Datasets:

davanstrien

/

model_cards_with_readmes

like 1

othrif/wav2vec2-large-xlsr-arabic

othrif

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['ar']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

# Wav2Vec2-Large-XLSR-53-Arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ar", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Arabic test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ar", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-arabic") model.to("cuda") chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\؛\\\\\\\\\\\\\\\\—\\\\\\\\\\\\\\\\_get\\\\\\\\\\\\\\\\«\\\\\\\\\\\\\\\\»\\\\\\\\\\\\\\\\ـ\\\\\\\\\\\\\\\\ـ\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�\\\\\\\\\\\\\\\\#\\\\\\\\\\\\\\\\،\\\\\\\\\\\\\\\\☭,\\\\\\\\\\\\\\\\؟]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 46.77 ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://huggingface.co/othrif/wav2vec2-large-xlsr-arabic/tree/main)

76c3ffc1cdc540af88204e45603b4913

jonatasgrosman/exp_w2v2t_ar_vp-es_s601

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['ar']

['mozilla-foundation/common_voice_7_0']

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

# exp_w2v2t_ar_vp-es_s601 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](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.

77ce0563d4dfa7fb1517aaa232359395

muhtasham/tiny-mlm-glue-wnli-target-glue-wnli

muhtasham

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. --> # tiny-mlm-glue-wnli-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1020 - Accuracy: 0.1127 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6885 | 25.0 | 500 | 0.7726 | 0.2394 | | 0.658 | 50.0 | 1000 | 1.1609 | 0.0986 | | 0.6084 | 75.0 | 1500 | 1.6344 | 0.1127 | | 0.5481 | 100.0 | 2000 | 2.1020 | 0.1127 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

898aeba87e63faa466ca6aba338358b5

underactuated/opt-350m_mle

underactuated

opt

transformers

text-generation

other

['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. --> # opt-350m_mle This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown 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: 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: 3.0 ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.13.2

5990be6bc8a9c76224f11a7319011f6b

NouRed/distilbert_ner_wnut17

NouRed

distilbert

transformers

token-classification

apache-2.0

['wnut_17']

['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_ner_wnut17 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [WNUT-17](https://huggingface.co/datasets/wnut_17) 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.23.0 - Pytorch 1.12.1+cu113 - Tokenizers 0.13.1

10bd956bbb9c3d049caec848d5ac9693

href/gpt2-schiappa

href

gpt2

transformers

text-generation

unknown

[]

# Schiappa-Minelli GPT-2 Pourquoi pas ? ## Dataset - Marianne est déchainée, de Marlène Schiappa - Osez les sexfriends, Marie Minelli - Osez réussir votre divorce, Marie Minelli - Sexe, mensonge et banlieues chaudes, Marie Minelli ## Versions V1: - Fine-tunée avec [Max Woolf's "aitextgen — Train a GPT-2 (or GPT Neo)" colab](https://colab.research.google.com/drive/15qBZx5y9rdaQSyWpsreMDnTiZ5IlN0zD?usp=sharing) - Depuis le modèle gpt-2 124M [aquadzn/gpt2-french](https://github.com/aquadzn/gpt2-french/), version romans. - ~50 minutes on Colab Pro, P100 GPU, 3 batchs, 500 steps

f2ff0b139f0f8b7166b00c504a397cdf

salesken/natural_rephrase

salesken

gpt2

transformers

text-generation

apache-2.0

[]

NLG model trained on the rephrase generation dataset published by Fb Paper : https://research.fb.com/wp-content/uploads/2020/12/Sound-Natural-Content-Rephrasing-in-Dialog-Systems.pdf Paper Abstract : " We introduce a new task of rephrasing for a more natural virtual assistant. Currently, vir- tual assistants work in the paradigm of intent- slot tagging and the slot values are directly passed as-is to the execution engine. However, this setup fails in some scenarios such as mes- saging when the query given by the user needs to be changed before repeating it or sending it to another user. For example, for queries like ‘ask my wife if she can pick up the kids’ or ‘re- mind me to take my pills’, we need to rephrase the content to ‘can you pick up the kids’and ‘take your pills’. In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query.. " Training data : http://dl.fbaipublicfiles.com/rephrasing/rephrasing_dataset.tar.gz ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("salesken/natural_rephrase") model = AutoModelWithLMHead.from_pretrained("salesken/natural_rephrase") Input_query="Hey Siri, Send message to mom to say thank you for the delicious dinner yesterday" query= Input_query + " ~~ " input_ids = tokenizer.encode(query.lower(), return_tensors='pt') sample_outputs = model.generate(input_ids, do_sample=True, num_beams=1, max_length=len(Input_query), temperature=0.2, top_k = 10, num_return_sequences=1) for i in range(len(sample_outputs)): result = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split('||')[0].split('~~')[1] print(result) ```

f616747f57a3d119cd4c56b969c068d4

SirVeggie/cutesexyrobutts

SirVeggie

creativeml-openrail-m

[]

# Cutesexyrobutts stable diffusion model Original artist: Cutesexyrobutts\ Patreon: https://www.patreon.com/cutesexyrobutts ## Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, but it is recommended to include at least one. Adding token word/phrase class word/phrase at the start of the prompt in that order produces results most similar to the trained concept, but they can be included elsewhere as well. Some models produce better results when not including all token/class words. 3k models are are more flexible, while 5k models produce images closer to the trained concept. I recommend 2k/3k models for normal use, and 5k/6k models for model merging and use without token/class words. However it can be also very prompt specific. I highly recommend self-experimentation. These models are subject to the same legal concerns as their base models. ## Comparison Epoch 5 version was earlier in the waifu diffusion 1.3 training process, so it is easier to produce more varied, non anime, results. Robutts-any is the newest and best model. ## robutts-any ``` token: m_robutts class: illustration style base: anything v3 ``` ## robutts ``` token: § class: robutts base: waifu diffusion 1.3 ``` ## robutts_e5 ``` token: § class: robutts base: waifu diffusion 1.3-e5 ```

5d27b91fd03fc2e98d20901d9a206409

Helsinki-NLP/opus-mt-ss-en

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-ss-en * source languages: ss * target languages: en * OPUS readme: [ss-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ss-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ss.en | 30.9 | 0.478 |

154d295745ff1e4054b05cd531184a00

google/t5-efficient-base-dl4

google

t5

transformers

text2text-generation

apache-2.0

['en']

['c4']

['deep-narrow']

# T5-Efficient-BASE-DL4 (Deep-Narrow version) T5-Efficient-BASE-DL4 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-base-dl4** - is of model type **Base** with the following variations: - **dl** is **4** It has **147.4** million parameters and thus requires *ca.* **589.62 MB** of memory in full precision (*fp32*) or **294.81 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

38a7b78982c1bf80a179bf31d999988c

FredZhang7/distilgpt2-stable-diffusion

FredZhang7

gpt2

transformers

text-generation

creativeml-openrail-m

['FredZhang7/krea-ai-prompts', 'Gustavosta/Stable-Diffusion-Prompts', 'bartman081523/stable-diffusion-discord-prompts']

['stable-diffusion', 'prompt-generator', 'distilgpt2']

# DistilGPT2 Stable Diffusion Model Card <a href="https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a> DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text. This model was finetuned on 2.03 million descriptive stable diffusion prompts from [Stable Diffusion discord](https://huggingface.co/datasets/bartman081523/stable-diffusion-discord-prompts), [Lexica.art](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts), and (my hand-picked) [Krea.ai](https://huggingface.co/datasets/FredZhang7/krea-ai-prompts). I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4. Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM. ### PyTorch ```bash pip install --upgrade transformers ``` ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel # load the pretrained tokenizer tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') tokenizer.add_special_tokens({'pad_token': '[PAD]'}) tokenizer.max_len = 512 # load the fine-tuned model model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion') # generate text using fine-tuned model from transformers import pipeline nlp = pipeline('text-generation', model=model, tokenizer=tokenizer) ins = "a beautiful city" # generate 10 samples outs = nlp(ins, max_length=80, num_return_sequences=10) # print the 10 samples for i in range(len(outs)): outs[i] = str(outs[i]['generated_text']).replace(' ', '') print('\033[96m' + ins + '\033[0m') print('\033[93m' + '\n\n'.join(outs) + '\033[0m') ``` Example Output: 

a70eb09d9bd77c64413f4e00ff618a04

nsaghatelyan/blue-back-pack

nsaghatelyan

diffusers

text-to-image

creativeml-openrail-m

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

### blue_back_pack Dreambooth model trained by nsaghatelyan 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) Sample pictures of this concept:

a8a69981e5b839a84565bd25dd6ec1e4

harmonai/jmann-small-190k

harmonai

diffusers

mit

['audio-generation']

[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers. ## FP32 ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/jmann-small-190k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ``` ## FP16 Faster at a small loss of quality ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/jmann-small-190k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ```

6e04fa21ba23c1a6da49c4a97f2dbb4b

prompthero/openjourney-v2

prompthero

diffusers

text-to-image

creativeml-openrail-m

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

# Openjourney v2 is an open source Stable Diffusion fine tuned model on +60k Midjourney images, by [PromptHero](https://prompthero.com/?utm_source=huggingface&utm_medium=referral) This repo is for testing the first Openjourney fine tuned model. It was trained over Stable Diffusion 1.5 with +60000 images, 4500 steps and 3 epochs. So "mdjrny-v4 style" is not necessary anymore (yay!) # Openjourney Links - [Lora version](https://huggingface.co/prompthero/openjourney-lora) - [Openjourney Dreambooth](https://huggingface.co/prompthero/openjourney)

36cb5dba81a7a7f5fc83e90c2cc5ec68

Helsinki-NLP/opus-mt-vi-it

Helsinki-NLP

marian

transformers

translation

apache-2.0

['vi', 'it']

['translation']

### vie-ita * source group: Vietnamese * target group: Italian * OPUS readme: [vie-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-ita/README.md) * model: transformer-align * source language(s): vie * target language(s): ita * 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-ita/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-ita/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-ita/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.vie.ita | 31.2 | 0.548 | ### System Info: - hf_name: vie-ita - source_languages: vie - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'it'] - src_constituents: {'vie', 'vie_Hani'} - tgt_constituents: {'ita'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-ita/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-ita/opus-2020-06-17.test.txt - src_alpha3: vie - tgt_alpha3: ita - short_pair: vi-it - chrF2_score: 0.5479999999999999 - bleu: 31.2 - brevity_penalty: 0.932 - ref_len: 1774.0 - src_name: Vietnamese - tgt_name: Italian - train_date: 2020-06-17 - src_alpha2: vi - tgt_alpha2: it - prefer_old: False - long_pair: vie-ita - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

bde7131f54d21defc536d02d1b8f8f4c

nimrah/wav2vec2-large-xls-r-300m-turkish-colab

nimrah

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-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. It achieves the following results on the evaluation set: - Loss: 3.2970 - Wer: 1.0 ## 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: 0.1 - 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 6.1837 | 3.67 | 400 | 3.2970 | 1.0 | | 0.0 | 7.34 | 800 | 3.2970 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

e589ce77b8310d22241093db1aab087a

NimaBoscarino/IS-Net_DIS-general-use

NimaBoscarino

image-segmentation

apache-2.0

['background-removal', 'computer-vision', 'image-segmentation']

# IS-Net_DIS-general-use * Model Authors: Xuebin Qin, Hang Dai, Xiaobin Hu, Deng-Ping Fan*, Ling Shao, Luc Van Gool * Paper: Highly Accurate Dichotomous Image Segmentation (ECCV 2022 - https://arxiv.org/pdf/2203.03041.pdf * Code Repo: https://github.com/xuebinqin/DIS * Project Homepage: https://xuebinqin.github.io/dis/index.html Note that this is an _optimized_ version of the IS-NET model. From the paper abstract: > [...] we introduce a simple intermediate supervision baseline (IS- Net) using both feature-level and mask-level guidance for DIS model training. Without tricks, IS-Net outperforms var- ious cutting-edge baselines on the proposed DIS5K, mak- ing it a general self-learned supervision network that can help facilitate future research in DIS.  # Citation ``` @InProceedings{qin2022, author={Xuebin Qin and Hang Dai and Xiaobin Hu and Deng-Ping Fan and Ling Shao and Luc Van Gool}, title={Highly Accurate Dichotomous Image Segmentation}, booktitle={ECCV}, year={2022} } ```

52aed6c16ad091aa07cef676fda80e70

Minerster/Text_process

Minerster

openrail

[]

# Initialize the pipeline with the "text-davinci-002" model segmenter = pipeline("text-segmentation", model="text-davinci-002", tokenizer='text-davinci-002') # Segment the text segmented_text = segmenter("This is a longer text that we want to segment into smaller chunks. Each chunk should correspond to a coherent piece of text.") # Process each segment with ChatGPT nlp = pipeline("text-generation", model="text-davinci-002", tokenizer='text-davinci-002') for segment in segmented_text: print(nlp(segment))

bb9071eddfc73f6767a218d772b08998

mriggs/mt5-small-finetuned-1epoch-opus_books-en-to-it

mriggs

mt5

transformers

text2text-generation

apache-2.0

['opus_books']

['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-small-finetuned-1epoch-opus_books-en-to-it This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 3.3717 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.5201 | 1.0 | 3638 | 3.3717 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.0 - Tokenizers 0.13.1

492e7f690ffdc7aa1dafa81d273a362d

jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-8_female-2_s874

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

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

# exp_w2v2r_de_vp-100k_gender_male-8_female-2_s874 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 (de)](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.

513e20bd439a05f825a68d7a1acc291c

Gumibit/cr7-v2-768

Gumibit

diffusers

text-to-image

creativeml-openrail-m

['text-to-image']

### CR7_v2_768 Dreambooth model trained by Gumibit with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-768 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: CrisRo07 (use that on your prompt) 

67a8241aaef708efbdbf7be00ee6819a

Das282000Prit/bert-base-uncased-finetuned-wikitext2

Das282000Prit

bert

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. --> # bert-base-uncased-finetuned-wikitext2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7295 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9288 | 1.0 | 2319 | 1.7729 | | 1.8208 | 2.0 | 4638 | 1.7398 | | 1.7888 | 3.0 | 6957 | 1.7523 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

ec5ade9d862ff3e96e60edb4034194f7

sanjeev498/vit-base-beans

sanjeev498

vit

transformers

image-classification

apache-2.0

['beans']

['image-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. --> # vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0189 - Accuracy: 1.0 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0568 | 1.54 | 100 | 0.0299 | 1.0 | | 0.0135 | 3.08 | 200 | 0.0189 | 1.0 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

134a6da5513e541eb965ce825f952951

Helsinki-NLP/opus-mt-tc-big-zls-de

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['bg', 'de', 'hr', 'mk', 'sh', 'sl', 'sr']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-zls-de ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from South Slavic languages (zls) to German (de). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-26 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn - Target Language(s): deu - Language Pair(s): bul-deu hbs-deu hrv-deu mkd-deu slv-deu srp_Cyrl-deu srp_Latn-deu - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT zls-deu README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-deu/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Jesi li ti student?", "Dve stvari deca treba da dobiju od svojih roditelja: korene i krila." ] model_name = "pytorch-models/opus-mt-tc-big-zls-de" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Sind Sie Student? # Zwei Dinge sollten Kinder von ihren Eltern bekommen: Wurzeln und Flügel. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-de") print(pipe("Jesi li ti student?")) # expected output: Sind Sie Student? ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | bul-deu | tatoeba-test-v2021-08-07 | 0.71220 | 54.5 | 314 | 2224 | | hbs-deu | tatoeba-test-v2021-08-07 | 0.71283 | 54.8 | 1959 | 15559 | | hrv-deu | tatoeba-test-v2021-08-07 | 0.69448 | 53.1 | 782 | 5734 | | slv-deu | tatoeba-test-v2021-08-07 | 0.36339 | 21.1 | 492 | 3003 | | srp_Latn-deu | tatoeba-test-v2021-08-07 | 0.72489 | 56.0 | 986 | 8500 | | bul-deu | flores101-devtest | 0.57688 | 28.4 | 1012 | 25094 | | hrv-deu | flores101-devtest | 0.56674 | 27.4 | 1012 | 25094 | | mkd-deu | flores101-devtest | 0.57688 | 29.3 | 1012 | 25094 | | slv-deu | flores101-devtest | 0.56258 | 26.7 | 1012 | 25094 | | srp_Cyrl-deu | flores101-devtest | 0.59271 | 30.7 | 1012 | 25094 | ## Citation Information * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 8b9f0b0 * port time: Sat Aug 13 00:05:30 EEST 2022 * port machine: LM0-400-22516.local

0e86657be1671ca1841fa94b8d3f05f2

tlapusan/distilbert-base-uncased-finetuned-imdb

tlapusan

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 None dataset. It achieves the following results on the evaluation set: - Loss: 2.1639 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.7695 | 1.0 | 90 | 2.3614 | | 2.3627 | 2.0 | 180 | 2.1959 | | 2.227 | 3.0 | 270 | 2.1313 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

975378ba8746e62cb7c8f476bf4d8cba

abdelkader/distilbert-base-uncased-distilled-clinc

abdelkader

distilbert

transformers

text-classification

apache-2.0

['clinc_oos']

['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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3038 - Accuracy: 0.9465 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 2.8460 | 0.7506 | | 3.322 | 2.0 | 636 | 1.4301 | 0.8532 | | 3.322 | 3.0 | 954 | 0.7377 | 0.9152 | | 1.2296 | 4.0 | 1272 | 0.4784 | 0.9316 | | 0.449 | 5.0 | 1590 | 0.3730 | 0.9390 | | 0.449 | 6.0 | 1908 | 0.3367 | 0.9429 | | 0.2424 | 7.0 | 2226 | 0.3163 | 0.9468 | | 0.1741 | 8.0 | 2544 | 0.3074 | 0.9452 | | 0.1741 | 9.0 | 2862 | 0.3054 | 0.9458 | | 0.1501 | 10.0 | 3180 | 0.3038 | 0.9465 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

5568279f835927c37ca01c2eb812c7e5

MoHai/wav2vec2-base-timit-demo-colab

MoHai

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-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4701 - Wer: 0.4537 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5672 | 4.0 | 500 | 1.6669 | 1.0323 | | 0.6226 | 8.0 | 1000 | 0.4701 | 0.4537 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

0e9bcf4bf575d451ce8c7bf5415fc9d6

CarpetCleaningPlanoTX/UpholsteryCleaningPlanoTX

CarpetCleaningPlanoTX

other

[]

Upholstery Cleaning Plano TX https://carpetcleaningplanotx.com/upholstery-cleaning.html ‪(469) 444-1903‬ We remove stains from sofas.When you have a nice, comfortable sofa in your home, spills are common.On that new couch, game day weekends can be difficult.When they are excited about who is winning on the playing field, friends, family, and pets can cause havoc.After a party, upholstery cleaning is not a problem.We can arrive with our mobile unit, which simplifies the task.

b2d52762f8ee1a851ceeba27de5749ea

CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26

CAMeL-Lab

bert

transformers

text-classification

apache-2.0

['ar']

[]

# CAMeLBERT-Mix DID Madar Corpus26 Model ## Model description **CAMeLBERT-Mix DID Madar Corpus26 Model** is a dialect identification (DID) 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 [MADAR Corpus 26](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/) dataset, which includes 26 labels. 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 DID Madar Corpus26 model as part of the transformers pipeline. This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. #### How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar26') >>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟'] >>> did(sentences) [{'label': 'CAI', 'score': 0.8751305937767029}, {'label': 'DOH', 'score': 0.9867215156555176}] ``` *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.", } ```

7672980558767e17d40eb43ad827b8f8

benjamin/gerpt2-large

benjamin

gpt2

transformers

text-generation

mit

['de']

[]

# GerPT2 German large and small versions of GPT2: - https://huggingface.co/benjamin/gerpt2 - https://huggingface.co/benjamin/gerpt2-large See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2. ## Comparison to [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) I evaluated both GerPT2-large and the other German GPT2, [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on the [CC-100](http://data.statmt.org/cc-100/) dataset and on the German Wikipedia: | | CC-100 (PPL) | Wikipedia (PPL) | |-------------------|--------------|-----------------| | dbmdz/german-gpt2 | 49.47 | 62.92 | | GerPT2 | 24.78 | 35.33 | | GerPT2-large | __16.08__ | __23.26__ | | | | | See the script `evaluate.py` in the [GerPT2 Github repository](https://github.com/bminixhofer/gerpt2) for the code. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("benjamin/gerpt2-large") model = AutoModelForCausalLM.from_pretrained("benjamin/gerpt2-large") prompt = "<your prompt>" pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) print(pipe(prompt)[0]["generated_text"]) ``` Also, two tricks might improve the generated text: ```python output = model.generate( # during training an EOS token was used to mark the beginning of each text # so it can help to insert it at the start torch.tensor( [tokenizer.eos_token_id] + tokenizer.encode(prompt) ).unsqueeze(0), do_sample=True, # try setting bad_words_ids=[[0]] to disallow generating an EOS token, without this the model is # prone to ending generation early because a significant number of texts from the training corpus # is quite short bad_words_ids=[[0]], max_length=max_length, )[0] print(tokenizer.decode(output)) ``` ## Training details GerPT2-large is trained on the entire German data from the [CC-100 Corpus](http://data.statmt.org/cc-100/) and weights were initialized from the [English GPT2 model](https://huggingface.co/gpt2-large). GerPT2-large was trained with: - a batch size of 256 - using OneCycle learning rate with a maximum of 5e-3 - with AdamW with a weight decay of 0.01 - for 2 epochs Training took roughly 12 days on 8 TPUv3 cores. To train GerPT2-large, follow these steps. Scripts are located in the [Github repository](https://github.com/bminixhofer/gerpt2): 0. Download and unzip training data from http://data.statmt.org/cc-100/. 1. Train a tokenizer using `prepare/train_tokenizer.py`. As training data for the tokenizer I used a random subset of 5% of the CC-100 data. 2. (optionally) generate a German input embedding matrix with `prepare/generate_aligned_wte.py`. This uses a neat trick to semantically map tokens from the English tokenizer to tokens from the German tokenizer using aligned word embeddings. E. g.: ``` ĠMinde -> Ġleast Ġjed -> Ġwhatsoever flughafen -> Air vermittlung -> employment teilung -> ignment ĠInterpretation -> Ġinterpretation Ġimport -> Ġimported hansa -> irl genehmigungen -> exempt ĠAuflist -> Ġlists Ġverschwunden -> Ġdisappeared ĠFlyers -> ĠFlyers Kanal -> Channel Ġlehr -> Ġteachers Ġnahelie -> Ġconvenient gener -> Generally mitarbeiter -> staff ``` This helps a lot on a trial run I did, although I wasn't able to do a full comparison due to budget and time constraints. To use this WTE matrix it can be passed via the `wte_path` to the training script. Credit to [this blogpost](https://medium.com/@pierre_guillou/faster-than-training-from-scratch-fine-tuning-the-english-gpt-2-in-any-language-with-hugging-f2ec05c98787) for the idea of initializing GPT2 from English weights. 3. Tokenize the corpus using `prepare/tokenize_text.py`. This generates files for train and validation tokens in JSON Lines format. 4. Run the training script `train.py`! `run.sh` shows how this was executed for the full run with config `configs/tpu_large.json`. ## License GerPT2 is licensed under the MIT License. ## Citing Please cite GerPT2 as follows: ``` @misc{Minixhofer_GerPT2_German_large_2020, author = {Minixhofer, Benjamin}, doi = {10.5281/zenodo.5509984}, month = {12}, title = {{GerPT2: German large and small versions of GPT2}}, url = {https://github.com/bminixhofer/gerpt2}, year = {2020} } ``` ## Acknowledgements Thanks to [Hugging Face](https://huggingface.co) for awesome tools and infrastructure. Huge thanks to [Artus Krohn-Grimberghe](https://twitter.com/artuskg) at [LYTiQ](https://www.lytiq.de/) for making this possible by sponsoring the resources used for training.

bebbd2a3f29d6d20c328a37fde0414c7

fghjfbrtb/wr1

fghjfbrtb

diffusers

text-to-image

creativeml-openrail-m

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

### wr1 Dreambooth model trained by fghjfbrtb 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:

21ac9e18da6f3e336296e9f0b02b7a6b

facebook/hubert-xlarge-ls960-ft

facebook

hubert

transformers

automatic-speech-recognition

apache-2.0

['en']

['libri-light', 'librispeech_asr']

['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard']

# Hubert-Extra-Large-Finetuned [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The extra large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine-tuned version of [hubert-xlarge-ll60k](https://huggingface.co/facebook/hubert-xlarge-ll60k). [Paper](https://arxiv.org/abs/2106.07447) Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed **Abstract** Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert . # Usage The model can be used for automatic-speech-recognition as follows: ```python import torch from transformers import Wav2Vec2Processor, HubertForCTC from datasets import load_dataset processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-xlarge-ls960-ft") model = HubertForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1 logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]) # ->"A MAN SAID TO THE UNIVERSE SIR I EXIST" ```

d27e0dda9b1c136fccfc1998caec173f

cdefghijkl/ap

cdefghijkl

diffusers

text-to-image

creativeml-openrail-m

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

### ap Dreambooth model trained by cdefghijkl 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) Sample pictures of this concept:

a171499801f31ec319c510752a9b2362

sd-concepts-library/dreamy-painting

sd-concepts-library

mit

[]

### Dreamy Painting on Stable Diffusion This is the `<dreamy-painting>` 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 a `style`:      Here are images generated in this style:    

875e6c86daecb83a7ae78a1fbff9e78f

hr16/any-ely-wd-ira-olympus-3000

hr16

diffusers

text-to-image

creativeml-openrail-m

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

### Model Dreambooth concept any_ely_wd-Ira_Olympus-3000 được train bởi hr16 bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVmt2oX7733W44b0) <br> Hoặc test bằng `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Ảnh mẫu của concept: WIP

5cb45850c4d39c40ded658ec02a7a04d

sd-concepts-library/yf21

sd-concepts-library

mit

[]

### YF21 on Stable Diffusion This is the `<YF21>` 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`:    

fbcc174a56a63c2898b9c32e7553b559

Helsinki-NLP/opus-mt-en-trk

Helsinki-NLP

marian

transformers

translation

apache-2.0

['en', 'tt', 'cv', 'tk', 'tr', 'ba', 'trk']

['translation']

### eng-trk * source group: English * target group: Turkic languages * OPUS readme: [eng-trk](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-trk/README.md) * model: transformer * source language(s): eng * target language(s): aze_Latn bak chv crh crh_Latn kaz_Cyrl kaz_Latn kir_Cyrl kjh kum ota_Arab ota_Latn sah tat tat_Arab tat_Latn tuk tuk_Latn tur tyv uig_Arab uig_Cyrl uzb_Cyrl uzb_Latn * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-trk/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-trk/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-trk/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-entr-engtur.eng.tur | 10.1 | 0.437 | | newstest2016-entr-engtur.eng.tur | 9.2 | 0.410 | | newstest2017-entr-engtur.eng.tur | 9.0 | 0.410 | | newstest2018-entr-engtur.eng.tur | 9.2 | 0.413 | | Tatoeba-test.eng-aze.eng.aze | 26.8 | 0.577 | | Tatoeba-test.eng-bak.eng.bak | 7.6 | 0.308 | | Tatoeba-test.eng-chv.eng.chv | 4.3 | 0.270 | | Tatoeba-test.eng-crh.eng.crh | 8.1 | 0.330 | | Tatoeba-test.eng-kaz.eng.kaz | 11.1 | 0.359 | | Tatoeba-test.eng-kir.eng.kir | 28.6 | 0.524 | | Tatoeba-test.eng-kjh.eng.kjh | 1.0 | 0.041 | | Tatoeba-test.eng-kum.eng.kum | 2.2 | 0.075 | | Tatoeba-test.eng.multi | 19.9 | 0.455 | | Tatoeba-test.eng-ota.eng.ota | 0.5 | 0.065 | | Tatoeba-test.eng-sah.eng.sah | 0.7 | 0.030 | | Tatoeba-test.eng-tat.eng.tat | 9.7 | 0.316 | | Tatoeba-test.eng-tuk.eng.tuk | 5.9 | 0.317 | | Tatoeba-test.eng-tur.eng.tur | 34.6 | 0.623 | | Tatoeba-test.eng-tyv.eng.tyv | 5.4 | 0.210 | | Tatoeba-test.eng-uig.eng.uig | 0.1 | 0.155 | | Tatoeba-test.eng-uzb.eng.uzb | 3.4 | 0.275 | ### System Info: - hf_name: eng-trk - source_languages: eng - target_languages: trk - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-trk/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'tt', 'cv', 'tk', 'tr', 'ba', 'trk'] - src_constituents: {'eng'} - tgt_constituents: {'kir_Cyrl', 'tat_Latn', 'tat', 'chv', 'uzb_Cyrl', 'kaz_Latn', 'aze_Latn', 'crh', 'kjh', 'uzb_Latn', 'ota_Arab', 'tuk_Latn', 'tuk', 'tat_Arab', 'sah', 'tyv', 'tur', 'uig_Arab', 'crh_Latn', 'kaz_Cyrl', 'uig_Cyrl', 'kum', 'ota_Latn', 'bak'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-trk/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-trk/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: trk - short_pair: en-trk - chrF2_score: 0.455 - bleu: 19.9 - brevity_penalty: 1.0 - ref_len: 57072.0 - src_name: English - tgt_name: Turkic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: trk - prefer_old: False - long_pair: eng-trk - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

0822a9ec16216342129307712f368c1a

priansh/maeve-12-6-xsum

priansh

bart

transformers

text2text-generation

gpl-3.0

['en']

['xsum']

['text2text-generation', 'pytorch']

# Maeve - XSUM Maeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer). This allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with traditional transformers. This specific model has been trained on the XSUM dataset, and can invert summaries into full-length news articles. Feel free to try examples on the right!

05c3fc5cb4d66c70642db5051a36a3cd

Haakf/allsides_left_text_headline_padded

Haakf

distilbert

transformers

fill-mask

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Haakf/allsides_left_text_headline_padded 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: - Train Loss: 2.1538 - Validation Loss: 2.0656 - Epoch: 5 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -712, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.6282 | 2.3390 | 0 | | 2.3665 | 2.1495 | 1 | | 2.2517 | 2.0798 | 2 | | 2.1652 | 2.0935 | 3 | | 2.1376 | 2.0485 | 4 | | 2.1538 | 2.0656 | 5 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

21351c016eef20ec3737793432ee1917

jmassot/xlm-roberta-base-jm-finetuned-panx-de-fr_hub

jmassot

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-jm-finetuned-panx-de-fr_hub 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: 0.1668 - F1: 0.8587 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2929 | 1.0 | 715 | 0.1811 | 0.8250 | | 0.1473 | 2.0 | 1430 | 0.1610 | 0.8519 | | 0.0934 | 3.0 | 2145 | 0.1668 | 0.8587 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.1

16afee3bc8670d4f201abeb6234ea2c3

paola-md/distilr-lr1e05-wd0.05-bs32

paola-md

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. --> # distilr-lr1e05-wd0.05-bs32 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: 0.2743 - Rmse: 0.5237 - Mse: 0.2743 - Mae: 0.4135 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2775 | 1.0 | 623 | 0.2735 | 0.5229 | 0.2735 | 0.4179 | | 0.2738 | 2.0 | 1246 | 0.2727 | 0.5222 | 0.2727 | 0.4126 | | 0.2722 | 3.0 | 1869 | 0.2727 | 0.5222 | 0.2727 | 0.4165 | | 0.2702 | 4.0 | 2492 | 0.2754 | 0.5248 | 0.2754 | 0.3997 | | 0.2684 | 5.0 | 3115 | 0.2765 | 0.5259 | 0.2765 | 0.4229 | | 0.2668 | 6.0 | 3738 | 0.2743 | 0.5237 | 0.2743 | 0.4135 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

772819539df872a0e3c357baca8bad23

byeongal/gpt2-large

byeongal

gpt2

transformers

text-generation

mit

['en']

['gpt2']

# GPT-2 - This model forked from [gpt2](https://huggingface.co/gpt2-large) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp). Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large 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 transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-large') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large') model = GPT2Model.from_pretrained('gpt2-large') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large') model = TFGPT2Model.from_pretrained('gpt2-large') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### 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. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-large') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## 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>

d38cf823fcd860d01566e14a38cf9221

ASCCCCCCCC/PENGMENGJIE-finetuned-emotion

ASCCCCCCCC

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. --> # PENGMENGJIE-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown 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: 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 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

946cf2f488997465a2ff3013c0dac869

unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2

unicamp-dl

xlm-roberta

transformers

text-classification

mit

['pt']

['msmarco']

['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br']

# mMiniLM-L6-v2 Reranker finetuned on mMARCO ## Introduction mMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the v2 version, the Portuguese dataset was translated using Google Translate. Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository. ## Usage ```python from transformers import AutoTokenizer, AutoModel model_name = 'unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` # Citation If you use mMiniLM-L6-v2-en-pt-msmarco-v2, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, eprint={2108.13897}, archivePrefix={arXiv}, primaryClass={cs.CL} }

1c6a609e86119c90c9ac6fff44e903c5

cammy/bart-large-cnn-100-lit-evalMA-NOpad

cammy

bart

transformers

text2text-generation

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. --> # bart-large-cnn-100-lit-evalMA-NOpad This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1514 - Rouge1: 27.5985 - Rouge2: 11.3869 - Rougel: 20.9359 - Rougelsum: 24.7113 - Gen Len: 62.5 ## 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 100 | 1.7982 | 28.7996 | 11.2592 | 19.7524 | 25.2125 | 62.5 | | No log | 2.0 | 200 | 2.1514 | 27.5985 | 11.3869 | 20.9359 | 24.7113 | 62.5 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0

a4619fd63db384586d2853d1e331e066

aiko/maeve-12-6-samsum

aiko

bart

transformers

text2text-generation

gpl-3.0

['en']

['samsum']

['text2text-generation', 'pytorch']

# Maeve - SAMSum Maeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer). This allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with traditional transformers. This specific model has been trained on the SAMSum dataset, and can invert summaries into full-length news articles. Feel free to try examples on the right!

90510154b10db4010e3822049f701d3a

Helsinki-NLP/opus-mt-en-om

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-en-om * source languages: en * target languages: om * OPUS readme: [en-om](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-om/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-om/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-om/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-om/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.om | 21.8 | 0.498 |

d465b0f5719fd322b046d1df2c719ddc

tkazusa/lilt-en-funsd-org

tkazusa

lilt

transformers

token-classification

mit

['funsd-layoutlmv3']

['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. --> # lilt-en-funsd-org This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.8428 - Answer: {'precision': 0.047225501770956316, 'recall': 0.09791921664626684, 'f1': 0.06371963361210674, 'number': 817} - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} - Question: {'precision': 0.08554412560909583, 'recall': 0.2934076137418756, 'f1': 0.13246698805281912, 'number': 1077} - Overall Precision: 0.0730 - Overall Recall: 0.1967 - Overall F1: 0.1065 - Overall Accuracy: 0.2652 ## 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 - training_steps: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

006cf0ef96e56785e09a20c76b7a79e7

stabilityai/sd-vae-ft-mse

stabilityai

diffusers

text-to-image

mit

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

# Improved Autoencoders ## Utilizing These weights are intended to be used with the [🧨 diffusers library](https://github.com/huggingface/diffusers). If you are looking for the model to use with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion), [come here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original). #### How to use with 🧨 diffusers You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` ```py from diffusers.models import AutoencoderKL from diffusers import StableDiffusionPipeline model = "CompVis/stable-diffusion-v1-4" vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae) ``` ## Decoder Finetuning We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder. _Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_ ## Evaluation ### COCO 2017 (256x256, val, 5000 images) | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | | original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD | | ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA | | ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs | ### LAION-Aesthetics 5+ (256x256, subset, 10000 images) | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | | original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD | | ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA | | ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs | ### Visual _Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._ <p align="center"> <br> <b> 256x256: ft-EMA (left), ft-MSE (middle), original (right)</b> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png /> </p>

f9a3bd9ea84070301254786499789aad

Yanjie24/t5-samsung-5e

Yanjie24

t5

transformers

text2text-generation

apache-2.0

['samsum']

['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-samsung-5e This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.7108 - Rouge1: 43.1484 - Rouge2: 20.4563 - Rougel: 36.6379 - Rougelsum: 40.196 - Gen Len: 16.7677 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.873 | 1.0 | 1841 | 1.7460 | 41.7428 | 19.2191 | 35.2428 | 38.8578 | 16.7286 | | 1.8627 | 2.0 | 3682 | 1.7268 | 42.4494 | 19.8301 | 36.1459 | 39.5271 | 16.6039 | | 1.8293 | 3.0 | 5523 | 1.7223 | 42.8908 | 19.9782 | 36.1848 | 39.8482 | 16.7164 | | 1.8163 | 4.0 | 7364 | 1.7101 | 43.2291 | 20.3177 | 36.6418 | 40.2878 | 16.8472 | | 1.8174 | 5.0 | 9205 | 1.7108 | 43.1484 | 20.4563 | 36.6379 | 40.196 | 16.7677 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

93637f1ad83093ee2b669d1a3338a257

muhtasham/small-mlm-snli-target-glue-rte

muhtasham

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. --> # small-mlm-snli-target-glue-rte This model is a fine-tuned version of [muhtasham/small-mlm-snli](https://huggingface.co/muhtasham/small-mlm-snli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3146 - Accuracy: 0.5921 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4047 | 6.41 | 500 | 1.4847 | 0.6318 | | 0.0588 | 12.82 | 1000 | 2.5459 | 0.6245 | | 0.0304 | 19.23 | 1500 | 2.8570 | 0.6101 | | 0.0182 | 25.64 | 2000 | 3.3146 | 0.5921 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

afb7203b7ca63b46be25629bf27d4a0e

Dongyeop/distilbert-base-uncased-finetuned-clinc

Dongyeop

distilbert

transformers

text-classification

apache-2.0

['clinc_oos']

['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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9184 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2890 | 0.7432 | | 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 | | 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 | | 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 | | 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

5874b5fcc9c07474b09e9fd2ab295628

Helsinki-NLP/opus-mt-tc-big-en-tr

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['en', 'tr']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-en-tr Neural machine translation model for translating from English (en) to Turkish (tr). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-02-25 * source language(s): eng * target language(s): tur * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tur/opusTCv20210807+bt_transformer-big_2022-02-25.zip) * more information released models: [OPUS-MT eng-tur README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-tur/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "I know Tom didn't want to eat that.", "On Sundays, we would get up early and go fishing." ] model_name = "pytorch-models/opus-mt-tc-big-en-tr" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Tom'un bunu yemek istemediğini biliyorum. # Pazar günleri erkenden kalkıp balık tutmaya giderdik. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-tr") print(pipe("I know Tom didn't want to eat that.")) # expected output: Tom'un bunu yemek istemediğini biliyorum. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tur/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tur/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-tur | tatoeba-test-v2021-08-07 | 0.68726 | 42.3 | 13907 | 84364 | | eng-tur | flores101-devtest | 0.62829 | 31.4 | 1012 | 20253 | | eng-tur | newsdev2016 | 0.58947 | 21.9 | 1001 | 15958 | | eng-tur | newstest2016 | 0.57624 | 23.4 | 3000 | 50782 | | eng-tur | newstest2017 | 0.58858 | 25.4 | 3007 | 51977 | | eng-tur | newstest2018 | 0.57848 | 22.6 | 3000 | 53731 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 18:11:39 EEST 2022 * port machine: LM0-400-22516.local

a32cf09a6cdbd1407504abb7565f358d

CIDAS/clipseg-rd64

CIDAS

clipseg

transformers

image-segmentation

apache-2.0

['vision', 'image-segmentation']

# CLIPSeg model CLIPSeg model with reduce dimension 64. It was introduced in the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Lüddecke et al. and first released in [this repository](https://github.com/timojl/clipseg). # Intended use cases This model is intended for zero-shot and one-shot image segmentation. # Usage Refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/clipseg).

8ddabaad1deae9bff14b76f914a86d58

anas-awadalla/bart-large-few-shot-k-256-finetuned-squad-infilling-seed-0

anas-awadalla

bart

transformers

text2text-generation

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. --> # bart-large-few-shot-k-256-finetuned-squad-infilling-seed-0 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

abcf751e7a302394bd48e02674f65fb3

johnnydevriese/vit_beans

johnnydevriese

vit

transformers

image-classification

apache-2.0

['beans']

['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_beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1176 - Accuracy: 0.9699 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.2 - Datasets 2.0.0 - Tokenizers 0.10.3

f23a2525bd7ee5f4ca88969f8c698e78

espnet/kan-bayashi_vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave

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_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036266/ 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} } ```

b47a3ca607d3f25be4b50f4dc89510e7

crang/wav2vec2-large-xlsr-53-tatar

crang

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['tt']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

# Wav2Vec2-Large-XLSR-53-Tatar Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tatar using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar") model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Tatar test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar") model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\u2013\u2014\;\:\"\\%\\\]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 30.93 % ## Training The Common Voice `train` and `validation` datasets were used for training.

1b3ec317b3b1d9bd4f1ef5372d1faf7a

jonatasgrosman/wav2vec2-xls-r-1b-german

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['de']

['mozilla-foundation/common_voice_8_0']

['automatic-speech-recognition', 'de', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event']

# Fine-tuned XLS-R 1B model for speech recognition in German Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on German using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). 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, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) ## Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-german") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "de" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-german" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) ``` ## Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-german --dataset mozilla-foundation/common_voice_8_0 --config de --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-german --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-german, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {G}erman}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-german}}, year={2022} } ```

19acbadff96482b10b1c1ddffd888d99

liam168/chat-DialoGPT-small-zh

liam168

gpt2

transformers

text-generation

apache-2.0

['zh']

[]

# liam168/chat-DialoGPT-small-zh ## Model description 用中文聊天数据训练的模型； ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch mode_name = 'liam168/chat-DialoGPT-small-zh' tokenizer = AutoTokenizer.from_pretrained(mode_name) model = AutoModelForCausalLM.from_pretrained(mode_name) # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("Answer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

8b7a966d3be0f8f93653c22e9ffa38d5

fanzru/t5-small-finetuned-xlsum-with-multi-news-10-epoch

fanzru

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. --> # t5-small-finetuned-xlsum-with-multi-news-10-epoch This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2332 - Rouge1: 31.4802 - Rouge2: 9.9475 - Rougel: 24.6687 - Rougelsum: 24.7013 - Gen Len: 18.8025 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7314 | 1.0 | 20543 | 2.3867 | 29.3997 | 8.2875 | 22.8406 | 22.8871 | 18.8204 | | 2.6652 | 2.0 | 41086 | 2.3323 | 30.3992 | 8.9058 | 23.6168 | 23.6626 | 18.8447 | | 2.632 | 3.0 | 61629 | 2.3002 | 30.8662 | 9.2869 | 24.0683 | 24.11 | 18.8122 | | 2.6221 | 4.0 | 82172 | 2.2785 | 31.143 | 9.5737 | 24.3473 | 24.381 | 18.7911 | | 2.5925 | 5.0 | 102715 | 2.2631 | 31.2144 | 9.6904 | 24.4419 | 24.4796 | 18.8133 | | 2.5812 | 6.0 | 123258 | 2.2507 | 31.3371 | 9.7959 | 24.5801 | 24.6166 | 18.7836 | | 2.5853 | 7.0 | 143801 | 2.2437 | 31.3593 | 9.8156 | 24.5533 | 24.5852 | 18.8103 | | 2.5467 | 8.0 | 164344 | 2.2377 | 31.368 | 9.8807 | 24.6226 | 24.6518 | 18.799 | | 2.5571 | 9.0 | 184887 | 2.2337 | 31.4356 | 9.9092 | 24.6543 | 24.6891 | 18.8075 | | 2.5563 | 10.0 | 205430 | 2.2332 | 31.4802 | 9.9475 | 24.6687 | 24.7013 | 18.8025 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.13.1+cpu - Datasets 2.8.0 - Tokenizers 0.10.3

ac8d6698ed7364c29d653d02d0f6ec92

Helsinki-NLP/opus-mt-fi-is

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-fi-is * source languages: fi * target languages: is * OPUS readme: [fi-is](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-is/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-is/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-is/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-is/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fi.is | 25.2 | 0.452 |

88657d6c6bb5668052bc22badb53f056

alex-apostolo/legal-roberta-base-cuad

alex-apostolo

roberta

transformers

question-answering

apache-2.0

['cuad']

['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. --> # legal-roberta-base-cuad This model is a fine-tuned version of [saibo/legal-roberta-base](https://huggingface.co/saibo/legal-roberta-base) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.0260 ## 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: 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 | |:-------------:|:-----:|:------:|:---------------:| | 0.0393 | 1.0 | 51295 | 0.0261 | | 0.0234 | 2.0 | 102590 | 0.0254 | | 0.0234 | 3.0 | 153885 | 0.0260 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

843a98aa77ce33fc2b6590d430172625

recklessrecursion/Warsaw_Pact-clustered

recklessrecursion

distilbert

transformers

question-answering

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # recklessrecursion/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0854 - Train End Logits Accuracy: 0.9722 - Train Start Logits Accuracy: 0.9861 - Validation Loss: 1.3331 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 ## 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: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.0854 | 0.9722 | 0.9861 | 1.3331 | 1.0 | 1.0 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

a59b0a2c64f96ac3322201b0712717e7

facebook/s2t-small-covost2-en-fa-st

facebook

speech_to_text

transformers

automatic-speech-recognition

mit

['en', 'fa']

['covost2']

['audio', 'speech-translation', 'automatic-speech-recognition']

# S2T-SMALL-COVOST2-EN-FA-ST `s2t-small-covost2-en-fa-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Farsi text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-en-fa-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-en-fa-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-en-fa-st is trained on English-Farsi subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using character based SentencePiece vocab. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for en-fa (BLEU score): 11.43 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```

40ab5acdf5dc5e75dd02a0a4d007c7f6

semy/hf-model-0

semy

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. --> # hf-model-0 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: 0.7158 - Accuracy: 0.45 - F1: 0.45 ## 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:| | 0.6107 | 1.0 | 12 | 0.7134 | 0.45 | 0.45 | | 0.5364 | 2.0 | 24 | 0.7158 | 0.45 | 0.45 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

0cda6201c555ac7d93c898cd57f3a5a8

shi-labs/dinat-large-11x11-in22k-in1k-384

shi-labs

dinat

transformers

image-classification

mit

['imagenet-21k', 'imagenet-1k']

['vision', 'image-classification']

# DiNAT (large variant with 11x11 kernel size) DiNAT-Large with a 7x7 kernel pre-trained on ImageNet-21K at 224x224, and fine-tuned with 11x11 kernel size on ImageNet-1K at 384x384. It was introduced in the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). ## Model description DiNAT is a hierarchical vision transformer based on Neighborhood Attention (NA) and its dilated variant (DiNA). Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. NA and DiNA are therefore sliding-window attention patterns, and as a result are highly flexible and maintain translational equivariance. They come with PyTorch implementations through the [NATTEN](https://github.com/SHI-Labs/NATTEN/) package.  [Source](https://paperswithcode.com/paper/dilated-neighborhood-attention-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=dinat) to look for fine-tuned versions on a task that interests you. ### Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, DinatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoImageProcessor.from_pretrained("shi-labs/dinat-large-11x11-in22k-in1k-384") model = DinatForImageClassification.from_pretrained("shi-labs/dinat-large-11x11-in22k-in1k-384") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more examples, please refer to the [documentation](https://huggingface.co/transformers/model_doc/dinat.html#). ### Requirements Other than transformers, this model requires the [NATTEN](https://shi-labs.com/natten) package. If you're on Linux, you can refer to [shi-labs.com/natten](https://shi-labs.com/natten) for instructions on installing with pre-compiled binaries (just select your torch build to get the correct wheel URL). You can alternatively use `pip install natten` to compile on your device, which may take up to a few minutes. Mac users only have the latter option (no pre-compiled binaries). Refer to [NATTEN's GitHub](https://github.com/SHI-Labs/NATTEN/) for more information. ### BibTeX entry and citation info ```bibtex @article{hassani2022dilated, title = {Dilated Neighborhood Attention Transformer}, author = {Ali Hassani and Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2209.15001}, eprint = {2209.15001}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ```

01e44566feb18bbfa13828456eaed7b7

sd-concepts-library/duranduran

sd-concepts-library

mit

[]

### DuranDuran on Stable Diffusion This is the `DuranDuran` 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 a `style`:     

0fdc5aaf1cf37f2a510f1017d9eb15ef

js-rockstar/urdu-colab

js-rockstar

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. --> # urdu-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003 - 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 1.13.3 - Tokenizers 0.10.3

d0373805272fb763bbda3d0f8f5d252e

parambharat/whisper-tiny-south-indic

parambharat

whisper

transformers

automatic-speech-recognition

apache-2.0

['ta', 'te', 'ml', 'kn', 'multilingual']

['whisper-event', '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 Tiny South Indic - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3515 - eval_wer: 70.0806 - eval_runtime: 66.8197 - eval_samples_per_second: 1.497 - eval_steps_per_second: 0.105 - epoch: 5.08 - step: 3000 ## 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: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

ebabdaf871d03d928321034674c7f268

RIOLITE/distilroberta-base-finetuned-aumet-lm

RIOLITE

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. --> # distilroberta-base-finetuned-aumet-lm 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: 2.9210 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 203 | 3.0614 | | No log | 2.0 | 406 | 2.9287 | | 2.9507 | 3.0 | 609 | 2.8713 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

fbd1e91dbb978712d1a4241d7bc6e602

ricardo-filho/bert_base_tcm_0.7