidful/asr_hubert_cluster_bart_base

voidful

bart

transformers

text2text-generation

apache-2.0

['en']

['librispeech']

['audio', 'automatic-speech-recognition', 'speech', 'asr', 'hubert']

# voidful/asr_hubert_cluster_bart_base ## Usage download file ```shell wget https://raw.githubusercontent.com/voidful/hubert-cluster-code/main/km_feat_100_layer_20 wget https://cdn-media.huggingface.co/speech_samples/sample1.flac ``` Hubert kmeans code ```python import joblib import torch from transformers import Wav2Vec2FeatureExtractor, HubertModel import soundfile as sf class HubertCode(object): def __init__(self, hubert_model, km_path, km_layer): self.processor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model) self.model = HubertModel.from_pretrained(hubert_model) self.km_model = joblib.load(km_path) self.km_layer = km_layer self.C_np = self.km_model.cluster_centers_.transpose() self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True) self.C = torch.from_numpy(self.C_np) self.Cnorm = torch.from_numpy(self.Cnorm_np) if torch.cuda.is_available(): self.C = self.C.cuda() self.Cnorm = self.Cnorm.cuda() self.model = self.model.cuda() def __call__(self, filepath, sampling_rate=None): speech, sr = sf.read(filepath) input_values = self.processor(speech, return_tensors="pt", sampling_rate=sr).input_values if torch.cuda.is_available(): input_values = input_values.cuda() hidden_states = self.model(input_values, output_hidden_states=True).hidden_states x = hidden_states[self.km_layer].squeeze() dist = ( x.pow(2).sum(1, keepdim=True) - 2 * torch.matmul(x, self.C) + self.Cnorm ) return dist.argmin(dim=1).cpu().numpy() ``` input ```python hc = HubertCode("facebook/hubert-large-ll60k", './km_feat_100_layer_20', 20) voice_ids = hc('./sample1.flac') ``` bart model ````python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/asr_hubert_cluster_bart_base") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/asr_hubert_cluster_bart_base") ```` generate output ```python gen_output = model.generate(input_ids=tokenizer("".join([f":vtok{i}:" for i in voice_ids]),return_tensors='pt').input_ids,max_length=1024) print(tokenizer.decode(gen_output[0], skip_special_tokens=True)) ``` ## Result `going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards`

98713a57a8897596bb488c011abe759c

takizawa/xlm-roberta-base-finetuned-panx-fr

takizawa

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- 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. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2763 - F1: 0.8346 ## 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: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 | | 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 | | 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

4f181cf957d8ea65f526de767d7df9c5

rossanez/t5-small-finetuned-de-en-256-lr2e-4

rossanez

t5

transformers

text2text-generation

apache-2.0

['wmt14']

['generated_from_trainer']

<!-- 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. --> # t5-small-finetuned-de-en-256-lr2e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## 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: 0.0002 - 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1169 | 7.6948 | 17.4103 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

4c01d75c95f5e1becbbead76feab9750

gwz0202/ddpm-butterflied-128

gwz0202

diffusers

apache-2.0

['en']

['huggan/few-shot-pokemon']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflied-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/few-shot-pokemon` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/gwz0202/ddpm-butterflied-128/tensorboard?#scalars)

e199738c3a29ab2f9338929adee82fb1

sshreshtha/vit-base-patch32-224-in21k-finetuned-eurosat

sshreshtha

vit

transformers

image-classification

apache-2.0

['food101']

['generated_from_trainer']

<!-- 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. --> # vit-base-patch32-224-in21k-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6175 - Accuracy: 0.7321 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6483 | 1.0 | 532 | 2.5574 | 0.6605 | | 1.8885 | 2.0 | 1064 | 1.8063 | 0.7182 | | 1.6371 | 3.0 | 1596 | 1.6175 | 0.7321 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

342511488ef5d9a04bd0005d085ce1f3

Langboat/mengzi-t5-base-mt

Langboat

t5

transformers

text2text-generation

apache-2.0

['zh']

[]

# Mengzi-T5-MT model This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on [Mengzi-T5-base](https://huggingface.co/Langboat/mengzi-t5-base). [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696) ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base-mt") model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base-mt") ``` ## Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. ``` @misc{zhang2021mengzi, title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou}, year={2021}, eprint={2110.06696}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

510aab3ff0cf225c7fb10d91c0acda24

prows12/wav2vec2-base-timit-demo-test_jong

prows12

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- 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. --> # wav2vec2-base-timit-demo-test_jong This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3

0752c6b9af06ecbc2a8bc8a264b14cfb

erickfm/t5-base-finetuned-bias

erickfm

t5

transformers

text2text-generation

apache-2.0

['en']

['WNC']

[]

This model is a fine-tune checkpoint of [T5-base](https://huggingface.co/t5-base), fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model reaches an accuracy of 0.39 on a dev split of the WNC. For more details about T5, check out this [model card](https://huggingface.co/t5-base).

18a263ee95e823383d45d2ed08619b57

lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05

lixiqi

beit

transformers

image-classification

apache-2.0

['image_folder']

['generated_from_trainer']

<!-- 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. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.7881 - Accuracy: 0.7221 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2307 | 1.0 | 224 | 1.0863 | 0.5874 | | 1.0893 | 2.0 | 448 | 0.9700 | 0.6362 | | 1.0244 | 3.0 | 672 | 0.8859 | 0.6757 | | 1.016 | 4.0 | 896 | 0.8804 | 0.6787 | | 0.9089 | 5.0 | 1120 | 0.8611 | 0.6897 | | 0.8935 | 6.0 | 1344 | 0.8283 | 0.7028 | | 0.8403 | 7.0 | 1568 | 0.8116 | 0.7102 | | 0.8179 | 8.0 | 1792 | 0.7934 | 0.7166 | | 0.7764 | 9.0 | 2016 | 0.7865 | 0.7208 | | 0.771 | 10.0 | 2240 | 0.7881 | 0.7221 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

aa77eb57ac76e6111ebc339ca918807f

stinoco/distilbert-base-uncased-finetuned-emotion

stinoco

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2284 - Accuracy: 0.9195 - F1: 0.9195 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8441 | 1.0 | 250 | 0.3260 | 0.9 | 0.8970 | | 0.2551 | 2.0 | 500 | 0.2284 | 0.9195 | 0.9195 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

ad5f8c9a207a7fd932c02d3e4d1bf1d8

jonatasgrosman/exp_w2v2t_id_xlsr-53_s149

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['id']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'id']

# exp_w2v2t_id_xlsr-53_s149 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

7db6b398e50b9f39a390ef017720abf5

Lykon/DreamShaper

Lykon

diffusers

text-to-image

other

['en']

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers']

# Dream Shaper ## Official Repository Read more about this model here: https://civitai.com/models/4384/dreamshaper You can run this model on: - https://huggingface.co/spaces/Lykon/DreamShaper-webui - https://sinkin.ai/m/4zdwGOB Some sample output:     

6b12ecdb5e52f7a94b04627714b0e87a

hucruz/custom-textcat-model-viajes

hucruz

bert

transformers

text-classification

apache-2.0

['text-classification', 'generated_from_trainer']

<!-- 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. --> # custom-textcat-model This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the custom dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3305 - Accuracy: 0.9541 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 209 | 0.3650 | 0.9514 | | No log | 2.0 | 418 | 0.3371 | 0.9568 | | 0.0108 | 3.0 | 627 | 0.3305 | 0.9541 | | 0.0108 | 4.0 | 836 | 0.3465 | 0.9568 | | 0.0056 | 5.0 | 1045 | 0.3498 | 0.9541 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ea32d5e14f2ea16c3526d584f4eb7e43

sashketka/en_food_entity_extractor_v2

sashketka

spacy

token-classification

mit

['en']

['spacy', 'token-classification']

English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_food_entity_extractor_v2` | | **Version** | `3.4.1` | | **spaCy** | `>=3.4.0,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (114 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `FOOD`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.93 | | `TOKEN_P` | 99.57 | | `TOKEN_R` | 99.58 | | `TOKEN_F` | 99.57 | | `TAG_ACC` | 97.34 | | `SENTS_P` | 91.79 | | `SENTS_R` | 89.14 | | `SENTS_F` | 90.44 | | `DEP_UAS` | 92.04 | | `DEP_LAS` | 90.23 | | `ENTS_P` | 85.35 | | `ENTS_R` | 85.93 | | `ENTS_F` | 85.64 |

4bc497f1299e6b916c7bcad9cb32c5af

Shenghao1993/xlm-roberta-base-finetuned-panx-it

Shenghao1993

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2403 - F1: 0.8358 ## 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: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7053 | 1.0 | 70 | 0.3077 | 0.7587 | | 0.2839 | 2.0 | 140 | 0.2692 | 0.8007 | | 0.1894 | 3.0 | 210 | 0.2403 | 0.8358 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

d688148aba1dc75fca23926a0235a120

dptrsa/ec_model

dptrsa

roberta

transformers

fill-mask

apache-2.0

['generated_from_trainer']

<!-- 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. --> # ec_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9323 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 497 | 1.1985 | | 1.578 | 2.0 | 994 | 1.0032 | | 1.187 | 3.0 | 1491 | 0.9479 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ee490705b13c141cc3ba0945cc7f03be

pere/nb-nn-translation

pere

translation

cc-by-4.0

False

['oscar']

['translation']

# 🇳🇴 Bokmål ⇔ Nynorsk 🇳🇴 Norwegian has two relatively similar written languages; Bokmål and Nynorsk. Historically Nynorsk is a written norm based on dialects curated by the linguist Ivar Aasen in the mid-to-late 1800s, whereas Bokmål is a gradual 'Norwegization' of written Danish. The two written languages are considered equal and citizens have a right to receive public service information in their primary and prefered language. Even though this right has been around for a long time only between 5-10% of Norwegian texts are written in Nynorsk. Nynorsk is therefore a low-resource language within a low-resource language. Apart from some word-list based engines, there are not any working off-the-shelf machine learning-based translation models. Translation between Bokmål and Nynorsk is not available in Google Translate. ## Demo | | | |---|---| | Widget | Try the widget in the top right corner | | Huggingface Spaces | [Spaces Demo](https://huggingface.co/spaces/NbAiLab/nb2nn) | | | | ## Pretraining a T5-base There is an [mt5](https://huggingface.co/google/mt5-base) that includes Norwegian. Unfortunately a very small part of this is Nynorsk; there is only around 1GB Nynorsk text in mC4. Despite this, the mt5 also gives a BLEU score above 80. During the project we extracted all available Nynorsk text from the [Norwegian Colossal Corpus](https://github.com/NBAiLab/notram/blob/master/guides/corpus_v2_summary.md) at the National Library of Norway, and matched it (by material type i.e. book, newspapers and so on) with an equal amount of Bokmål. The corpus collection is described [here](https://github.com/NBAiLab/notram/blob/master/guides/nb_nn_balanced_corpus.md) and the total size is 19GB. ## Finetuning - BLEU-SCORE 88.17 🎉 The central finetuning data of the project have been 200k translation units (TU) i.e. aligned pairs of sentences in the respective languages extracted from textbooks of various subjects and newspapers. Training for [10] epochs with a learning rate of [7e-4], a batch size of [32] and a max source and target length of [512] fine tuning reached a SACREBLEU score of [88.03] at training and a test score of [**88.17**] after training. ## This is not a translator We found out that we were able to get almost identical BLEU-score with training it both ways, and letting the model decide if the input is in Bokmål or Nynorsk. This way we can train one model instead of two. We call it a language switcher. ## Future work The following Google Docs Add-on is currently pending approval.  ## How to use the model ```python # Set up the pipeline from transformers import pipeline translator = pipeline("translation", model='pere/nb-nn-translation') # Do the translation text = "Hun vil ikke gi bort sine personlige data." print(translator(text, max_length=255)) ```

e79e2c6eb877c76263039bfaa607d137

cwchengtw/wav2vec2-large-xls-r-300m-turkish-colab2

cwchengtw

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer']

<!-- 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. --> # wav2vec2-large-xls-r-300m-turkish-colab2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3738 - Wer: 0.3532 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9022 | 3.7 | 400 | 0.6778 | 0.7414 | | 0.4106 | 7.4 | 800 | 0.4123 | 0.5049 | | 0.1862 | 11.11 | 1200 | 0.4260 | 0.4232 | | 0.1342 | 14.81 | 1600 | 0.3951 | 0.4097 | | 0.0997 | 18.51 | 2000 | 0.4100 | 0.3999 | | 0.0782 | 22.22 | 2400 | 0.3918 | 0.3875 | | 0.059 | 25.92 | 2800 | 0.3803 | 0.3698 | | 0.0474 | 29.63 | 3200 | 0.3738 | 0.3532 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

ba810a8a5e0da8cd2ce5937aefe5fb39

Prang9/distilbert-base-uncased-finetuned-imdb

Prang9

distilbert

transformers

fill-mask

apache-2.0

['imdb']

['generated_from_trainer']

<!-- 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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

d63af670b27c7df680821f06c5912ae6

mmillet/rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis

mmillet

bert

transformers

text-classification

mit

['generated_from_trainer']

<!-- 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. --> # rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5820 - Accuracy: 0.7881 - F1: 0.7886 - Precision: 0.7906 - Recall: 0.7881 ## 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: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0996 | 1.0 | 69 | 1.0013 | 0.6879 | 0.6779 | 0.7070 | 0.6879 | | 0.9524 | 2.0 | 138 | 0.8651 | 0.7265 | 0.7245 | 0.7322 | 0.7265 | | 0.8345 | 3.0 | 207 | 0.7821 | 0.7422 | 0.7413 | 0.7445 | 0.7422 | | 0.7573 | 4.0 | 276 | 0.7222 | 0.7484 | 0.7473 | 0.7482 | 0.7484 | | 0.6923 | 5.0 | 345 | 0.6828 | 0.7568 | 0.7562 | 0.7562 | 0.7568 | | 0.6412 | 6.0 | 414 | 0.6531 | 0.7568 | 0.7559 | 0.7556 | 0.7568 | | 0.5982 | 7.0 | 483 | 0.6320 | 0.7610 | 0.7601 | 0.7597 | 0.7610 | | 0.5593 | 8.0 | 552 | 0.6133 | 0.7651 | 0.7655 | 0.7664 | 0.7651 | | 0.5183 | 9.0 | 621 | 0.6036 | 0.7714 | 0.7708 | 0.7709 | 0.7714 | | 0.5042 | 10.0 | 690 | 0.5951 | 0.7756 | 0.7755 | 0.7760 | 0.7756 | | 0.483 | 11.0 | 759 | 0.5878 | 0.7766 | 0.7768 | 0.7774 | 0.7766 | | 0.4531 | 12.0 | 828 | 0.5855 | 0.7850 | 0.7841 | 0.7839 | 0.7850 | | 0.4386 | 13.0 | 897 | 0.5828 | 0.7797 | 0.7790 | 0.7786 | 0.7797 | | 0.4238 | 14.0 | 966 | 0.5788 | 0.7777 | 0.7780 | 0.7786 | 0.7777 | | 0.4018 | 15.0 | 1035 | 0.5793 | 0.7839 | 0.7842 | 0.7855 | 0.7839 | | 0.3998 | 16.0 | 1104 | 0.5801 | 0.7850 | 0.7844 | 0.7841 | 0.7850 | | 0.3747 | 17.0 | 1173 | 0.5791 | 0.7839 | 0.7836 | 0.7833 | 0.7839 | | 0.3595 | 18.0 | 1242 | 0.5799 | 0.7891 | 0.7891 | 0.7894 | 0.7891 | | 0.3575 | 19.0 | 1311 | 0.5820 | 0.7881 | 0.7886 | 0.7906 | 0.7881 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

a9dd52aa859a36c5dfc520aa4e36b43f

KoboldAI/OPT-2.7B-Erebus

KoboldAI

opt

transformers

text-generation

other

['en']

[]

# OPT 2.7B - Erebus ## Model description This is the second generation of the original Shinen made by Mr. Seeker. The full dataset consists of 6 different sources, all surrounding the "Adult" theme. The name "Erebus" comes from the greek mythology, also named "darkness". This is in line with Shin'en, or "deep abyss". For inquiries, please contact the KoboldAI community. **Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.** ## Training data The data can be divided in 6 different datasets: - Literotica (everything with 4.5/5 or higher) - Sexstories (everything with 90 or higher) - Dataset-G (private dataset of X-rated stories) - Doc's Lab (all stories) - Pike Dataset (novels with "adult" rating) - SoFurry (collection of various animals) The dataset uses `[Genre: <comma-separated list of genres>]` for tagging. ### 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='KoboldAI/OPT-2.7B-Erebus') >>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50) [{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}] ``` ## Limitations and biases Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). **Warning: This model has a very strong NSFW bias!** ### License OPT-6.7B is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ### BibTeX entry and citation info ``` @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} } ```

a86245e3e3b45ee168a60be14b4bfb76

projecte-aina/roberta-large-ca-paraphrase

projecte-aina

roberta

transformers

text-classification

apache-2.0

['ca']

['projecte-aina/Parafraseja']

['catalan', 'paraphrase', 'textual entailment']

# Catalan BERTa (roberta-large-ca-v2) finetuned for Paraphrase Detection ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-large-ca-paraphrase** is a Paraphrase Detection model for the Catalan language fine-tuned from the roberta-large-ca-v2 model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers. ## Intended uses and limitations **roberta-large-ca-paraphrase** model can be used to detect if two sentences are in a paraphrase relation. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-large-ca-paraphrase") example = "Tinc un amic a Manresa. </s></s> A Manresa hi viu un amic meu." paraphrase = nlp(example) pprint(paraphrase) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the Paraphase Detection dataset in Catalan [Parafraseja](https://huggingface.co/datasets/projecte-aina/Parafraseja) for training and evaluation. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing the combined_score. ## Evaluation results We evaluated the _roberta-large-ca-paraphrase_ on the Parafraseja test set against standard multilingual and monolingual baselines: | Model | Parafraseja (combined_score) | | ------------|:-------------| | roberta-large-ca-v2 |**86.42** | | roberta-base-ca-v2 |84.38 | | mBERT | 79.66 | | XLM-RoBERTa | 77.83 | ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) ### Contact information For further information, send an email to aina@bsc.es ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ### Citation Information NA ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

ba3147b5c5bff3c4bd8bef0f86f77999

Sentdex/GPyT

Sentdex

gpt2

transformers

text-generation

mit

['Python']

['Code', 'GPyT', 'code generator']

GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~80GB of pure Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training and/or fine-tuning. Newlines are replaced by `<N>` Input to the model is code, up to the context length of 1024, with newlines replaced by `<N>` Here's a quick example of using this model: ```py from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("Sentdex/GPyT") model = AutoModelWithLMHead.from_pretrained("Sentdex/GPyT") # copy and paste some code in here inp = """import""" newlinechar = "<N>" converted = inp.replace("\n", newlinechar) tokenized = tokenizer.encode(converted, return_tensors='pt') resp = model.generate(tokenized) decoded = tokenizer.decode(resp[0]) reformatted = decoded.replace("<N>","\n") print(reformatted) ``` Should produce: ``` import numpy as np import pytest import pandas as pd<N ``` This model does a ton more than just imports, however. For a bunch of examples and a better understanding of the model's capabilities: https://pythonprogramming.net/GPT-python-code-transformer-model-GPyT/ Considerations: 1. This model is intended for educational and research use only. Do not trust model outputs. 2. Model is highly likely to regurgitate code almost exactly as it saw it. It's up to you to determine licensing if you intend to actually use the generated code. 3. All Python code was blindly pulled from github. This means included code is both Python 2 and 3, among other more subtle differences, such as tabs being 2 spaces in some cases and 4 in others...and more non-homologous things. 4. Along with the above, this means the code generated could wind up doing or suggesting just about anything. Run the generated code at own risk...it could be *anything*

38a80fa206d3f13768ecb491f86afae6

bharat-raghunathan/Tamil-Wav2Vec-xls-r-300m-Tamil-colab

bharat-raghunathan

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer', 'ta', 'robust-speech-event']

<!-- 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. --> # Tamil-Wav2Vec-xls-r-300m-Tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

e9cf15628a02e3311192f7955eb582cc

NAWNIE/golden-hour-photography

NAWNIE

diffusers

text-to-image

creativeml-openrail-m

['text-to-image', 'stable-diffusion']

### Golden_hour_photography Dreambooth model trained by NAWNIE with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:       

a652ede18d7baec1105af1f9c8b84656

HUPD/hupd-distilroberta-base

HUPD

roberta

transformers

fill-mask

cc-by-sa-4.0

['en']

['HUPD/hupd']

['hupd', 'roberta', 'distilroberta', 'patents']

# HUPD DistilRoBERTa-Base Model This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It was originally introduced in [this paper](TBD). For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentdataset.org/) or the [project's GitHub repository](https://github.com/suzgunmirac/hupd). ### How to Use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline model = pipeline(task="fill-mask", model="hupd/hupd-distilroberta-base") model("Improved <mask> for playing a game of thumb wrestling.") ``` Here is the output: ```python [{'score': 0.4274042248725891, 'sequence': 'Improved method for playing a game of thumb wrestling.', 'token': 5448, 'token_str': ' method'}, {'score': 0.06967400759458542, 'sequence': 'Improved system for playing a game of thumb wrestling.', 'token': 467, 'token_str': ' system'}, {'score': 0.06849079579114914, 'sequence': 'Improved device for playing a game of thumb wrestling.', 'token': 2187, 'token_str': ' device'}, {'score': 0.04544765502214432, 'sequence': 'Improved apparatus for playing a game of thumb wrestling.', 'token': 26529, 'token_str': ' apparatus'}, {'score': 0.025765646249055862, 'sequence': 'Improved means for playing a game of thumb wrestling.', 'token': 839, 'token_str': ' means'}] ``` Alternatively, you can load the model and use it as follows: ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM # cuda/cpu device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = AutoTokenizer.from_pretrained("hupd/hupd-distilroberta-base") model = AutoModelForMaskedLM.from_pretrained("hupd/hupd-distilroberta-base").to(device) TEXT = "Improved <mask> for playing a game of thumb wrestling." inputs = tokenizer(TEXT, return_tensors="pt").to(device) with torch.no_grad(): logits = model(**inputs).logits # retrieve indices of <mask> mask_token_indxs = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0] for mask_idx in mask_token_indxs: predicted_token_id = logits[0, mask_idx].argmax(axis=-1) output = tokenizer.decode(predicted_token_id) print(f'Prediction for the <mask> token at index {mask_idx}: "{output}"') ``` Here is the output: ```python Prediction for the <mask> token at index 2: " method" ``` ## Citation For more information, please take a look at the original paper. * Paper: [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](TBD) * Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* * BibTeX: ``` @article{suzgun2022hupd, title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications}, author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K and Kominers, Scott and Shieber, Stuart}, year={2022} } ```

d1658aeee5f74e36666d10602da01bf3

joewoodworth/ddpm-butterflies-128

joewoodworth

diffusers

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/joewoodworth/ddpm-butterflies-128/tensorboard?#scalars)

e451b873bd1975d7d03e67bab0d1fb4b

lariskelmer/opus-mt-en-ro-finetuned-en-to-ro

lariskelmer

marian

transformers

text2text-generation

apache-2.0

['wmt16']

['generated_from_trainer']

<!-- 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. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1505 - Gen Len: 34.1036 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7437 | 1.0 | 38145 | 1.2886 | 28.1505 | 34.1036 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

0c7b1c544a58d15e40ca47b97f7d22a2

anas-awadalla/roberta-base-few-shot-k-256-finetuned-squad-seed-6

anas-awadalla

roberta

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- 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. --> # roberta-base-few-shot-k-256-finetuned-squad-seed-6 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. ## 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: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

242debf15dc411fb4bd8a17708025a05

espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave

espnet

espnet

text-to-speech

cc-by-4.0

['ja']

['jsut']

['espnet', 'audio', 'text-to-speech']

## Example ESPnet2 TTS model ### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4032246/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ede15f3aa5c3a08db391c18174a153b9

anas-awadalla/bert-medium-pretrained-finetuned-squad

anas-awadalla

bert

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- 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. --> # bert_medium_pretrain_squad This model is a fine-tuned version of [anas-awadalla/bert-medium-pretrained-on-squad](https://huggingface.co/anas-awadalla/bert-medium-pretrained-on-squad) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.0973 - "exact_match": 77.95648060548723 - "f1": 85.85300366384631 ## 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: 5e-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: 3.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

4eb4d5326d08d9725c7cd711bb5b3852

Chikashi/t5-small-finetuned-cnndm3-wikihow2

Chikashi

t5

transformers

text2text-generation

apache-2.0

['cnn_dailymail']

['generated_from_trainer']

<!-- 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. --> # t5-small-finetuned-cnndm3-wikihow2 This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm2-wikihow2](https://huggingface.co/Chikashi/t5-small-finetuned-cnndm2-wikihow2) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6265 - Rouge1: 24.6704 - Rouge2: 11.9038 - Rougel: 20.3622 - Rougelsum: 23.2612 - Gen Len: 18.9997 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.8071 | 1.0 | 71779 | 1.6265 | 24.6704 | 11.9038 | 20.3622 | 23.2612 | 18.9997 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

a98580004b6782764aa2a8e65f5e4683

ConvLab/setsumbt-dst-sgd

ConvLab

apache-2.0

['en']

['ConvLab/sgd']

['roberta', 'classification', 'dialog state tracking', 'conversational system', 'task-oriented dialog']

# SetSUMBT-dst-sgd This model is a fine-tuned version [SetSUMBT](https://github.com/ConvLab/ConvLab-3/tree/master/convlab/dst/setsumbt) of [roberta-base](https://huggingface.co/roberta-base) on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00001 - train_batch_size: 3 - eval_batch_size: 16 - seed: 0 - gradient_accumulation_steps: 1 - optimizer: AdamW - lr_scheduler_type: linear - num_epochs: 50.0 ### Framework versions - Transformers 4.17.0 - Pytorch 1.8.0+cu110 - Datasets 2.3.2 - Tokenizers 0.12.1

ca502d21021e9ba5fe2753c291cc5fbb

SamSick/TriviaQA_NLP4Web_Group12

SamSick

bert

transformers

question-answering