davanstrien/model_cards_with_readmes · Datasets at Hugging Face

DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2

DrishtiSharma

wav2vec2

12

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['mr']

['mozilla-foundation/common_voice_8_0']

null

0

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

true

3,083

false

# wav2vec2-large-xls-r-300m-mr-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset. It achieves the following results on the evaluation set: - Loss: 0.8729 - Wer: 0.4942 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1 Note: Marathi language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000333 - 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: 1000 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.4934 | 9.09 | 200 | 3.7326 | 1.0 | | 3.4234 | 18.18 | 400 | 3.3383 | 0.9996 | | 3.2628 | 27.27 | 600 | 2.7482 | 0.9992 | | 1.7743 | 36.36 | 800 | 0.6755 | 0.6787 | | 1.0346 | 45.45 | 1000 | 0.6067 | 0.6193 | | 0.8137 | 54.55 | 1200 | 0.6228 | 0.5612 | | 0.6637 | 63.64 | 1400 | 0.5976 | 0.5495 | | 0.5563 | 72.73 | 1600 | 0.7009 | 0.5383 | | 0.4844 | 81.82 | 1800 | 0.6662 | 0.5287 | | 0.4057 | 90.91 | 2000 | 0.6911 | 0.5303 | | 0.3582 | 100.0 | 2200 | 0.7207 | 0.5327 | | 0.3163 | 109.09 | 2400 | 0.7107 | 0.5118 | | 0.2761 | 118.18 | 2600 | 0.7538 | 0.5118 | | 0.2415 | 127.27 | 2800 | 0.7850 | 0.5178 | | 0.2127 | 136.36 | 3000 | 0.8016 | 0.5034 | | 0.1873 | 145.45 | 3200 | 0.8302 | 0.5187 | | 0.1723 | 154.55 | 3400 | 0.9085 | 0.5223 | | 0.1498 | 163.64 | 3600 | 0.8396 | 0.5126 | | 0.1425 | 172.73 | 3800 | 0.8776 | 0.5094 | | 0.1258 | 181.82 | 4000 | 0.8651 | 0.5014 | | 0.117 | 190.91 | 4200 | 0.8772 | 0.4970 | | 0.1093 | 200.0 | 4400 | 0.8729 | 0.4942 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

6e83d3525f7b641e6503765a14894a23

theojolliffe/bart-cnn-science-v3-e1-v4-e4-manual

theojolliffe

bart

13

1

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,790

false

# bart-cnn-science-v3-e1-v4-e4-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2615 - Rouge1: 53.36 - Rouge2: 32.0237 - Rougel: 33.2835 - Rougelsum: 50.7455 - Gen Len: 142.0 ## 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 1.0675 | 51.743 | 31.3774 | 34.1939 | 48.7234 | 142.0 | | No log | 2.0 | 84 | 1.0669 | 49.4166 | 28.1438 | 30.188 | 46.0289 | 142.0 | | No log | 3.0 | 126 | 1.1799 | 52.6909 | 31.0174 | 35.441 | 50.0351 | 142.0 | | No log | 4.0 | 168 | 1.2615 | 53.36 | 32.0237 | 33.2835 | 50.7455 | 142.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ff8ab05385267dd05553bcb7c7e386d3

sujathass/distilbert-base-uncased-finetuned-cola

sujathass

distilbert

19

1

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,497

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5532 - Matthews Correlation: 0.5452 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5248 | 1.0 | 535 | 0.5479 | 0.3922 | | 0.3503 | 2.0 | 1070 | 0.5148 | 0.4822 | | 0.2386 | 3.0 | 1605 | 0.5532 | 0.5452 | | 0.1773 | 4.0 | 2140 | 0.6818 | 0.5282 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

130df59874821f2d9ebf48d538947de5

jonatasgrosman/exp_w2v2t_es_unispeech_s767

jonatasgrosman

unispeech

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_es_unispeech_s767 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](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.

063f4b69e511feb90556fe95384c590c

rishabhjain16/whisper_large_to_pf10h

rishabhjain16

whisper

23

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,711

false

# openai/whisper-large This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1412 - Wer: 6.7893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0475 | 2.03 | 500 | 0.1095 | 62.6591 | | 0.0201 | 5.01 | 1000 | 0.1225 | 16.9285 | | 0.0044 | 7.03 | 1500 | 0.1312 | 3.6701 | | 0.0026 | 10.01 | 2000 | 0.1278 | 7.9506 | | 0.0001 | 12.04 | 2500 | 0.1323 | 17.9186 | | 0.0001 | 15.02 | 3000 | 0.1386 | 16.3031 | | 0.0001 | 17.05 | 3500 | 0.1403 | 6.7074 | | 0.0 | 20.02 | 4000 | 0.1412 | 6.7893 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

75ea4a3b12ef02bafcfa00fcab57474a

jayantapaul888/vit-base-patch16-224-finetuned-memes-v3

jayantapaul888

vit

12

9

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,527

false

# vit-base-patch16-224-finetuned-memes-v3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3862 - Accuracy: 0.8478 ## 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: 0.00012 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5649 | 0.99 | 40 | 0.6342 | 0.7488 | | 0.3083 | 1.99 | 80 | 0.4146 | 0.8423 | | 0.1563 | 2.99 | 120 | 0.3900 | 0.8547 | | 0.0827 | 3.99 | 160 | 0.3862 | 0.8478 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

9c7e55749f4cdff0a07234ac3edfa792

ali2066/finetuned_token_3e-05_all_16_02_2022-16_25_56

ali2066

distilbert

13

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,791

false

# finetuned_token_3e-05_all_16_02_2022-16_25_56 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1630 - Precision: 0.3684 - Recall: 0.3714 - F1: 0.3699 - Accuracy: 0.9482 ## 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: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 | | No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 | | No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 | | No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 | | No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

af79ee7cbeb622fde1968682e8e9da46

osanseviero/tips

osanseviero

null

3

0

sklearn

0

null

false

apache-2.0

null

0

[]

false

true

8,875

false

## Baseline Model trained on tips to predict sex Metrics of the best model: accuracy 0.647364 average_precision 0.481257 roc_auc 0.608805 recall_macro 0.588751 f1_macro 0.588435 Name: MultinomialNB(), dtype: float64 See model plot below: <style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False True ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False True ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False True ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div>

a714059d8d88f26113ba49c67eafc6a1

varadhbhatnagar/fc-claim-det-DPEGASUS

varadhbhatnagar

pegasus

10

6

transformers

0

summarization

true

false

apache-2.0

['en']

null

0

[]

false

true

6,250

false

# Model Card for Pegasus for Claim Summarization  This model can be used to summarize noisy claims on social media into clean and concise claims which can be used for downstream tasks in a fact-checking pipeline. # Model Details This is the fine-tuned D PEGASUS model with 'No Preprocessing (NP)' detailed in Table 2 in the paper. This was the best performing model at the time of experimentation. ## Model Description  - **Developed by:** Varad Bhatnagar, Diptesh Kanojia and Kameswari Chebrolu - **Model type:** Summarization - **Language(s) (NLP):** English - **Finetuned from model:** https://huggingface.co/sshleifer/distill-pegasus-cnn-16-4 ## Model Sources  - **Repository:** https://github.com/varadhbhatnagar/FC-Claim-Det - **Paper:** https://aclanthology.org/2022.coling-1.259/ ## Tokenizer Same as https://huggingface.co/sshleifer/distill-pegasus-cnn-16-4 # Uses  ## Direct Use  English to English summarization on noisy fact-checking worthy claims found on social media. ## Downstream Use  Can be used for other tasks in a fact-checking pipeline such as claim matching and evidence retrieval. # Bias, Risks, and Limitations  As the [Google Fact Check Explorer](https://toolbox.google.com/factcheck/explorer) is an ever growing and evolving system, the current Retrieval@k results may not exactly match those in the corresponding paper as those experiments were conducted in the month of April and May 2022. # Training Details ## Training Data  [Data](https://github.com/varadhbhatnagar/FC-Claim-Det/blob/main/public_data/released_data.csv) ## Training Procedure  Finetuning the pretrained Distilled PEGASUS model on the 567 pairs released in our paper. ### Preprocessing No preprocessing of input is done while fine-tuning this model. # Evaluation  Retrieval@5 and Mean Reciprocal Recall scores are reported. ## Results Retrieval@5 = 34.91 MRR = 0.3 Further details can be found in the paper. # Other Models from same work [DBART](https://huggingface.co/varadhbhatnagar/fc-claim-det-DBART) [T5-Base](https://huggingface.co/varadhbhatnagar/fc-claim-det-T5-base) # Citation  **BibTeX:** ``` @inproceedings{bhatnagar-etal-2022-harnessing, title = "Harnessing Abstractive Summarization for Fact-Checked Claim Detection", author = "Bhatnagar, Varad and Kanojia, Diptesh and Chebrolu, Kameswari", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.259", pages = "2934--2945", abstract = "Social media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true to their journalistic processes but cannot cope with its rapid dissemination. We believe that the solution lies in partial automation of the fact-checking life cycle, saving human time for tasks which require high cognition. We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. These queries can then be executed on a general-purpose retrieval system associated with a collection of previously fact-checked claims. We curate an abstractive text summarization dataset comprising noisy claims from Twitter and their gold summaries. It is shown that retrieval performance improves 2x by using popular out-of-the-box summarization models and 3x by fine-tuning them on the accompanying dataset compared to verbatim querying. Our approach achieves Recall@5 and MRR of 35{\%} and 0.3, compared to baseline values of 10{\%} and 0.1, respectively. Our dataset, code, and models are available publicly: https://github.com/varadhbhatnagar/FC-Claim-Det/.", } ``` # Model Card Authors Varad Bhatnagar # Model Card Contact Email: varadhbhatnagar@gmail.com # How to Get Started with the Model Use the code below to get started with the model. ``` from transformers import PegasusForConditionalGeneration, PegasusTokenizerFast tokeizer = PegasusTokenizerFast.from_pretrained('varadhbhatnagar/fc-claim-det-DPEGASUS') model = PegasusForConditionalGeneration.from_pretrained('varadhbhatnagar/fc-claim-det-DPEGASUS') text ='world health organisation has taken a complete u turn and said that corona patients neither need isolate nor quarantine nor social distance and it can not even transmit from one patient to another' tokenized_text = tokeizer.encode(text, return_tensors="pt") summary_ids = model.generate(tokenized_text, num_beams=6, no_repeat_ngram_size=2, min_length=5, max_length=15, early_stopping=True) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) ```

5b7c94c80f74d25dfe6e1045cdb944b5

yoavgur/gpt2-bash-history-baseline2

yoavgur

gpt2

13

4

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,333

false

# gpt2-bash-history-baseline2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 158 | 1.8653 | | No log | 2.0 | 316 | 1.7574 | | No log | 3.0 | 474 | 1.6939 | | 1.9705 | 4.0 | 632 | 1.6597 | | 1.9705 | 5.0 | 790 | 1.6480 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

ed795f6ccd0045793f6ba5368b572a09

microsoft/beit-base-patch16-384

microsoft

beit

6

387

transformers

0

image-classification

true

false

true

apache-2.0

null

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

null

0

['image-classification', 'vision']

false

true

5,476

false

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

1dbbc509c03ae588eaa3dffd2e241f61

WillHeld/bert-base-cased-stsb

WillHeld

bert

14

6

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,896

false

# bert-base-cased-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4322 - Pearson: 0.9007 - Spearmanr: 0.8963 - Combined Score: 0.8985 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 1.6464 | 1.39 | 500 | 0.5662 | 0.8820 | 0.8814 | 0.8817 | | 0.3329 | 2.78 | 1000 | 0.5070 | 0.8913 | 0.8883 | 0.8898 | | 0.173 | 4.17 | 1500 | 0.4465 | 0.8988 | 0.8943 | 0.8966 | | 0.1085 | 5.56 | 2000 | 0.4537 | 0.8958 | 0.8917 | 0.8937 | | 0.0816 | 6.94 | 2500 | 0.4594 | 0.8977 | 0.8933 | 0.8955 | | 0.0621 | 8.33 | 3000 | 0.4450 | 0.8997 | 0.8950 | 0.8974 | | 0.0519 | 9.72 | 3500 | 0.4322 | 0.9007 | 0.8963 | 0.8985 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.7.1 - Datasets 1.18.3 - Tokenizers 0.11.6

1e0e5f307fccadc9cfac79f68b689af7

KoboldAI/fairseq-dense-6.7B-Shinen

KoboldAI

xglm

8

2,536

transformers

0

text-generation

true

false

mit

['en']

null

0

[]

false

true

1,270

false

# Fairseq-dense 6.7B - Shinen ## Model Description Fairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content. **Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.** ## Training data The training data contains user-generated stories from sexstories.com. All stories are tagged using the following way: ``` [Theme: <theme1>, <theme2> ,<theme3>] <Story goes here> ``` ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-6.7B-Shinen') >>> generator("She was staring at me", do_sample=True, min_length=50) [{'generated_text': 'She was staring at me with a look that said it all. She wanted me so badly tonight that I wanted'}] ``` ### Limitations and Biases Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). ### BibTeX entry and citation info ``` Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts ```

e298e6401a1be407af94527004b4109f

Yuang/unilm-base-chinese-news-sum

Yuang

bert

7

19

transformers

1

text2text-generation

true

false

apache-2.0

['zh']

null

0

['unilm']

false

true

858

false

# unilm-base-chinese-news-sum ```sh pip install git+https://github.com/Liadrinz/transformers-unilm # 安装兼容HuggingFace的UniLM模型代码 ``` ```py from unilm import UniLMTokenizer, UniLMForConditionalGeneration news_article = ( "12月23日，河北石家庄。8岁哥哥轻车熟路哄睡弟弟，姿势标准动作熟练。" "妈妈杨女士表示：哥哥很喜欢弟弟，因为心思比较细，自己平时带孩子的习惯他都会跟着学习，" "哄睡孩子也都会争着来，技巧很娴熟，两人在一块很有爱，自己感到很幸福，平时帮了自己很大的忙，感恩有这么乖的宝宝。" ) tokenizer = UniLMTokenizer.from_pretrained("Yuang/unilm-base-chinese-news-sum") model = UniLMForConditionalGeneration.from_pretrained("Yuang/unilm-base-chinese-news-sum") inputs = tokenizer(news_article, return_tensors="pt") output_ids = model.generate(**inputs, max_new_tokens=16) output_text = tokenizer.decode(output_ids[0]) print(output_text) # "[CLS] <news_article> [SEP] <news_summary> [SEP]" news_summary = output_text.split("[SEP]")[1].strip() print(news_summary) ```

16675c0877927c8da4182bfdfa3700ae

deblagoj/distilbert-base-uncased-finetuned-emotion

deblagoj

distilbert

28

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,343

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2225 - Accuracy: 0.919 - F1: 0.9191 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.814 | 1.0 | 250 | 0.3153 | 0.904 | 0.9016 | | 0.2515 | 2.0 | 500 | 0.2225 | 0.919 | 0.9191 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1

e4ff784545aac7f2f902a872bc7c6536

mrm8488/t5-small-finetuned-text-simplification

mrm8488

t5

11

3

transformers

0

text2text-generation

true

false

apache-2.0

null

['wiki_auto_asset_turk']

null

0

['generated_from_trainer']

true

1,859

false

# t5-small-finetuned-text-simplification This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wiki_auto_asset_turk dataset. It achieves the following results on the evaluation set: - Loss: 0.1217 - Rouge2 Precision: 0.5537 - Rouge2 Recall: 0.4251 - Rouge2 Fmeasure: 0.4616 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.1604 | 1.0 | 15119 | 0.1156 | 0.5567 | 0.4266 | 0.4633 | | 0.1573 | 2.0 | 30238 | 0.1163 | 0.5534 | 0.4258 | 0.462 | | 0.1552 | 3.0 | 45357 | 0.1197 | 0.5527 | 0.4244 | 0.4608 | | 0.1514 | 4.0 | 60476 | 0.1214 | 0.5528 | 0.4257 | 0.4617 | | 0.1524 | 5.0 | 75595 | 0.1217 | 0.5537 | 0.4251 | 0.4616 | ### Framework versions - Transformers 4.22.0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

91b5322cd79ec24f9edbb93e41baa655

an303042/Jocelyn_Hobbie_Diffusion_v1

an303042

null

23

3

diffusers

1

text-to-image

false

creativeml-openrail-m

['en']

null

2

1

0

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

false

true

1,643

false

### Jocelyn Hobbie Diffusion v1 This model was created to celebrate the works of Jocelyn Hobbie - A wonderful contemporary artist. Check out her works @ www.jocelynhobbie.com and @jocelynhobbie **Token to use is "jclnhbe style" ** ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ### Examples ![Example](https://huggingface.co/an303042/Jocelyn_Hobbie_Diffusion/resolve/main/00026-865999057.png) ![Example](https://huggingface.co/an303042/Jocelyn_Hobbie_Diffusion/resolve/main/00030-2232312440.png) ![Example](https://huggingface.co/an303042/Jocelyn_Hobbie_Diffusion/resolve/main/00033-1415196133.png)

9188b06f898687a1aba05a90eb0493f3

Kaku0o0/distilbert-base-uncased-finetuned-squad

Kaku0o0

distilbert

16

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,279

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6090 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 274 | 1.5943 | | 0.9165 | 2.0 | 548 | 1.5836 | | 0.9165 | 3.0 | 822 | 1.6090 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

d72b717e9045dd10b1935091cc228aee

jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-8_austria-2_s452

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

480

false

# exp_w2v2r_de_xls-r_accent_germany-8_austria-2_s452 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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.

ace2107aff97558bfdd0b20289eec12f

luccazen/finetuning-sentiment-model-3000-samples

luccazen

distilbert

19

11

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

1

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3026 - Accuracy: 0.8667 - F1: 0.8667 ## 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 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

b0c2608194605d909e3c6fefc09b6c05

jonatasgrosman/exp_w2v2t_zh-cn_no-pretraining_s805

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['zh-CN']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'zh-CN']

false

true

420

false

# exp_w2v2t_zh-cn_no-pretraining_s805 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](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.

8a91a3682d09b46388d94c6315ab861a

nandysoham/Human_Development_Index-clustered

nandysoham

distilbert

8

0

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,877

false

# nandysoham/Human_Development_Index-clustered This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2234 - Train End Logits Accuracy: 0.9410 - Train Start Logits Accuracy: 0.9479 - Validation Loss: 1.1060 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 0.6667 - 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2234 | 0.9410 | 0.9479 | 1.1060 | 0.6667 | 0.6667 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

84fceefdce7b88d4c94bcbc34365dc34

cwkeam/mctct-large

cwkeam

mctct

9

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['librispeech_asr', 'common_voice']

null

0

['speech']

false

true

2,641

false

# M-CTC-T Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal. ![model image](https://raw.githubusercontent.com/cwkeam/scientific-images/main/MCTCT/mctct-arch.png) The original Flashlight code, model checkpoints, and Colab notebook can be found at https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl . ## Citation [Paper](https://arxiv.org/abs/2111.00161) Authors: Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert ``` @article{lugosch2021pseudo, title={Pseudo-Labeling for Massively Multilingual Speech Recognition}, author={Lugosch, Loren and Likhomanenko, Tatiana and Synnaeve, Gabriel and Collobert, Ronan}, journal={ICASSP}, year={2022} } ``` Additional thanks to [Chan Woo Kim](https://huggingface.co/cwkeam) and [Patrick von Platen](https://huggingface.co/patrickvonplaten) for porting the model from Flashlight to PyTorch. # Training method ![model image](https://raw.githubusercontent.com/cwkeam/scientific-images/main/MCTCT/mctct-slimipl.png) TO-DO: replace with the training diagram from paper For more information on how the model was trained, please take a look at the [official paper](https://arxiv.org/abs/2111.00161). # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import MCTCTForCTC, MCTCTProcessor model = MCTCTForCTC.from_pretrained("speechbrain/mctct-large") processor = MCTCTProcessor.from_pretrained("speechbrain/mctct-large") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_features = processor(ds[0]["audio"]["array"], return_tensors="pt").input_features # retrieve logits logits = model(input_features).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` Results for Common Voice, averaged over all languages: *Character error rate (CER)*: | Valid | Test | |-------|------| | 21.4 | 23.3 |

ed65c127da6e0aa38494aefb4897f005

Helsinki-NLP/opus-mt-lue-en

Helsinki-NLP

marian

10

14

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-lue-en * source languages: lue * target languages: en * OPUS readme: [lue-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lue.en | 31.7 | 0.469 |

f491a004b92f2843883c300950a40c6a

IIIT-L/hing-mbert-finetuned-code-mixed-DS

IIIT-L

bert

10

1

transformers

0

text-classification

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,380

false

# hing-mbert-finetuned-code-mixed-DS This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7248 - Accuracy: 0.7364 - Precision: 0.6847 - Recall: 0.7048 - F1: 0.6901 ## 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: 2.7277800745684633e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 43 - 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 | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.6977 | 2.0 | 497 | 0.7248 | 0.7364 | 0.6847 | 0.7048 | 0.6901 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.1+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

e21f506810cb337315a23c35890a68f0

BAAI/AltCLIP-m9

BAAI

altclip

10

68

transformers

5

text-to-image

true

false

creativeml-openrail-m

['zh']

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)']

false

true

4,172

false

# AltCLIP-m9 It supports English(En), Chinese(Zh), Spanish(Es), French(Fr), Russian(Ru), Japanese(Ja), Korean(Ko), Arabic(Ar) and Italian(It) languages. | 名称 Name | 任务 Task | 语言 Language(s) | 模型 Model | Github | |:------------------:|:----------:|:-------------------:|:--------:|:------:| | AltCLIP-m9 | Text-Image | Multilingual | CLIP | [FlagAI](https://github.com/FlagAI-Open/FlagAI) | ## 简介 Brief Introduction 我们提出了一个简单高效的方法去训练更加优秀的九语CLIP模型。命名为AltCLIP-m9。AltCLIP训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LIAON](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus) AltCLIP-m9模型可以为本项目中的AltDiffusion-m9模型提供支持，关于AltDiffusion-m9模型的具体信息可查看[此教程](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion/README.md) 。模型代码已经在 [FlagAI](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) 上开源，权重位于我们搭建的 [modelhub](https://model.baai.ac.cn/model-detail/100077) 上。我们还提供了微调，推理，验证的脚本，欢迎试用。 We propose a simple and efficient method to train a better multilingua CLIP model. Named AltCLIP-m9. AltCLIP-m9 is trained with training data from [WuDao dataset](https://data.baai.ac.cn/details/WuDaoCorporaText) and [Liaon](https://huggingface.co/datasets/laion/laion2B-en). The AltCLIP-m9 model can provide support for the AltDiffusion-m9 model in this project. Specific information on the AltDiffusion model can be found in [this tutorial](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion/README.md). The model code has been open sourced on [FlagAI](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) and the weights are located on [modelhub](https://model.baai.ac.cn/model-detail/100077). We also provide scripts for fine-tuning, inference, and validation, so feel free to try them out. ## 引用关于AltCLIP，我们已经推出了相关报告，有更多细节可以查阅，如对您的工作有帮助，欢迎引用。 If you find this work helpful, please consider to cite ``` @article{https://doi.org/10.48550/arxiv.2211.06679, doi = {10.48550/ARXIV.2211.06679}, url = {https://arxiv.org/abs/2211.06679}, author = {Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences}, title = {AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ## 训练 Training 训练共有两个阶段。在平行知识蒸馏阶段，我们只是使用平行语料文本来进行蒸馏（平行语料相对于图文对更容易获取且数量更大）。在多语对比学习阶段，我们使用少量的中-英图像-文本对（每种语言6百万）来训练我们的文本编码器以更好地适应图像编码器。 There are two phases of training. In the parallel knowledge distillation phase, we only use parallel corpus texts for distillation (parallel corpus is easier to obtain and larger in number compared to image text pairs). In the multilingual comparison learning phase, we use a small number of text-image pairs (about 6 million in each language) to train our text encoder to better fit the image encoder. ## 下游效果 Performance ![](./xtd.png) ## 可视化效果 Visualization effects 基于AltCLIP，我们还开发了AltDiffusion模型，可视化效果如下。 Based on AltCLIP, we have also developed the AltDiffusion model, visualized as follows. ![](m9.png) ## 模型推理 Inference Please download the code from [FlagAI AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) ```python from PIL import Image import requests # transformers version >= 4.21.0 from modeling_altclip import AltCLIP from processing_altclip import AltCLIPProcessor # now our repo's in private, so we need `use_auth_token=True` model = AltCLIP.from_pretrained("BAAI/AltCLIP-m9") processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP-m9") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) outputs = model(**inputs) logits_per_image = outputs.logits_per_image # this is the image-text similarity score probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```

68750b38809d102196dd00ad93645524

gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier

gabrielgmendonca

bert

17

10

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,357

false

# bert-base-portuguese-cased-finetuned-chico-xavier This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7196 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.0733 | 1.0 | 561 | 1.8147 | | 1.8779 | 2.0 | 1122 | 1.7624 | | 1.8345 | 3.0 | 1683 | 1.7206 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

d5d143c8259c4d79cce030265b0aa0fc

Yagorka/ddpm-butterflies-256_new_data

Yagorka

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['imagefolder']

null

0

[]

false

true

1,219

false

# ddpm-butterflies-256_new_data ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-butterflies-256_new_data/tensorboard?#scalars)

1c9932db46508cc533b9cde330e7af62

DunnBC22/distilbert-base-uncased_research_articles_multilabel

DunnBC22

distilbert

10

2

transformers

1

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,647

false

# distilbert-base-uncased_research_articles_multilabel 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: 0.1956 - F1: 0.8395 - Roc Auc: 0.8909 - Accuracy: 0.6977 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.3043 | 1.0 | 263 | 0.2199 | 0.8198 | 0.8686 | 0.6829 | | 0.2037 | 2.0 | 526 | 0.1988 | 0.8355 | 0.8845 | 0.7010 | | 0.1756 | 3.0 | 789 | 0.1956 | 0.8395 | 0.8909 | 0.6977 | | 0.1579 | 4.0 | 1052 | 0.1964 | 0.8371 | 0.8902 | 0.6919 | | 0.1461 | 5.0 | 1315 | 0.1991 | 0.8353 | 0.8874 | 0.6953 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

c76d52c9647733727a993ed42ba09e51

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

Helsinki-NLP

marian

13

7

transformers

1

translation

true

false

cc-by-4.0

['ca', 'en', 'es', 'oc']

null

2

1

0

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

true

6,454

false

# opus-mt-tc-big-en-cat_oci_spa Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa). 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: 2022-03-13 * source language(s): eng * target language(s): cat spa * valid target language labels: >>cat<< >>spa<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-cat+oci+spa README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cat+oci+spa/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>cat<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>spa<< Why do you want Tom to go there with me?", ">>spa<< She forced him to eat spinach." ] model_name = "pytorch-models/opus-mt-tc-big-en-cat_oci_spa" 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: # ¿Por qué quieres que Tom vaya conmigo? # Ella lo obligó a comer espinacas. ``` 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-cat_oci_spa") print(pipe(">>spa<< Why do you want Tom to go there with me?")) # expected output: ¿Por qué quieres que Tom vaya conmigo? ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.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-cat | tatoeba-test-v2021-08-07 | 0.66414 | 47.8 | 1631 | 12344 | | eng-spa | tatoeba-test-v2021-08-07 | 0.73725 | 57.0 | 16583 | 134710 | | eng-cat | flores101-devtest | 0.66071 | 41.5 | 1012 | 27304 | | eng-oci | flores101-devtest | 0.56192 | 25.4 | 1012 | 27305 | | eng-spa | flores101-devtest | 0.56288 | 28.1 | 1012 | 29199 | | eng-spa | newssyscomb2009 | 0.58431 | 31.4 | 502 | 12503 | | eng-spa | news-test2008 | 0.56622 | 30.0 | 2051 | 52586 | | eng-spa | newstest2009 | 0.57988 | 30.5 | 2525 | 68111 | | eng-spa | newstest2010 | 0.62343 | 37.4 | 2489 | 65480 | | eng-spa | newstest2011 | 0.62424 | 39.1 | 3003 | 79476 | | eng-spa | newstest2012 | 0.63006 | 39.6 | 3003 | 79006 | | eng-spa | newstest2013 | 0.60291 | 35.8 | 3000 | 70528 | | eng-spa | tico19-test | 0.73224 | 52.5 | 2100 | 66563 | ## 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 16:40:45 EEST 2022 * port machine: LM0-400-22516.local

424dcc1893eb9b2aad20ff43c4edf4b9

RichardsonTXCarpetCleaning/AreaRugCleaningRichardsonTX

RichardsonTXCarpetCleaning

null

2

0

null

0

null

false

other

null

0

[]

false

true

459

false

Area Rug Cleaning Richardson TX https://carpetcleaning-richardson.com/area-rug-cleaning.html (972) 454-9815 Do you need the best cleaning services in town from Rug Shampooers?Do you want to bring back the natural beauty of your rugs after they have lost their original appearance?By simply calling our professionals, Richardson TX Carpet Cleaning will be able to properly clean them for you, leaving them looking good and brightening up your home at any time.

694c0350332d4430ac749829753baccd

l3cube-pune/hi-random-twt-1m

l3cube-pune

bert

8

4

transformers

0

fill-mask

true

false

cc-by-4.0

['hi']

null

0

[]

false

true

551

false

A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on random 1 million Hindi Tweets.<br> More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br> ``` @article{gokhale2022spread, title={Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection}, author={Gokhale, Omkar and Kane, Aditya and Patankar, Shantanu and Chavan, Tanmay and Joshi, Raviraj}, journal={arXiv preprint arXiv:2210.04267}, year={2022} } ```

3349905774dfdab2c24196023f189b4e

Lvxue/finetuned-mt5-base-10epoch

Lvxue

mt5

14

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,003

false

# finetuned-mt5-base-10epoch This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 1.2607 ## 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: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

a65143b8c7e50ca50dab0697f67ae252

MultiBertGunjanPatrick/multiberts-seed-1-1900k

MultiBertGunjanPatrick

bert

7

3

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-1']

false

true

6,487

false

# MultiBERTs Seed 1 Checkpoint 1900k (uncased) Seed 1 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-1](https://hf.co/multberts-seed-1). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-1900k') model = BertModel.from_pretrained("multiberts-seed-1-1900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

663ace922b0b996678a08f74deb67fc9

huggingnft/boredapeyachtclub

huggingnft

null

5

47

transformers

1

unconditional-image-generation

false

mit

null

['huggingnft/boredapeyachtclub']

null

0

['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation']

false

true

2,210

false

# Hugging NFT: boredapeyachtclub ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/boredapeyachtclub). Dataset is available [here](https://huggingface.co/datasets/huggingnft/boredapeyachtclub). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/boredapeyachtclub). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```

1f3c1d6d2f0cd0860896659206f084f3

jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s905

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

475

false

# exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s905 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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.

ac1314255eb7b3a6bf1f2417270ff61c

DLochmelis33/22s-dl-sentiment-1

DLochmelis33

distilbert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['yelp_review_full']

null

0

['generated_from_trainer']

true

1,033

false

# 22s-dl-sentiment-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.2574 - Accuracy: 0.9542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.0 - Tokenizers 0.12.1

d3c88d2135db8a2544841ea69f23b9e5

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

zannabethl

marian

13

0

transformers

0

text2text-generation

true

false

apache-2.0

null

['wmt16']

null

0

['generated_from_trainer']

true

927

false

# opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

76a413edc8df66c234a0f39348f724fa

MeshalAlamr/wav2vec2-xls-r-300m-arabic_speech_commands_half

MeshalAlamr

wav2vec2

10

3

transformers

0

audio-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,896

false

# wav2vec2-xls-r-300m-arabic_speech_commands_half 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. It achieves the following results on the evaluation set: - Loss: 0.2678 - Accuracy: 0.9975 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.6816 | 0.99 | 28 | 3.4693 | 0.1575 | | 3.0227 | 1.99 | 56 | 2.5330 | 0.2775 | | 2.3345 | 2.99 | 84 | 1.8723 | 0.5925 | | 1.6785 | 3.99 | 112 | 1.2944 | 0.8092 | | 1.2606 | 4.99 | 140 | 0.8933 | 0.9425 | | 1.0024 | 5.99 | 168 | 0.6041 | 0.9817 | | 0.6478 | 6.99 | 196 | 0.3814 | 0.9933 | | 0.4768 | 7.99 | 224 | 0.2678 | 0.9975 | | 0.4143 | 8.99 | 252 | 0.2198 | 0.9967 | | 0.3278 | 9.99 | 280 | 0.1993 | 0.9967 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

837c135a53555b1a28b5536206baab65

jhaochenz/finetuned_gpt2-xl_sst2_negation0.01_pretrainedFalse_epochs10

jhaochenz

gpt2

16

0

transformers

0

text-generation

true

false

mit

null

['sst2']

null

0

['generated_from_trainer']

true

1,628

false

# finetuned_gpt2-xl_sst2_negation0.01_pretrainedFalse_epochs10 This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 4.2831 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7704 | 1.0 | 1323 | 3.1202 | | 1.0733 | 2.0 | 2646 | 3.4126 | | 0.867 | 3.0 | 3969 | 3.6512 | | 0.7585 | 4.0 | 5292 | 3.7861 | | 0.6945 | 5.0 | 6615 | 3.8881 | | 0.6429 | 6.0 | 7938 | 3.9924 | | 0.5997 | 7.0 | 9261 | 4.0998 | | 0.5643 | 8.0 | 10584 | 4.1730 | | 0.5279 | 9.0 | 11907 | 4.2446 | | 0.4999 | 10.0 | 13230 | 4.2831 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.7.0 - Datasets 2.8.0 - Tokenizers 0.13.2

7fc5cb52fbb45a8f32b834c029cd52c6

tosin/dialogpt_mwoz

tosin

gpt2

9

16

transformers

3

conversational

true

false

cc-by-4.0

['en']

['multi_woz_v22']

null

0

['conversational', 'transformers']

false

true

2,435

false

## DialoGPT_MWOZ This is a fine-tuned model of DialoGPT (medium) on the MultiWOZ v2.2 dataset. It is intended to be used as a conversational system. The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, train, hospital and police. The perplexity achieved on the test and validation sets are 10.25 and 10.71, respectively. More information about the original pre-trained model can be found [here](https://huggingface.co/microsoft/DialoGPT-medium) * Generation example from an interactive environment: |Role | Response | |---------|------------| |User | I would like to have breakfast. | |Bot | There are several options for Chinese in the city centre. Are you looking for a particular price range? | |User | I'm looking for free food | |Bot | There are 4 Chinese restaurants that are in the cheap price range. 3 in centre and one in north. | |User | Not good enough | |Bot | How about Charlie Chan? It is a Chinese restaurant in the centre of town. | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) The paper for this work was accepted at the Northern Lights Deep Learning (NLDL) conference 2022. Arxiv paper: [https://arxiv.org/pdf/2110.06273.pdf](https://arxiv.org/pdf/2110.06273.pdf) ### 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 tokenizer = AutoTokenizer.from_pretrained("tosin/dialogpt_mwoz") model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_mwoz") # 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("DialoGPT_MWOZ_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

a341239ce94becdd77f1d92edd3ab5ef

annahaz/bert-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3

annahaz

bert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,773

false

# bert-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3 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: 0.8767 - Accuracy: 0.7094 - F1: 0.7184 - Precision: 0.6491 - Recall: 0.8043 - Mae: 0.2906 - Tn: 338 - Fp: 200 - Fn: 90 - Tp: 370 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4767 | 1.0 | 1346 | 0.6853 | 0.6323 | 0.6501 | 0.5789 | 0.7413 | 0.3677 | 290 | 248 | 119 | 341 | | 0.3783 | 2.0 | 2692 | 0.7041 | 0.6653 | 0.6528 | 0.6255 | 0.6826 | 0.3347 | 350 | 188 | 146 | 314 | | 0.2803 | 3.0 | 4038 | 0.8767 | 0.7094 | 0.7184 | 0.6491 | 0.8043 | 0.2906 | 338 | 200 | 90 | 370 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

c9ee3a0ca85591b750f03ea027c7063d

farleyknight/patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20

farleyknight

bigbird_pegasus

13

2

transformers

0

text2text-generation

true

false

apache-2.0

null

['farleyknight/big_patent_5_percent']

null

0

['generated_from_trainer']

true

2,664

false

# patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20 This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) on the farleyknight/big_patent_5_percent dataset. It achieves the following results on the evaluation set: - Loss: 2.2617 - Rouge1: 37.3764 - Rouge2: 13.2442 - Rougel: 26.011 - Rougelsum: 31.0145 - Gen Len: 113.8789 ## 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: 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: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.6121 | 0.08 | 5000 | 2.5652 | 35.0673 | 12.0073 | 24.5471 | 28.9315 | 119.9866 | | 2.5182 | 0.17 | 10000 | 2.4797 | 34.6909 | 11.6432 | 24.87 | 28.1543 | 119.2043 | | 2.5102 | 0.25 | 15000 | 2.4238 | 35.8574 | 12.2402 | 25.0712 | 29.5607 | 115.2890 | | 2.4292 | 0.33 | 20000 | 2.3869 | 36.0133 | 12.2453 | 25.4039 | 29.483 | 112.5920 | | 2.3678 | 0.41 | 25000 | 2.3594 | 35.238 | 11.6833 | 25.0449 | 28.3313 | 119.1739 | | 2.3511 | 0.5 | 30000 | 2.3326 | 36.7755 | 12.8394 | 25.7218 | 30.2594 | 110.5819 | | 2.3334 | 0.58 | 35000 | 2.3125 | 36.6317 | 12.7493 | 25.5388 | 30.094 | 115.5998 | | 2.3833 | 0.66 | 40000 | 2.2943 | 37.1219 | 13.1564 | 25.7571 | 30.8666 | 113.8222 | | 2.341 | 0.75 | 45000 | 2.2813 | 36.4962 | 12.6225 | 25.6904 | 29.9741 | 115.9845 | | 2.3179 | 0.83 | 50000 | 2.2725 | 37.3535 | 13.1596 | 25.7385 | 31.056 | 117.7754 | | 2.3164 | 0.91 | 55000 | 2.2654 | 36.9191 | 12.9316 | 25.7586 | 30.4691 | 116.1670 | | 2.3046 | 0.99 | 60000 | 2.2618 | 37.3992 | 13.2731 | 26.0327 | 31.0338 | 114.5195 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.4.0 - Tokenizers 0.12.1

b92767257b5f485b805b057cfc637666

fathyshalab/all-roberta-large-v1-kitchen_and_dining-2-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,523

false

# all-roberta-large-v1-kitchen_and_dining-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

6e54ddf30303149db7d54742f5cf3fdd

hfl/chinese-electra-small-generator

hfl

electra

10

22

transformers

0

fill-mask

true

false

apache-2.0

['zh']

null

0

[]

false

true

1,963

false

**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.** ## Chinese ELECTRA Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA. ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants. This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra) You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/ymcui/HFL-Anthology ## Citation If you find our resource or paper is useful, please consider including the following citation in your paper. - https://arxiv.org/abs/2004.13922 ``` @inproceedings{cui-etal-2020-revisiting, title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", author = "Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", pages = "657--668", } ```

e069d866b48aaa5e05864080cb00307e

muhtasham/small-mlm-glue-qqp

muhtasham

bert

12

8

transformers

1

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,597

false

# small-mlm-glue-qqp This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5151 ## 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 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8517 | 0.4 | 500 | 2.7156 | | 2.8184 | 0.8 | 1000 | 2.6309 | | 2.7461 | 1.2 | 1500 | 2.5335 | | 2.5785 | 1.6 | 2000 | 2.5472 | | 2.5753 | 2.0 | 2500 | 2.5667 | | 2.4744 | 2.4 | 3000 | 2.4824 | | 2.4448 | 2.8 | 3500 | 2.5490 | | 2.476 | 3.2 | 4000 | 2.4906 | | 2.3352 | 3.6 | 4500 | 2.5151 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

7440ec9b2bfe1419644995ce705d6ec1

jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s365

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

501

false

# exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s365 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

3a6aade8201e30053f31f9afe284f8be

96harsh56/roberta-finetuned-subjqa-movies_1110pm

96harsh56

roberta

13

11

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

995

false

# roberta-finetuned-subjqa-movies_1110pm This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

f97a2db645f6d3404f1eab3568e7b9c5

shivam/xls-r-300m-marathi

shivam

wav2vec2

24

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['mr']

['mozilla-foundation/common_voice_8_0']

null

0

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

true

2,444

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset. It achieves the following results on the mozilla-foundation/common_voice_8_0 mr test set: - Without LM + WER: 48.53 + CER: 10.63 - With LM + WER: 38.27 + CER: 8.91 ## 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: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 400.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.2706 | 22.73 | 500 | 4.0174 | 1.0 | | 3.2492 | 45.45 | 1000 | 3.2309 | 0.9908 | | 1.9709 | 68.18 | 1500 | 1.0651 | 0.8440 | | 1.4088 | 90.91 | 2000 | 0.5765 | 0.6550 | | 1.1326 | 113.64 | 2500 | 0.4842 | 0.5760 | | 0.9709 | 136.36 | 3000 | 0.4785 | 0.6013 | | 0.8433 | 159.09 | 3500 | 0.5048 | 0.5419 | | 0.7404 | 181.82 | 4000 | 0.5052 | 0.5339 | | 0.6589 | 204.55 | 4500 | 0.5237 | 0.5897 | | 0.5831 | 227.27 | 5000 | 0.5166 | 0.5447 | | 0.5375 | 250.0 | 5500 | 0.5292 | 0.5487 | | 0.4784 | 272.73 | 6000 | 0.5480 | 0.5596 | | 0.4421 | 295.45 | 6500 | 0.5682 | 0.5467 | | 0.4047 | 318.18 | 7000 | 0.5681 | 0.5447 | | 0.3779 | 340.91 | 7500 | 0.5783 | 0.5347 | | 0.3525 | 363.64 | 8000 | 0.5856 | 0.5367 | | 0.3393 | 386.36 | 8500 | 0.5960 | 0.5359 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0

60eb6938d5de282313300646ccf600ae

Helsinki-NLP/opus-mt-en-mkh

Helsinki-NLP

marian

11

17

transformers

0

translation

true

false

apache-2.0

['en', 'vi', 'km', 'mkh']

null

1

0

['translation']

false

true

2,387

false

### eng-mkh * source group: English * target group: Mon-Khmer languages * OPUS readme: [eng-mkh](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-mkh/README.md) * model: transformer * source language(s): eng * target language(s): kha khm khm_Latn mnw vie vie_Hani * 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: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.zip) * test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.test.txt) * test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-kha.eng.kha | 0.1 | 0.015 | | Tatoeba-test.eng-khm.eng.khm | 0.2 | 0.226 | | Tatoeba-test.eng-mnw.eng.mnw | 0.7 | 0.003 | | Tatoeba-test.eng.multi | 16.5 | 0.330 | | Tatoeba-test.eng-vie.eng.vie | 33.7 | 0.513 | ### System Info: - hf_name: eng-mkh - source_languages: eng - target_languages: mkh - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-mkh/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'vi', 'km', 'mkh'] - src_constituents: {'eng'} - tgt_constituents: {'vie_Hani', 'mnw', 'vie', 'kha', 'khm_Latn', 'khm'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.test.txt - src_alpha3: eng - tgt_alpha3: mkh - short_pair: en-mkh - chrF2_score: 0.33 - bleu: 16.5 - brevity_penalty: 1.0 - ref_len: 34734.0 - src_name: English - tgt_name: Mon-Khmer languages - train_date: 2020-07-27 - src_alpha2: en - tgt_alpha2: mkh - prefer_old: False - long_pair: eng-mkh - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

1ee49bf659127fbeb0d8666783c21dcc

KarelDO/gpt2.CEBaB_confounding.observational.sa.5-class.seed_42

KarelDO

gpt2

15

2

transformers

0

null

true

false

mit

['en']

['OpenTable']

null

0

['generated_from_trainer']

true

1,084

false

# gpt2.CEBaB_confounding.observational.sa.5-class.seed_42 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.9425 - Accuracy: 0.6091 - Macro-f1: 0.5206 - Weighted-macro-f1: 0.5595 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.5.2 - Tokenizers 0.12.1

575588f07f423ae923db8ec78fbb9bb2

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

dkasti

xlm-roberta

10

11

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,314

false

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2388 - F1: 0.8233 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8099 | 1.0 | 70 | 0.3035 | 0.7333 | | 0.2766 | 2.0 | 140 | 0.2661 | 0.7948 | | 0.1792 | 3.0 | 210 | 0.2388 | 0.8233 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

02684ba200c93ae4df77ce81e7c13054

nlp-waseda/roberta-base-japanese-with-auto-jumanpp

nlp-waseda

roberta

7

292

transformers

2

fill-mask

true

false

cc-by-sa-4.0

['ja']

['wikipedia', 'cc100']

null

0

1

0

1

[]

false

true

2,188

false

# nlp-waseda/roberta-base-japanese-with-auto-jumanpp ## Model description This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp") sentence = '早稲田大学で自然言語処理を[MASK]する。' encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. ## Tokenization `BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-base-japanese](https://huggingface.co/nlp-waseda/roberta-base-japanese). Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). ## Vocabulary The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). ## Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took a week using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 1e-4 - per_device_train_batch_size: 256 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 4096 - max_seq_length: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 700000 - warmup_steps: 10000 - mixed_precision_training: Native AMP ## Performance on JGLUE See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.

ee5ef6bef5d35ebc16a6e3898aed0490

kit-nlp/transformers-ud-japanese-electra-base-discriminator-irony

kit-nlp

electra

8

5

transformers

0

text-classification

true

false

cc-by-sa-4.0

['ja']

null

0

[]

false

true

1,198

false

# Electra Base Japanese Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [transformers-ud-japanese-electra-ginza](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator/tree/main), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Megagon Labs ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-basic-char-v2-irony" } ```

689dc644faf1377cbd734eabffa6c260

Yuto01/distilbert-base-uncased-finetuned-imdb

Yuto01

distilbert

23

0

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,278

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4442 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6985 | 1.0 | 157 | 2.5612 | | 2.562 | 2.0 | 314 | 2.4226 | | 2.5316 | 3.0 | 471 | 2.4218 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

d28467f6ba25ad4749d6fcec74c4e688

sd-dreambooth-library/metahuman-rkr

sd-dreambooth-library

null

29

2

diffusers

1

null

false

mit

null

2

0

1

0

[]

false

true

1,700

false

### metahuman rkr on Stable Diffusion via Dreambooth #### model by hans120791 This your the Stable Diffusion model fine-tuned the metahuman rkr concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks rkr** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/8.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/1.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/5.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/0.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/7.jpeg) ![image 6](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/10.jpeg) ![image 7](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/6.jpeg) ![image 8](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/4.jpeg) ![image 9](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/9.jpeg) ![image 10](https://huggingface.co/sd-dreambooth-library/metahuman-rkr/resolve/main/concept_images/2.jpeg)

f98d4ae3df5227b5b33e5a25d5330cde

FluxML/resnet18

FluxML

null

3

0

null

0

null

false

mit

null

4

0

3

1

0

[]

false

true

517

false

ResNet18 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [add the Metalhead.jl package to your environment](https://pkgdocs.julialang.org/v1/managing-packages/#Adding-packages). Then execute: ```julia using Metalhead model = ResNet(18; pretrain = true) ```

db581bb6bd39497eac67071d8c8b58c4

ufal/byt5-small-multilexnorm2021-tr

ufal

t5

6

3

transformers

0

text2text-generation

true

false

apache-2.0

['tr']

['mc4', 'wikipedia', 'multilexnorm']

null

0

['lexical normalization']

false

true

2,759

false

# Fine-tuned ByT5-small for MultiLexNorm (Turkish version) ![model image](https://github.com/ufal/multilexnorm2021/raw/master/img/overall.png) This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). ## How to use The model was *not* fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [**the interactive demo on Colab notebook**](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models. ## How to cite ```bibtex @inproceedings{wnut-ufal, title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}", author = "Samuel, David and Straka, Milan", booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)", year = "2021", publisher = "Association for Computational Linguistics", address = "Punta Cana, Dominican Republic" } ``` ## ByT5 - Small ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*

9667530f8b3a767d6b7c589eef18494e

clu-ling/whisper-large-v2-japanese-5k-steps

clu-ling

whisper

33

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_11_0']

null

0

['generated_from_trainer']

true

2,978

false

# whisper-large-v2-japanese-5k-steps This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Japanese CommonVoice dataset (v11).. It achieves the following results on the evaluation set: - Loss: 0.4200 - Wer: 0.7449 ## Model description This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users. ## Training and evaluation data - Training Data: CommonVoice (v11) train split - Validation Data: CommonVoice (v11) Validation split - Test Data: CommonVoice (v11) Test split ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 50 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0111 | 7.63 | 1000 | 0.3210 | 0.7888 | | 0.0007 | 15.27 | 2000 | 0.3585 | 0.7478 | | 0.0003 | 22.9 | 3000 | 0.3937 | 0.7432 | | 0.0002 | 30.53 | 4000 | 0.4123 | 0.7443 | | 0.0002 | 38.17 | 5000 | 0.4200 | 0.7449 | ### Transcription ```python from datasets import load_dataset, Audio import torch from transformers import WhisperProcessor, WhisperForConditionalGeneration # device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # load the model processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps") model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps").to(device) forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe") # load the dataset commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="validation", streaming=True) commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000)) sample = next(iter(commonvoice_eval))["audio"] # features and generate token ids input_features = processor(sample["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids) # decode transcription = processor.batch_decode(predicted_ids) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) print(transcription) ``` ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

b92733fb94d2d94892f0c8301eca625f

misterneil/distilbert-base-uncased-finetuned-emotion

misterneil

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,345

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2116 - Accuracy: 0.9295 - F1: 0.9293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8487 | 1.0 | 250 | 0.3135 | 0.909 | 0.9051 | | 0.2515 | 2.0 | 500 | 0.2116 | 0.9295 | 0.9293 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

6a276eb2c70616ccb70f5b2b13c01583

IDEA-CCNL/Wenzhong2.0-GPT2-110M-BertTokenizer-chinese

IDEA-CCNL

gpt2

6

171

transformers

3

text-generation

true

false

apache-2.0

['zh']

null

0

['generate', 'gpt2']

false

true

3,494

false

# Wenzhong2.0-GPT2-110M-BertTokenizer-chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 善于处理NLG任务，中文版的GPT2-Small。基于BertTokenizer，实现字级别token，更便于受控文本生成。 Focused on handling NLG tasks, Chinese GPT2-Small. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言生成 NLG | 闻仲 Wenzhong | GPT2 | 110M | 中文 Chinese | ## 模型信息 Model Information 类似于Wenzhong2.0-GPT2-3.5B-chinese，我们实现了一个small版本的12层的Wenzhong2.0-GPT2-110M-BertTokenizer-chinese，并在悟道（300G版本）上面进行预训练。本次开源别于之前开源的闻仲-GPT2系列，主要在于将BPE的分词换成了BertTokenzier的字级别分词。 Similar to Wenzhong2.0-GPT2-3.5B-chinese, we implement a small size Wenzhong2.0-GPT2-110M-BertTokenizer-chinese with 12 layers, which is pre-trained on Wudao Corpus (300G version).This open source version is different from the previous open source Wenzhong-GPT2 series, mainly because the word segmentation of BPE is replaced by the word level word segmentation of BertTokenzier. ## 使用 Usage ### 加载模型 Loading Models ```python from transformers import BertTokenizer,GPT2LMHeadModel hf_model_path = 'IDEA-CCNL/Wenzhong-GPT2-110M' tokenizer = BertTokenizer.from_pretrained(hf_model_path) model = GPT2LMHeadModel.from_pretrained(hf_model_path) ``` ### 使用示例 Usage Examples 这里需要提一点，GPT在训练的时候是没有添加special_tokens的，BertTokenizer会默认补充special_tokens，所以在tokenzier的时候需要将add_special_tokens设置为false，这样生产效果会更好。 ```python def generate_word_level(input_text,n_return=5,max_length=128,top_p=0.9): inputs = tokenizer(input_text,return_tensors='pt',add_special_tokens=False).to(model.device) gen = model.generate( inputs=inputs['input_ids'], max_length=max_length, do_sample=True, top_p=top_p, eos_token_id=21133, pad_token_id=0, num_return_sequences=n_return) sentences = tokenizer.batch_decode(gen) for idx,sentence in enumerate(sentences): print(f'sentence {idx}: {sentence}') print('*'*20) return gen outputs = generate_word_level('西湖的景色',n_return=5,max_length=128) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

e36f706aec3695ee91445996bf69254c

anas-awadalla/bart-base-few-shot-k-512-finetuned-squad-seed-0

anas-awadalla

bart

16

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

988

false

# bart-base-few-shot-k-512-finetuned-squad-seed-0 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

63cfe6f933e4a28487bc4aa7ddcd238f

Jethuestad/distilbert-base-uncased-test2

Jethuestad

distilbert

10

6

transformers

0

token-classification

true

false

apache-2.0

null

['wnut_17']

null

0

['generated_from_trainer']

true

1,545

false

# distilbert-base-uncased-test2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3055 - Precision: 0.5278 - Recall: 0.3957 - F1: 0.4523 - Accuracy: 0.9462 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2889 | 0.5439 | 0.3503 | 0.4262 | 0.9453 | | No log | 2.0 | 426 | 0.2938 | 0.5236 | 0.3800 | 0.4404 | 0.9457 | | 0.0544 | 3.0 | 639 | 0.3055 | 0.5278 | 0.3957 | 0.4523 | 0.9462 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

3eddf0667a212d05ba094e44056024da

google/tapas-base-finetuned-wtq

google

tapas

8

31,285

transformers

63

table-question-answering

true

false

apache-2.0

['en']

['wikitablequestions']

null

0

['tapas']

false

true

7,105

false

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

435a49fa91a4647f091568eac00ed51e

jlartey10/wav2vec2-large-xls-r-300m-tr-colab

jlartey10

wav2vec2

14

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,425

false

# wav2vec2-large-xls-r-300m-tr-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: 1.1786 - Wer: 0.5933 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3421 | 14.81 | 400 | 1.1795 | 0.5922 | | 0.113 | 29.63 | 800 | 1.1786 | 0.5933 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

1bf781f8a51f2c4e8813d1a0f7a9a232

eykarim/stable-diffusion-v1

eykarim

null

21

26

diffusers

1

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

2,061

false

# Fork of [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) > Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. > For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). For more information about the model, license and limitations check the original model card at [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4). ### License (CreativeML OpenRAIL-M) The full license can be found here: https://huggingface.co/spaces/CompVis/stable-diffusion-license --- This repository implements a custom `handler` task for `text-to-image` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/create_handler.ipynb) included, on how to create the `handler.py` ### expected Request payload ```json { "inputs": "A prompt used for image generation" } ``` below is an example on how to run a request using Python and `requests`. ## Run Request ```python import json from typing import List import requests as r import base64 from PIL import Image from io import BytesIO ENDPOINT_URL = "" HF_TOKEN = "" # helper decoder def decode_base64_image(image_string): base64_image = base64.b64decode(image_string) buffer = BytesIO(base64_image) return Image.open(buffer) def predict(prompt:str=None): payload = {"inputs": code_snippet,"parameters": parameters} response = r.post( ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": prompt} ) resp = response.json() return decode_base64_image(resp["image"]) prediction = predict( prompt="the first animal on the mars" ) ``` expected output ![sample](sample.jpg)

02e252d8b8662adf82fe9de6d7455da4

jonatasgrosman/exp_w2v2t_th_vp-nl_s947

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['th']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_th_vp-nl_s947 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (th)](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.

28385eaceae96f7f37c0228ba47ea2e6

anuragshas/wav2vec2-large-xls-r-300m-bg

anuragshas

wav2vec2

25

9

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['bg']

['mozilla-foundation/common_voice_8_0']

null

0

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

true

3,586

false

# XLS-R-300M - Bulgarian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.2473 - Wer: 0.3002 ## 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: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - 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: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1589 | 3.48 | 400 | 3.0830 | 1.0 | | 2.8921 | 6.96 | 800 | 2.6605 | 0.9982 | | 1.3049 | 10.43 | 1200 | 0.5069 | 0.5707 | | 1.1349 | 13.91 | 1600 | 0.4159 | 0.5041 | | 1.0686 | 17.39 | 2000 | 0.3815 | 0.4746 | | 0.999 | 20.87 | 2400 | 0.3541 | 0.4343 | | 0.945 | 24.35 | 2800 | 0.3266 | 0.4132 | | 0.9058 | 27.83 | 3200 | 0.2969 | 0.3771 | | 0.8672 | 31.3 | 3600 | 0.2802 | 0.3553 | | 0.8313 | 34.78 | 4000 | 0.2662 | 0.3380 | | 0.8068 | 38.26 | 4400 | 0.2528 | 0.3181 | | 0.7796 | 41.74 | 4800 | 0.2537 | 0.3073 | | 0.7621 | 45.22 | 5200 | 0.2503 | 0.3036 | | 0.7611 | 48.7 | 5600 | 0.2477 | 0.2991 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset mozilla-foundation/common_voice_8_0 --config bg --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ### Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "anuragshas/wav2vec2-large-xls-r-300m-bg" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "bg", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text # => "и надутият му ката блоонкурем взе да се събира" ``` ### Eval results on Common Voice 8 "test" (WER): | Without LM | With LM (run `./eval.py`) | |---|---| | 30.07 | 21.195 |

84dd25bb62749346a323e6c010245e17

Pikachu/Crust

Pikachu

null

3

0

null

0

null

false

apache-2.0

['en']

["gathered from Uberduck's discord server, put together by Crust."]

null

0

['synthesis', 'speech', 'speech synthesis']

false

true

3,183

false

# **CRUST - RELEASED** (Chungus Related Uberduck's Speech toy) # Welcome to Crust 🍕⭕ Crust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results with only 1 minute of data. The results are surprisingly good and because of the lower dataset, batch size can be lowered and the model is generally faster than other models. ### What is a multispeaker model? A multispeaker model is a model that has been trained on multiple speakers, the model first generates an "average" voice of all of the speakers and then tunes the different speakers on that average voice. If you have a lot of speakers, individual results won't be that great, as the model only has ~250+ mb to work with, but this is great for finetuning different voices on it because the model has learned an "average" voice. This average voice has the knowledge of all voices included in the dataset. Core: A multispeaker model is a model trained on multiple speakers. ### How does this make training possible with 1 minute of training data? The model has been trained on 168 datasets, ~20 hours of data, or ~19.8 thousand audio files. This is smaller than LJ speech but it has way more variety in voices, which LJ speech doesn't have. this variety allows the model to learn speech in different genders, accents, pitches, and other important factors, meaning that it knows a lot more in terms of voices. Finetuning this on 1 minute of data is possible because it already has a decently close match of your voice somewhere in its latent space. Core: The multispeaker has more knowledge of multiple people speaking, making it surprisingly good at training on low-minute datasets. ### What are the downsides? **-Training time.** Training time sadly does still take a while, but considering you might only be training using 1 minute of data, it would take shorter than training it on the Lj-speech model, but would not come close to corentj's realtime voice cloning, it would be more accurate. **-Clean datasets.** We still doubt if the model would be able to be trained on datasets that have loud noise in them or have background music in them, realistically, it would not be able to be trained on these kinds of datasets, so before you train, please use a clean dataset. **-Inference.** Even though this model can be trained on 1 minute of data, we still recommend training it on more, we can't promise good results if the model doesn't have sufficient data, this would ideally be measured in syllables or phonemes, but minutes is a lot easier. **-Audio quality.** Sadly, the model has only been trained on 22050 hz and mono audio files, while this still sounds good when there's a Hi-Fi Gan vocoder, It's still going to not have stereo sound (which would not be that useful) or 44100 hz audio quality on its own. Sadly the Hi-Fi Gan vocoder does also bring in artifacts into the wav files which makes synthesis not as realistic. We used [**Uberduck's TTS Pipeline on github**](https://github.com/uberduck-ai/uberduck-ml-dev) To train our model.

b4e2cbf3881321700051a97f3faf1cb1

chaitanya97/german_trained

chaitanya97

wav2vec2

12

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,568

false

# german_trained This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-german) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9367 - 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: 0.0003 - 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: 5 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 12.0352 | 5.0 | 5 | 12.6165 | 1.0 | | 4.0249 | 10.0 | 10 | 6.6453 | 1.0 | | 2.6661 | 15.0 | 15 | 5.7873 | 1.0 | | 2.4123 | 20.0 | 20 | 4.3250 | 1.0 | | 1.9481 | 25.0 | 25 | 3.9899 | 1.0 | | 1.7533 | 30.0 | 30 | 3.9367 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3

169b3cbebecb4672672b3e3d6123e16f

luigisaetta/whisper-tiny2-it

luigisaetta

whisper

17

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_11_0']

null

0

['generated_from_trainer', 'whisper-event']

true

1,361

false

# luigisaetta/whisper-tiny2-it This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4686 - Wer: 25.9110 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5765 | 2.01 | 1000 | 0.5728 | 32.2181 | | 0.3726 | 4.02 | 2000 | 0.5035 | 28.4606 | | 0.2789 | 6.04 | 3000 | 0.4861 | 26.7894 | | 0.2996 | 8.05 | 4000 | 0.4694 | 26.0279 | | 0.2925 | 10.06 | 5000 | 0.4686 | 25.9110 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

5fbbe039ed45a8d11885f9a4f170f0b2

shizil/distilbert-base-uncased-finetuned-squad

shizil

distilbert

19

3

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,285

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8489 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7098 | 1.0 | 5681 | 2.3952 | | 2.3633 | 2.0 | 11362 | 1.9956 | | 2.1293 | 3.0 | 17043 | 1.8489 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

e8acf8ef09d24d168de5bf0646ab244a

uaritm/df_lik_n_mg_221

uaritm

t5

7

3

transformers

0

text2text-generation

true

false

mit

['ru', 'uk', 'multilingual']

null

1

0

['russian', 'ukrainian']

false

true

512

false

# A little about the model The model is trained to answer questions about health topics (Open-book question answering-comprehend). cointegrated/rut5-base-multitask For training, a compact T5 model was used: cointegrated/rut5-base-multitask The training was conducted on a small set out of 220 thousand pairs of question-answer sentences, so it still does not work as correctly as we would like. The model is not a medical application and it is strongly discouraged to use the model for medical purposes!

e046b2b219e39c780ad78d166b5599b0

sanchit-gandhi/wav2vec2-large-tedlium

sanchit-gandhi

wav2vec2

9

6

transformers

1

automatic-speech-recognition

true

false

true

apache-2.0

['en']

['LIUM/tedlium']

null

0

1

0

1

['speech']

false

true

2,881

false

# Wav2Vec2-Large-Tedlium The Wav2Vec2 large model fine-tuned on the TEDLIUM corpus. The model is initialised with Facebook's [Wav2Vec2 large LV-60k](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoint pre-trained on 60,000h of audiobooks from the LibriVox project. It is fine-tuned on 452h of TED talks from the [TEDLIUM](https://huggingface.co/datasets/LIUM/tedlium) corpus (Release 3). When using the model, make sure that your speech input is sampled at 16Khz. The model achieves a word error rate (WER) of 8.4% on the dev set and 8.2% on the test set. [Training logs](https://wandb.ai/sanchit-gandhi/tedlium/runs/10c85yc4?workspace=user-sanchit-gandhi) document the training and evaluation progress over 50k steps of fine-tuning. See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how this model was fine-tuned. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") # load dummy dataset ds = load_dataset("sanchit-gandhi/tedlium_dummy", split="validation") # process audio inputs input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) print("Target: ", ds["text"][0]) print("Transcription: ", transcription[0]) ``` ## Evaluation This code snippet shows how to evaluate **Wav2Vec2-Large-Tedlium** on the TEDLIUM test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer tedlium_eval = load_dataset("LIUM/tedlium", "release3", split="test") model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium").to("cuda") processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = tedlium_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ```

3f618c125b27ddc1d02470c6cb79ffe4

jannesg/takalane_zul_roberta

jannesg

roberta

8

7

transformers

0

fill-mask

true

false

true

mit

['zul']

null

0

['zul', 'fill-mask', 'pytorch', 'roberta', 'masked-lm']

false

true

1,060

false

# Takalani Sesame - Zulu 🇿🇦 <img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/> ## Model description Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performance with larger languages around the world. ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_zul_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_zul_roberta") ``` #### Limitations and bias Updates will be added continously to improve performance. ## Training data Data collected from [https://wortschatz.uni-leipzig.de/en](https://wortschatz.uni-leipzig.de/en) <br/> **Sentences:** 410000 ## Training procedure No preprocessing. Standard Huggingface hyperparameters. ## Author Jannes Germishuys [website](http://jannesgg.github.io)

4c5357f5616807cc6a10f95039e94c24

zasheza/wav2vec2-base-timit-demo-colab-1

zasheza

wav2vec2

16

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,761

false

# wav2vec2-base-timit-demo-colab-1 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: 0.9634 - Wer: 0.4398 ## 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: 6 - 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: 800 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8991 | 5.26 | 500 | 1.4319 | 0.7522 | | 0.8555 | 10.53 | 1000 | 0.7895 | 0.5818 | | 0.4584 | 15.79 | 1500 | 0.7198 | 0.5211 | | 0.3096 | 21.05 | 2000 | 0.7983 | 0.5118 | | 0.2165 | 26.32 | 2500 | 0.7893 | 0.4745 | | 0.163 | 31.58 | 3000 | 0.8779 | 0.4589 | | 0.1144 | 36.84 | 3500 | 0.9256 | 0.4540 | | 0.0886 | 42.11 | 4000 | 0.9184 | 0.4530 | | 0.0668 | 47.37 | 4500 | 0.9634 | 0.4398 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

ea59332d428c9b0d3e6e6d9bf0c8489e

ashrielbrian/xlm-roberta-base-finetuned-panx-de-fr

ashrielbrian

xlm-roberta

10

4

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,315

false

# xlm-roberta-base-finetuned-panx-de-fr 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: 0.1636 - F1: 0.8567 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1810 | 0.8263 | | 0.1477 | 2.0 | 1430 | 0.1561 | 0.8488 | | 0.095 | 3.0 | 2145 | 0.1636 | 0.8567 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3

124653195a6a7520f21aee497db4b1a3

tomekkorbak/goofy_pasteur

tomekkorbak

gpt2

23

83

transformers

0

null

true

false

mit

['en']

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

null

1

0

1

0

['generated_from_trainer']

true

7,472

false

# goofy_pasteur - **Repository: https://github.com/tomekkorbak/aligned-pretraining-objectives** - **Paper: Arxiv link to be added** ## Model description This model was trained using [pile-detoxify](https://huggingface.co/datasets/tomekkorbak/pile-detoxify), which is data from [The Pile](https://huggingface.co/datasets/the_pile), annotated based on toxicity detected by [Detoxify](https://github.com/unitaryai/detoxify). ## Intended uses & limitations This model has been trained to generate text that receives a low score for toxicity from [Detoxify](https://github.com/unitaryai/detoxify). While we have promising results with the methods used to avoid toxic text, we cannot guarantee that it will output text that is fully aligned with non-toxicity in every situation. This model and its associated datasets are intended for research purposes only and should not be deployed anywhere. Please take care to avoid misusing the datasets used to train this model (where toxicity and personal identifiable information are annotated) or putting anybody in danger by publicizing their information. ## Training and evaluation data This model was trained using [pile-detoxify](https://huggingface.co/datasets/tomekkorbak/pile-detoxify). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'goofy_pasteur', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/20d87pk8

3b21f10400385cc1fead2a991255332a

tyqiangz/xlm-roberta-base-finetuned-chaii

tyqiangz

xlm-roberta

12

9

transformers

0

question-answering

true

false

mit

null

[]

null

0

['generated_from_trainer']

false

true

1,256

false

# xlm-roberta-base-finetuned-chaii 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: 0.4651 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.92 | 1.0 | 899 | 0.4482 | | 0.8055 | 2.0 | 1798 | 0.3225 | | 0.7485 | 3.0 | 2697 | 0.4651 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

903c6f565f28b0dcd2212c9630e73d3d

LowGI/STT_Model_4

LowGI

wav2vec2

9

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,182

false

# STT_Model_4 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: 0.2311 - Wer: 0.1373 ## 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: 8 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4196 | 5.68 | 500 | 0.9866 | 0.6983 | | 0.3696 | 11.36 | 1000 | 0.8788 | 0.4010 | | 0.1182 | 17.05 | 1500 | 0.2187 | 0.1947 | | 0.0658 | 22.73 | 2000 | 0.2578 | 0.1757 | | 0.0421 | 28.41 | 2500 | 0.2178 | 0.1609 | | 0.0346 | 34.09 | 3000 | 0.2038 | 0.1584 | | 0.0285 | 39.77 | 3500 | 0.2187 | 0.1594 | | 0.0228 | 45.45 | 4000 | 0.2114 | 0.1445 | | 0.0262 | 51.14 | 4500 | 0.2201 | 0.1631 | | 0.0162 | 56.82 | 5000 | 0.2078 | 0.1424 | | 0.0135 | 62.5 | 5500 | 0.1989 | 0.1393 | | 0.0128 | 68.18 | 6000 | 0.2118 | 0.1410 | | 0.0104 | 73.86 | 6500 | 0.2158 | 0.1361 | | 0.0081 | 79.55 | 7000 | 0.2154 | 0.1348 | | 0.0067 | 85.23 | 7500 | 0.2107 | 0.1358 | | 0.0067 | 90.91 | 8000 | 0.2161 | 0.1373 | | 0.0056 | 96.59 | 8500 | 0.2311 | 0.1373 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

293c8686900c5fd418414e3ca9843a3e

ShihTing/PanJuOffset_TwoClass

ShihTing

vit

6

5

transformers

0

image-classification

true

false

apache-2.0

null

0

['vision', 'image-classification']

false

true

1,560

false

# PanJu offset detect by image Use fintune from google/vit-base-patch16-224(https://huggingface.co/google/vit-base-patch16-224) ## Dataset ```python DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 329 }) validation: Dataset({ features: ['image', 'label'], num_rows: 56 }) }) ``` 36 Break and 293 Normal in train 5 Break and 51 Normal in validation ## Intended uses ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python # Load image import torch from transformers import ViTFeatureExtractor, ViTForImageClassification,AutoModel from PIL import Image import requests url='https://datasets-server.huggingface.co/assets/ShihTing/IsCausewayOffset/--/ShihTing--IsCausewayOffset/validation/0/image/image.jpg' image = Image.open(requests.get(url, stream=True).raw) # Load model from transformers import AutoFeatureExtractor, AutoModelForImageClassification device = torch.device('cpu') extractor = AutoFeatureExtractor.from_pretrained('ShihTing/PanJuOffset_TwoClass') model = AutoModelForImageClassification.from_pretrained('ShihTing/PanJuOffset_TwoClass') # Predict inputs = extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits Prob = outputs.logits.softmax(dim=-1).tolist() print(Prob) # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ```

1d16f73b1de3059a040820e317f167de

okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad

okho0653

bert

13

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,071

false

# Bio_ClinicalBERT-zero-shot-finetuned-50cad This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1475 - Accuracy: 0.5 - F1: 0.6667 ## 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 ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

dbbbd8ac2dc99df7a1b90c7780cbd37b

augustocsc/gpt-m

augustocsc

gpt2

13

49

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

2,102

false

# gpt-m This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0096 ## 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: 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: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3775 | 0.06 | 500 | 0.0302 | | 0.0207 | 0.11 | 1000 | 0.0188 | | 0.0182 | 0.17 | 1500 | 0.0179 | | 0.0171 | 0.22 | 2000 | 0.0152 | | 0.0178 | 0.28 | 2500 | 0.0161 | | 0.0147 | 0.33 | 3000 | 0.0150 | | 0.0157 | 0.39 | 3500 | 0.0137 | | 0.0137 | 0.44 | 4000 | 0.0126 | | 0.0133 | 0.5 | 4500 | 0.0137 | | 0.012 | 0.56 | 5000 | 0.0120 | | 0.0122 | 0.61 | 5500 | 0.0117 | | 0.0129 | 0.67 | 6000 | 0.0118 | | 0.0113 | 0.72 | 6500 | 0.0114 | | 0.0106 | 0.78 | 7000 | 0.0109 | | 0.0119 | 0.83 | 7500 | 0.0108 | | 0.0122 | 0.89 | 8000 | 0.0102 | | 0.0105 | 0.94 | 8500 | 0.0101 | | 0.0094 | 1.0 | 9000 | 0.0098 | | 0.01 | 1.06 | 9500 | 0.0097 | | 0.0097 | 1.11 | 10000 | 0.0096 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

380d15c66b02710e30b082e1a90405c7

anantoj/T5-summarizer-simple-wiki-v2

anantoj

t5

23

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,275

false

# T5-summarizer-simple-wiki-v2 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: 2.0866 ## 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: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2575 | 1.0 | 14719 | 2.1173 | | 2.2663 | 2.0 | 29438 | 2.0926 | | 2.2092 | 3.0 | 44157 | 2.0866 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

bed1a5a45f8b90a202461269c7c821ed

Tsubame/ddpm-butterflies-64

Tsubame

null

17

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,227

false

# ddpm-butterflies-64 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Tsubame/ddpm-butterflies-64/tensorboard?#scalars)

248fd1bba82acb68669c346a9019a404

haesun/distilbert-base-uncased-distilled-clinc

haesun

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['clinc_oos']

null

0

['generated_from_trainer']

true

1,792

false

# 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: 0.1894 - Accuracy: 0.9448 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6133 | 1.0 | 318 | 1.0679 | 0.7290 | | 0.8231 | 2.0 | 636 | 0.5164 | 0.8652 | | 0.4289 | 3.0 | 954 | 0.3019 | 0.9168 | | 0.2722 | 4.0 | 1272 | 0.2336 | 0.9335 | | 0.214 | 5.0 | 1590 | 0.2117 | 0.94 | | 0.1914 | 6.0 | 1908 | 0.2007 | 0.9445 | | 0.1785 | 7.0 | 2226 | 0.1947 | 0.9435 | | 0.1716 | 8.0 | 2544 | 0.1919 | 0.9468 | | 0.1674 | 9.0 | 2862 | 0.1901 | 0.9452 | | 0.1659 | 10.0 | 3180 | 0.1894 | 0.9448 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

a6e4be0233b77389f3b4b9c9737a6066

Hoax0930/kyoto_marian_mod_3

Hoax0930

marian

14

1

transformers

0

translation

true

false

apache-2.0

null

0

['translation', 'generated_from_trainer']

true

1,071

false

# kyoto_marian_mod_3_5 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8052 - Bleu: 18.4305 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

78deeef2e9b4896927745ca99503ea2c

royam0820/xlm-roberta-base-finetuned-panx-it

royam0820

xlm-roberta

10

23

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - F1: 0.8124 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 | | 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 | | 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

3a2879f06f18ad084d192880b27f74e4

devtanumisra/finetuning-insult-model-deberta

devtanumisra

deberta-v2

14

8

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,107

false

# finetuning-insult-model-deberta This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9472 - Accuracy: 0.7458 - F1: 0.7630 - Precision: 0.7332 - Recall: 0.7953 ## 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 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

526444f17d845fe9f1d8bf0c457d8f50

Lvxue/distilled-mt5-small-0.02-0.5

Lvxue

mt5

14

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,038

false

# distilled-mt5-small-0.02-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8160 - Bleu: 7.448 - Gen Len: 44.2241 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

d53ff25017c6bbc8946f185c24d262af

muhtasham/tiny-mlm-glue-rte-target-glue-cola

muhtasham

bert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,107

false

# tiny-mlm-glue-rte-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-rte](https://huggingface.co/muhtasham/tiny-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7986 - Matthews Correlation: 0.1168 ## 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 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6097 | 1.87 | 500 | 0.6209 | 0.0 | | 0.6011 | 3.73 | 1000 | 0.6173 | 0.0 | | 0.5827 | 5.6 | 1500 | 0.6197 | 0.0622 | | 0.5534 | 7.46 | 2000 | 0.6410 | 0.0939 | | 0.5244 | 9.33 | 2500 | 0.6664 | 0.1184 | | 0.5087 | 11.19 | 3000 | 0.6684 | 0.1327 | | 0.4867 | 13.06 | 3500 | 0.6789 | 0.0999 | | 0.4693 | 14.93 | 4000 | 0.7124 | 0.1109 | | 0.4483 | 16.79 | 4500 | 0.7333 | 0.1388 | | 0.4303 | 18.66 | 5000 | 0.7486 | 0.1287 | | 0.4105 | 20.52 | 5500 | 0.7961 | 0.1321 | | 0.4046 | 22.39 | 6000 | 0.7986 | 0.1168 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

81ef4f9eae5b899e627580e496844142

skparida/wav2vec2-base-timit-demo-google-colab

skparida

wav2vec2

12

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,998

false

# wav2vec2-base-timit-demo-google-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: 0.5090 - Wer: 0.3435 ## 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: 8 - 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5501 | 1.0 | 500 | 1.9752 | 0.9950 | | 0.8608 | 2.01 | 1000 | 0.5051 | 0.5035 | | 0.43 | 3.01 | 1500 | 0.4485 | 0.4525 | | 0.2921 | 4.02 | 2000 | 0.4658 | 0.4332 | | 0.2248 | 5.02 | 2500 | 0.4262 | 0.4268 | | 0.1863 | 6.02 | 3000 | 0.4126 | 0.3977 | | 0.1542 | 7.03 | 3500 | 0.4795 | 0.3987 | | 0.1374 | 8.03 | 4000 | 0.4882 | 0.3982 | | 0.1231 | 9.04 | 4500 | 0.4312 | 0.3790 | | 0.1082 | 10.04 | 5000 | 0.4344 | 0.3679 | | 0.0949 | 11.04 | 5500 | 0.4720 | 0.3769 | | 0.0897 | 12.05 | 6000 | 0.5382 | 0.3706 | | 0.0816 | 13.05 | 6500 | 0.4946 | 0.3618 | | 0.0726 | 14.06 | 7000 | 0.5383 | 0.3630 | | 0.0656 | 15.06 | 7500 | 0.4944 | 0.3693 | | 0.059 | 16.06 | 8000 | 0.5096 | 0.3639 | | 0.0572 | 17.07 | 8500 | 0.5066 | 0.3572 | | 0.0559 | 18.07 | 9000 | 0.5366 | 0.3610 | | 0.0468 | 19.08 | 9500 | 0.5103 | 0.3604 | | 0.0413 | 20.08 | 10000 | 0.5126 | 0.3496 | | 0.044 | 21.08 | 10500 | 0.5055 | 0.3524 | | 0.0351 | 22.09 | 11000 | 0.5526 | 0.3515 | | 0.0328 | 23.09 | 11500 | 0.4884 | 0.3512 | | 0.032 | 24.1 | 12000 | 0.5167 | 0.3474 | | 0.0271 | 25.1 | 12500 | 0.5027 | 0.3495 | | 0.0229 | 26.1 | 13000 | 0.5076 | 0.3444 | | 0.0252 | 27.11 | 13500 | 0.5122 | 0.3464 | | 0.0224 | 28.11 | 14000 | 0.5133 | 0.3447 | | 0.0236 | 29.12 | 14500 | 0.5090 | 0.3435 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

b73bc8068a38d9842a1a28aa5e1fccbd

jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s50

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

497

false

# exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s50 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.

99ab1b4e801eeb604d455abf4166e5b3

sd-concepts-library/artist-yukiko-kanagai

sd-concepts-library

null

10

0

null

1

null

false

mit

null

0

[]

false

true

1,232

false

### Artist_Yukiko Kanagai on Stable Diffusion This is the `<Yukiko Kanagai >` 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`: ![<Yukiko Kanagai > 0](https://huggingface.co/sd-concepts-library/artist-yukiko-kanagai/resolve/main/concept_images/0.jpeg) ![<Yukiko Kanagai > 1](https://huggingface.co/sd-concepts-library/artist-yukiko-kanagai/resolve/main/concept_images/4.jpeg) ![<Yukiko Kanagai > 2](https://huggingface.co/sd-concepts-library/artist-yukiko-kanagai/resolve/main/concept_images/1.jpeg) ![<Yukiko Kanagai > 3](https://huggingface.co/sd-concepts-library/artist-yukiko-kanagai/resolve/main/concept_images/3.jpeg) ![<Yukiko Kanagai > 4](https://huggingface.co/sd-concepts-library/artist-yukiko-kanagai/resolve/main/concept_images/2.jpeg)

490ed1b19eca329789151e9d49bec8da

reinoudbosch/xlm-roberta-base-finetuned-panx-en

reinoudbosch

xlm-roberta

10

11

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,313

false

# xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4025 - F1: 0.6778 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1069 | 1.0 | 50 | 0.5201 | 0.5010 | | 0.4975 | 2.0 | 100 | 0.4503 | 0.6198 | | 0.3705 | 3.0 | 150 | 0.4025 | 0.6778 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.0

ece74ab822853eb0563103f75426e4f2

CLTL/MedRoBERTa.nl

CLTL

roberta

7

2,910

transformers

0

fill-mask

true

false

mit

['nl']

null

0

[]

false

true

1,229

false

# MedRoBERTa.nl ## Description This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of MedRoBERTa.nl can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medical_language_model. ## Intended use The model can be fine-tuned on any type of task. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. ## Data The model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. The training data was anonymized before starting the pre-training procedure. ## Privacy By anonymizing the training data we made sure the model did not learn any representative associations linked to names. Apart from the training data, the model's vocabulary was also anonymized. This ensures that the model can not predict any names in the generative fill-mask task. ## Authors Stella Verkijk, Piek Vossen ## Reference Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.

873d46ae03ff8457e2f7b42384fd2b5a

d2lee/finetuning-sentiment-model-3000-samples

d2lee

distilbert

13

12

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,049

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3150 - Accuracy: 0.8633 - F1: 0.8656 ## 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 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

c404266336f43fcb6ca4c8815ce452e6

lmqg/mt5-small-koquad-qag

lmqg

mt5

13

36

transformers

0

text2text-generation

true

false

cc-by-4.0

['ko']

['lmqg/qag_koquad']

null

0

['questions and answers generation']

true

3,848

false

# Model Card of `lmqg/mt5-small-koquad-qag` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ko - **Training data:** [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ko", model="lmqg/mt5-small-koquad-qag") # model prediction question_answer_pairs = model.generate_qa("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qag") output = pipe("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_koquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 74.23 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | | QAAlignedF1Score (MoverScore) | 75.06 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | | QAAlignedPrecision (BERTScore) | 74.29 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | | QAAlignedPrecision (MoverScore) | 75.14 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | | QAAlignedRecall (BERTScore) | 74.2 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | | QAAlignedRecall (MoverScore) | 75.04 | default | [lmqg/qag_koquad](https://huggingface.co/datasets/lmqg/qag_koquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_koquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 256 - epoch: 13 - batch: 8 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-koquad-qag/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

aef1594b8d22239d9dcc9334295d5bd1

Akanksha27/distilbert-base-uncased-finetuned-cola

Akanksha27

distilbert

18

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,275

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4969 - Matthews Correlation: 0.4354 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5287 | 1.0 | 535 | 0.4969 | 0.4354 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ceca805ab9da6775e812ea83a342f1c3

KoichiYasuoka/roberta-classical-chinese-base-char

KoichiYasuoka

roberta

7

44

transformers

3

fill-mask

true

false

apache-2.0

['lzh']

null

0

['classical chinese', 'literary chinese', 'ancient chinese', 'masked-lm']

false

true

1,101

false

# roberta-classical-chinese-base-char ## Model Description This is a RoBERTa model pre-trained on Classical Chinese texts, derived from [GuwenBERT-base](https://huggingface.co/ethanyt/guwenbert-base). Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune `roberta-classical-chinese-base-char` for downstream tasks, such as [sentence-segmentation](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation), [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char") ``` ## See Also [SuPar-Kanbun](https://github.com/KoichiYasuoka/SuPar-Kanbun): Tokenizer POS-tagger and Dependency-parser for Classical Chinese

aaec20edf80480a0d79681f3ee60b9b7