davanstrien/model_cards_with_readmes · Datasets at Hugging Face

jonatasgrosman/exp_w2v2t_id_vp-100k_s842

jonatasgrosman

wav2vec2

10

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['id']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

475

false

# exp_w2v2t_id_vp-100k_s842 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 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

a8c90ba73f52e898dd703a5480ecf56f

vuiseng9/roberta-l-squadv1.1

vuiseng9

roberta

15

13

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

2,067

false

# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1 # Train ```bash python run_qa.py \ --model_name_or_path roberta-large \ --dataset_name squad \ --do_eval \ --do_train \ --evaluation_strategy steps \ --eval_steps 500 \ --learning_rate 3e-5 \ --fp16 \ --num_train_epochs 2 \ --per_device_eval_batch_size 64 \ --per_device_train_batch_size 16 \ --max_seq_length 384 \ --doc_stride 128 \ --save_steps 1000 \ --logging_steps 1 \ --overwrite_output_dir \ --run_name $RUNID \ --output_dir $OUTDIR ``` # Eval ```bash export CUDA_VISIBLE_DEVICES=0 MODEL=vuiseng9/roberta-l-squadv1.1 OUTDIR=eval-$(basename $MODEL) WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path $MODEL \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 16 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log & ``` ```bash eval_exact_match = 88.4674 eval_f1 = 94.3001 eval_samples = 10790 ```

737aa01dfd4a8c4993c2bf21a01a682f

jonatasgrosman/exp_w2v2t_ru_xlsr-53_s303

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ru']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

461

false

# exp_w2v2t_ru_xlsr-53_s303 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ru)](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.

c131f3deda8ec6583b70c268e1d765c8

Helsinki-NLP/opus-mt-es-ru

Helsinki-NLP

marian

10

1,062

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

850

false

### opus-mt-es-ru * source languages: es * target languages: ru * OPUS readme: [es-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ru/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.es.ru | 20.9 | 0.489 | | newstest2013.es.ru | 23.4 | 0.504 | | Tatoeba.es.ru | 47.0 | 0.657 |

2634f7d46333dc4e9ce52e7d65db7265

sanjin7/distilbert-base-uncased_proba

sanjin7

distilbert

6

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

923

false

# distilbert-base-uncased_proba This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 2 ### Framework versions - Transformers 4.25.1 - Pytorch 1.14.0.dev20221202 - Datasets 2.7.1 - Tokenizers 0.13.2

2b6b03a012aef3c4da75943a107c2289

symons/finetuning-sentiment-model-3000-samples

symons

distilbert

16

1

transformers

0

text-classification

true

false

apache-2.0

null

['rotten_tomatoes_movie_review']

null

0

['generated_from_trainer']

true

1,079

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 rotten_tomatoes_movie_review dataset. It achieves the following results on the evaluation set: - Loss: 0.8692 - Accuracy: 0.8433 - F1: 0.8407 ## 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: 5 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

ca04d26adbc4b9717dcfe8ab7e69ed88

TahaRazzaq/wav2vec2-base-urdu-demo-colab

TahaRazzaq

wav2vec2

22

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,032

false

# wav2vec2-base-urdu-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

def33ebf38b994b9291b04e34309666a

Siyong/MC

Siyong

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,799

false

# wav2vec-base-All 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: 3.0545 - Wer: 0.8861 - Cer: 0.5014 ## 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: 120 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | No log | 3.33 | 500 | 4.0654 | 1.0 | 0.9823 | | No log | 6.67 | 1000 | 3.4532 | 1.0 | 0.9823 | | No log | 10.0 | 1500 | 3.0707 | 0.9992 | 0.9781 | | No log | 13.33 | 2000 | 2.7335 | 1.0017 | 0.9027 | | No log | 16.67 | 2500 | 2.5896 | 1.0690 | 0.7302 | | No log | 20.0 | 3000 | 2.3315 | 1.0690 | 0.6677 | | No log | 23.33 | 3500 | 2.2217 | 1.0150 | 0.5966 | | No log | 26.67 | 4000 | 2.3802 | 1.0549 | 0.5948 | | No log | 30.0 | 4500 | 2.2208 | 0.9975 | 0.5681 | | 2.4224 | 33.33 | 5000 | 2.2687 | 0.9800 | 0.5537 | | 2.4224 | 36.67 | 5500 | 2.3169 | 0.9476 | 0.5493 | | 2.4224 | 40.0 | 6000 | 2.5196 | 0.9900 | 0.5509 | | 2.4224 | 43.33 | 6500 | 2.4816 | 0.9501 | 0.5272 | | 2.4224 | 46.67 | 7000 | 2.4894 | 0.9485 | 0.5276 | | 2.4224 | 50.0 | 7500 | 2.4555 | 0.9418 | 0.5305 | | 2.4224 | 53.33 | 8000 | 2.7326 | 0.9559 | 0.5255 | | 2.4224 | 56.67 | 8500 | 2.5514 | 0.9227 | 0.5209 | | 2.4224 | 60.0 | 9000 | 2.9135 | 0.9717 | 0.5455 | | 2.4224 | 63.33 | 9500 | 3.0465 | 0.8346 | 0.5002 | | 0.8569 | 66.67 | 10000 | 2.8177 | 0.9302 | 0.5216 | | 0.8569 | 70.0 | 10500 | 2.9908 | 0.9310 | 0.5128 | | 0.8569 | 73.33 | 11000 | 3.1752 | 0.9235 | 0.5284 | | 0.8569 | 76.67 | 11500 | 2.7412 | 0.8886 | 0.5 | | 0.8569 | 80.0 | 12000 | 2.7362 | 0.9127 | 0.5040 | | 0.8569 | 83.33 | 12500 | 2.9636 | 0.9152 | 0.5093 | | 0.8569 | 86.67 | 13000 | 3.0139 | 0.9011 | 0.5097 | | 0.8569 | 90.0 | 13500 | 2.8325 | 0.8853 | 0.5032 | | 0.8569 | 93.33 | 14000 | 3.0383 | 0.8845 | 0.5056 | | 0.8569 | 96.67 | 14500 | 2.7931 | 0.8795 | 0.4965 | | 0.3881 | 100.0 | 15000 | 2.8972 | 0.8928 | 0.5012 | | 0.3881 | 103.33 | 15500 | 2.7780 | 0.8736 | 0.4947 | | 0.3881 | 106.67 | 16000 | 3.1081 | 0.9036 | 0.5109 | | 0.3881 | 110.0 | 16500 | 3.0078 | 0.8928 | 0.5032 | | 0.3881 | 113.33 | 17000 | 3.0245 | 0.8886 | 0.5009 | | 0.3881 | 116.67 | 17500 | 3.0739 | 0.8928 | 0.5065 | | 0.3881 | 120.0 | 18000 | 3.0545 | 0.8861 | 0.5014 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

f36693a2957bdf3c18be84742446fc3f

peterhsu/marian-finetuned-kde4-en-to-zh_TW-accelerate

peterhsu

marian

9

5

transformers

0

translation

true

false

apache-2.0

null

['kde4']

null

0

['translation']

true

930

false

# marian-finetuned-kde4-en-to-zh_TW-accelerate ## Model description This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset. It achieves the following results on the evaluation set: - Bleu: 40.70 More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

98702876c67ba8fc1bb5e5501f6f7678

HanSSH/mt5-small-finetuned-amazon-en-es

HanSSH

mt5

17

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,484

false

# HanSSH/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2684 - Validation Loss: 3.2288 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.00056, 'decay_steps': 4836, '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, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.8144 | 3.5283 | 0 | | 3.8758 | 3.2971 | 1 | | 3.4741 | 3.2452 | 2 | | 3.2684 | 3.2288 | 3 | ### Framework versions - Transformers 4.21.3 - TensorFlow 2.10.0 - Datasets 2.4.0 - Tokenizers 0.12.1

1dec1da82f003552ea7f9d2264a9e6a4

Fulccrum/distilbert-base-uncased-finetuned-sst2

Fulccrum

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

1

0

1

0

['generated_from_trainer']

true

1,482

false

# distilbert-base-uncased-finetuned-sst2 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: 0.3739 - Accuracy: 0.9128 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1885 | 1.0 | 4210 | 0.3092 | 0.9083 | | 0.1311 | 2.0 | 8420 | 0.3809 | 0.9071 | | 0.1036 | 3.0 | 12630 | 0.3739 | 0.9128 | | 0.0629 | 4.0 | 16840 | 0.4623 | 0.9083 | | 0.036 | 5.0 | 21050 | 0.5198 | 0.9048 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

a519a67868991c9874db057fb9c9abaa

TransQuest/siamesetransquest-da-ro_en-wiki

TransQuest

xlm-roberta

12

transformers

0

feature-extraction

true

false

apache-2.0

['ro-en']

null

1

0

['Quality Estimation', 'siamesetransquest', 'da']

false

true

5,243

false

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

f4d0d525b09f559bd0c46c7e5fb941c7

pritamdeka/PubMedBert-abstract-cord19-v2

pritamdeka

bert

13

7

transformers

0

fill-mask

true

false

mit

null

['pritamdeka/cord-19-abstract']

null

0

['generated_from_trainer']

true

1,842

false

# PubMedBert-abstract-cord19-v2 This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [pritamdeka/cord-19-abstract](https://huggingface.co/datasets/pritamdeka/cord-19-abstract) dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Accuracy: 0.7247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 4.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.27 | 0.53 | 5000 | 1.2425 | 0.7236 | | 1.2634 | 1.06 | 10000 | 1.3123 | 0.7141 | | 1.3041 | 1.59 | 15000 | 1.3583 | 0.7072 | | 1.3829 | 2.12 | 20000 | 1.3590 | 0.7121 | | 1.3069 | 2.65 | 25000 | 1.3506 | 0.7154 | | 1.2921 | 3.18 | 30000 | 1.3448 | 0.7160 | | 1.2731 | 3.7 | 35000 | 1.3375 | 0.7178 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

972bdde3c53ff80f8a096f1ce9919934

HPL/roberta-large-unlabeled-gab-reddit-semeval2023-task10-57000sample

HPL

roberta

11

1

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,340

false

# roberta-large-unlabeled-gab-reddit-semeval2023-task10-57000sample This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8874 ## 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 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1999 | 1.0 | 3563 | 2.0576 | | 2.0587 | 2.0 | 7126 | 1.9371 | | 1.9591 | 3.0 | 10689 | 1.8823 | | 1.8652 | 4.0 | 14252 | 1.8874 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.10.3

80c3147fec31aa87ca98fde1fdb610ec

google/tapas-large-finetuned-wtq

google

tapas

8

2,913

transformers

18

table-question-answering

true

false

apache-2.0

['en']

['wikitablequestions']

null

0

['tapas', 'table-question-answering']

false

true

7,108

false

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

a8355557fc6795e0b5c11791007438ff

gokuls/mobilebert_add_GLUE_Experiment_mnli

gokuls

mobilebert

17

4

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,840

false

# mobilebert_add_GLUE_Experiment_mnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0985 - Accuracy: 0.3522 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0988 | 1.0 | 3068 | 1.0988 | 0.3182 | | 1.0987 | 2.0 | 6136 | 1.0986 | 0.3184 | | 1.0987 | 3.0 | 9204 | 1.0989 | 0.3274 | | 1.0987 | 4.0 | 12272 | 1.0987 | 0.3182 | | 1.0987 | 5.0 | 15340 | 1.0984 | 0.3545 | | 1.0986 | 6.0 | 18408 | 1.0987 | 0.3274 | | 1.0986 | 7.0 | 21476 | 1.0993 | 0.3274 | | 1.0986 | 8.0 | 24544 | 1.0985 | 0.3545 | | 1.0986 | 9.0 | 27612 | 1.0985 | 0.3545 | | 1.0986 | 10.0 | 30680 | 1.0987 | 0.3182 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

bfe46f2adc252e888eb687c973b04f39

stanfordnlp/corenlp-english-extra

stanfordnlp

null

3

0

null

0

null

false

gpl-2.0

['en']

null

0

['corenlp']

false

true

666

false

# Core NLP model for english-extra CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP). This card and repo were automatically prepared with `hugging_corenlp.py` in the `stanfordnlp/huggingface-models` repo Last updated 2023-01-21 01:36:25.611

40b71a23e28aa21b0dfafab4afd2fd6c

spooncats/lacroix-can-plus-van-gogh

spooncats

null

19

8

diffusers

0

text-to-image

false

creativeml-openrail-m

null

2

0

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

false

true

740

false

### lacroix_can_plus_van_gogh Dreambooth model trained by spooncats with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/spooncats/lacroix-can-plus-van-gogh/resolve/main/sample_images/grid-0020.png)

3536b16cf5332ec18e7a8918522a616a

lmqg/mt5-base-jaquad-ae

lmqg

mt5

13

72

transformers

0

text2text-generation

true

false

cc-by-4.0

['ja']

['lmqg/qg_jaquad']

null

0

['answer extraction']

true

4,385

false

# Model Card of `lmqg/mt5-base-jaquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** ja - **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="lmqg/mt5-base-jaquad-ae") # model prediction answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-jaquad-ae") output = pipe("『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。") ``` ## Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 28.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | AnswerF1Score | 28.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | BERTScore | 77.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_1 | 33.75 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_2 | 30.74 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_3 | 28.29 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_4 | 26.48 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | METEOR | 25.61 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | MoverScore | 64.96 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | ROUGE_L | 35.58 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_jaquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 9 - batch: 8 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-jaquad-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

0e6b148a4952ed50068c4302f1978f37

daekeun-ml/koelectra-small-v3-nsmc

daekeun-ml

electra

9

13

transformers

1

text-classification

true

false

mit

['ko']

['nsmc']

null

0

['classification']

false

true

4,575

false

# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset) ## Usage (Amazon SageMaker inference applicable) It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint. ### inference_nsmc.py ```python import json import sys import logging import torch from torch import nn from transformers import ElectraConfig from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification logging.basicConfig( level=logging.INFO, format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s', handlers=[ logging.FileHandler(filename='tmp.log'), logging.StreamHandler(sys.stdout) ] ) logger = logging.getLogger(__name__) max_seq_length = 128 classes = ['Neg', 'Pos'] tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def model_fn(model_path=None): #### # If you have your own trained model # Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator' #### #config = ElectraConfig.from_json_file(f'{model_path}/config.json') #model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config) # Download model from the Huggingface hub model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc') model.to(device) return model def input_fn(input_data, content_type="application/jsonlines"): data_str = input_data.decode("utf-8") jsonlines = data_str.split("\n") transformed_inputs = [] for jsonline in jsonlines: text = json.loads(jsonline)["text"][0] logger.info("input text: {}".format(text)) encode_plus_token = tokenizer.encode_plus( text, max_length=max_seq_length, add_special_tokens=True, return_token_type_ids=False, padding="max_length", return_attention_mask=True, return_tensors="pt", truncation=True, ) transformed_inputs.append(encode_plus_token) return transformed_inputs def predict_fn(transformed_inputs, model): predicted_classes = [] for data in transformed_inputs: data = data.to(device) output = model(**data) softmax_fn = nn.Softmax(dim=1) softmax_output = softmax_fn(output[0]) _, prediction = torch.max(softmax_output, dim=1) predicted_class_idx = prediction.item() predicted_class = classes[predicted_class_idx] score = softmax_output[0][predicted_class_idx] logger.info("predicted_class: {}".format(predicted_class)) prediction_dict = {} prediction_dict["predicted_label"] = predicted_class prediction_dict['score'] = score.cpu().detach().numpy().tolist() jsonline = json.dumps(prediction_dict) logger.info("jsonline: {}".format(jsonline)) predicted_classes.append(jsonline) predicted_classes_jsonlines = "\n".join(predicted_classes) return predicted_classes_jsonlines def output_fn(outputs, accept="application/jsonlines"): return outputs, accept ``` ### test.py ```python >>> from inference_nsmc import model_fn, input_fn, predict_fn, output_fn >>> with open('samples/nsmc.txt', mode='rb') as file: >>> model_input_data = file.read() >>> model = model_fn() >>> transformed_inputs = input_fn(model_input_data) >>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model) >>> model_outputs = output_fn(predicted_classes_jsonlines) >>> print(model_outputs[0]) [{inference_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다 [{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다 [{inference_nsmc.py:77} INFO - predicted_class: Pos [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613} [{inference_nsmc.py:77} INFO - predicted_class: Neg [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967} {"predicted_label": "Pos", "score": 0.9619030952453613} {"predicted_label": "Neg", "score": 0.9994170665740967} ``` ### Sample data (samples/nsmc.txt) ``` {"text": ["이 영화는 최고의 영화입니다"]} {"text": ["최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다"]} ``` ## References - KoELECTRA: https://github.com/monologg/KoELECTRA - Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc

a3ae86628b7c67f47fa1eaac4f8ba1c1

facebook/regnet-y-320-seer-in1k

facebook

regnet

6

9

transformers

0

image-classification

true

false

apache-2.0

null

['imagenet-1k']

null

1

0

1

0

['vision', 'image-classification']

false

true

1,911

false

# RegNet RegNet model trained on imagenet-1k. It was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on a billion uncurated Instagram images. This model is later fine-tuned on ImageNet. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-320-seer-in1k") >>> model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-320-seer-in1k") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) 'tabby, tabby cat' ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).

7677d25c81a13f82a1316bc9715cc037

tsmatz/roberta_qa_japanese

tsmatz

roberta

10

296

transformers

1

question-answering

true

false

mit

['ja']

null

0

['question-answering', 'generated_from_trainer', 'bert', 'jaquad']

true

4,054

false

# roberta_qa_japanese (Japanese caption : 日本語の (抽出型) 質問応答のモデル) This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) (pre-trained RoBERTa model provided by rinna Co., Ltd.) trained for extractive question answering. The model is fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD) dataset provided by Skelter Labs, in which data is collected from Japanese Wikipedia articles and annotated by a human. ## Intended uses When running with a dedicated pipeline : ```python from transformers import pipeline model_name = "tsmatz/roberta_qa_japanese" qa_pipeline = pipeline( "question-answering", model=model_name, tokenizer=model_name) result = qa_pipeline( question = "決勝トーナメントで日本に勝ったのはどこでしたか。", context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。", align_to_words = False, ) print(result) ``` When manually running through forward pass : ```python import torch import numpy as np from transformers import AutoModelForQuestionAnswering, AutoTokenizer model_name = "tsmatz/roberta_qa_japanese" model = (AutoModelForQuestionAnswering .from_pretrained(model_name)) tokenizer = AutoTokenizer.from_pretrained(model_name) def inference_answer(question, context): question = question context = context test_feature = tokenizer( question, context, max_length=318, ) with torch.no_grad(): outputs = model(torch.tensor([test_feature["input_ids"]])) start_logits = outputs.start_logits.cpu().numpy() end_logits = outputs.end_logits.cpu().numpy() answer_ids = test_feature["input_ids"][np.argmax(start_logits):np.argmax(end_logits)+1] return "".join(tokenizer.batch_decode(answer_ids)) question = "決勝トーナメントで日本に勝ったのはどこでしたか。" context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。" answer_pred = inference_answer(question, context) print(answer_pred) ``` ## Training procedure You can download the source code for fine-tuning from [here](https://github.com/tsmatz/huggingface-finetune-japanese/blob/master/03-question-answering.ipynb). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1293 | 0.13 | 150 | 1.0311 | | 1.1965 | 0.26 | 300 | 0.6723 | | 1.022 | 0.39 | 450 | 0.4838 | | 0.9594 | 0.53 | 600 | 0.5174 | | 0.9187 | 0.66 | 750 | 0.4671 | | 0.8229 | 0.79 | 900 | 0.4650 | | 0.71 | 0.92 | 1050 | 0.2648 | | 0.5436 | 1.05 | 1200 | 0.2665 | | 0.5045 | 1.19 | 1350 | 0.2686 | | 0.5025 | 1.32 | 1500 | 0.2082 | | 0.5213 | 1.45 | 1650 | 0.1715 | | 0.4648 | 1.58 | 1800 | 0.1563 | | 0.4698 | 1.71 | 1950 | 0.1488 | | 0.4823 | 1.84 | 2100 | 0.1050 | | 0.4482 | 1.97 | 2250 | 0.0821 | | 0.2755 | 2.11 | 2400 | 0.0898 | | 0.2834 | 2.24 | 2550 | 0.0964 | | 0.2525 | 2.37 | 2700 | 0.0533 | | 0.2606 | 2.5 | 2850 | 0.0561 | | 0.2467 | 2.63 | 3000 | 0.0601 | | 0.2799 | 2.77 | 3150 | 0.0562 | | 0.2497 | 2.9 | 3300 | 0.0516 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

c733f7d509525c45fcbd1a152dc68e6f

krishnayogik/distilbert-base-uncased-finetuned-emotion

krishnayogik

distilbert

12

6

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

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: 0.2258 - Accuracy: 0.9245 - F1: 0.9248 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3316 | 0.901 | 0.8967 | | 0.2584 | 2.0 | 500 | 0.2258 | 0.9245 | 0.9248 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

e4db0a508c5f7ee6c7b3c0f6a561a095

furyhawk/t5-base-finetuned-bbc

furyhawk

t5

20

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,203

false

# t5-base-finetuned-bbc This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 334 | 0.1500 | 24.5024 | 21.4979 | 24.0227 | 24.0303 | 19.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3

d8ec256b0ccb9a199dd6e2fa87c8367f

thu-coai/CDial-GPT2_LCCC-base

thu-coai

null

5

82

transformers

1

conversational

true

false

mit

null

0

['conversational']

false

true

1,082

false

## Chinese pre-trained dialogue model (CDial-GPT) This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc). We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset. Similar to [TransferTransfo](https://arxiv.org/abs/1901.08149), we concatenate all dialogue histories into one context sentence, and use this sentence to predict the response. The input of our model consists of word embedding, speaker embedding, and positional embedding of each word. Paper: [A Large-Scale Chinese Short-Text Conversation Dataset](https://arxiv.org/pdf/2008.03946.pdf) ### How to use ```python from transformers import OpenAIGPTLMHeadModel, GPT2LMHeadModel, BertTokenizer import torch tokenizer = BertTokenizer.from_pretrained("thu-coai/CDial-GPT2_LCCC-base") model = GPT2LMHeadModel.from_pretrained("thu-coai/CDial-GPT2_LCCC-base") ``` For more details, please refer to our [repo.](https://github.com/thu-coai/CDial-GPT) on github.

497d441bff43f6601f10c667f2d93073

PriaPillai/distilbert-base-uncased-finetuned-query

PriaPillai

distilbert

30

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,410

false

# distilbert-base-uncased-finetuned-query This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3668 - Accuracy: 0.8936 - F1: 0.8924 ## 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: 5 - eval_batch_size: 5 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6511 | 1.0 | 30 | 0.5878 | 0.7234 | 0.6985 | | 0.499 | 2.0 | 60 | 0.4520 | 0.8723 | 0.8683 | | 0.3169 | 3.0 | 90 | 0.3668 | 0.8936 | 0.8924 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

e68d338fb7c05ec4de38e213094749df

HYM/test_ner-finetuned-ner

HYM

distilbert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,540

false

# test_ner-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Precision: 0.9242 - Recall: 0.9349 - F1: 0.9295 - Accuracy: 0.9834 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2385 | 1.0 | 878 | 0.0708 | 0.9140 | 0.9216 | 0.9178 | 0.9808 | | 0.055 | 2.0 | 1756 | 0.0626 | 0.9209 | 0.9340 | 0.9274 | 0.9828 | | 0.0309 | 3.0 | 2634 | 0.0623 | 0.9242 | 0.9349 | 0.9295 | 0.9834 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

bbf8bd081460175bb5d21c52c15b6253

stanfordnlp/stanza-hyw

stanfordnlp

null

9

1

stanza

0

token-classification

false

apache-2.0

['hyw']

null

0

['stanza', 'token-classification']

false

true

590

false

# Stanza model for Western_Armenian (hyw) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:32:11.573

2d8d946d462cd9a9f1443ff3cb6880e5

pyf98/tedlium2_transducer_e_branchformer

pyf98

null

21

0

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['tedlium2']

null

0

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

false

true

11,168

false

## ESPnet2 ASR model ### `pyf98/tedlium2_transducer_e_branchformer` This model was trained by Yifan Peng using tedlium2 recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html) ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 478ba004e114e7862b05fb01112de7f7e1da3996 pip install -e . cd egs2/tedlium2/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/tedlium2_transducer_e_branchformer ```  # RESULTS ## Environments - date: `Thu Feb 9 01:29:33 CST 2023` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.13.1` - Git hash: `478ba004e114e7862b05fb01112de7f7e1da3996` - Commit date: `Tue Feb 7 00:50:49 2023 +0000` ## asr_train_asr_transducer_e_branchformer_e12_raw_en_bpe500_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|14671|93.4|4.3|2.3|1.0|7.6|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|27500|93.6|4.0|2.4|1.0|7.4|63.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|78259|97.1|0.9|2.0|0.9|3.8|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|145066|97.1|0.9|2.1|0.9|3.9|63.5| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|28296|94.7|3.1|2.3|0.8|6.2|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|52113|95.1|2.6|2.2|0.9|5.8|63.5| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transducer_e_branchformer_e12.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transducer_e_branchformer_e12_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 6 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45753 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 5 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 10000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - s - ▁the - t - ▁a - ▁and - ▁to - d - e - ▁of - '''' - n - ing - ▁in - ▁i - ▁that - i - a - l - p - m - y - o - ▁it - ▁we - c - u - ▁you - ed - ▁ - r - ▁is - re - ▁this - ar - g - ▁so - al - b - ▁s - or - ▁f - ▁c - in - k - f - ▁for - ic - er - le - ▁be - ▁do - ▁re - ve - ▁e - ▁w - ▁was - es - ▁they - ly - h - ▁on - v - ▁are - ri - ▁have - an - ▁what - ▁with - ▁t - w - ur - it - ent - ▁can - ▁he - ▁but - ra - ce - ▁me - ▁b - ▁ma - ▁p - ll - ▁st - ▁one - 'on' - ▁about - th - ▁de - en - ▁all - ▁not - il - ▁g - ch - at - ▁there - ▁mo - ter - ation - tion - ▁at - ▁my - ro - ▁as - te - ▁le - ▁con - ▁like - ▁people - ▁or - ▁an - el - ▁if - ▁from - ver - ▁su - ▁co - ate - ▁these - ol - ci - ▁now - ▁see - ▁out - ▁our - ion - ▁know - ect - ▁just - as - ▁ex - ▁ch - ▁d - ▁when - ▁very - ▁think - ▁who - ▁because - ▁go - ▁up - ▁us - ▁pa - ▁no - ies - ▁di - ▁ho - om - ive - ▁get - id - ▁o - ▁hi - un - ▁how - ▁by - ir - et - ck - ity - ▁po - ul - ▁which - ▁mi - ▁some - z - ▁sp - ▁un - ▁going - ▁pro - ist - ▁se - ▁look - ▁time - ment - de - ▁more - ▁had - ng - ▁would - ge - la - ▁here - ▁really - x - ▁your - ▁them - us - me - ▁en - ▁two - ▁k - ▁li - ▁world - ne - ow - ▁way - ▁want - ▁work - ▁don - ▁lo - ▁fa - ▁were - ▁their - age - vi - ▁ha - ac - der - est - ▁bo - am - ▁other - able - ▁actually - ▁sh - ▁make - ▁ba - ▁la - ine - ▁into - ▁where - ▁could - ▁comp - ting - ▁has - ▁will - ▁ne - j - ical - ally - ▁vi - ▁things - ▁te - igh - ▁say - ▁years - ers - ▁ra - ther - ▁than - ru - ▁ro - op - ▁did - ▁any - ▁new - ound - ig - ▁well - mo - ▁she - ▁na - ▁been - he - ▁thousand - ▁car - ▁take - ▁right - ▁then - ▁need - ▁start - ▁hundred - ▁something - ▁over - ▁com - ia - ▁kind - um - if - ▁those - ▁first - ▁pre - ta - ▁said - ize - end - ▁even - ▁thing - one - ▁back - ite - ▁every - ▁little - ry - ▁life - ▁much - ke - ▁also - ▁most - ant - per - ▁three - ▁come - ▁lot - ance - ▁got - ▁talk - ▁per - ▁inter - ▁sa - ▁use - ▁mu - ▁part - ish - ence - ▁happen - ▁bi - ▁mean - ough - ▁qu - ▁bu - ▁day - ▁ga - ▁only - ▁many - ▁different - ▁dr - ▁th - ▁show - ful - ▁down - ated - ▁good - ▁tra - ▁around - ▁idea - ▁human - ous - ▁put - ▁through - ▁five - ▁why - ▁change - ▁real - ff - ible - ▁fact - ▁same - ▁jo - ▁live - ▁year - ▁problem - ▁ph - ▁four - ▁give - ▁big - ▁tell - ▁great - ▁try - ▁va - ▁ru - ▁system - ▁six - ▁plan - ▁place - ▁build - ▁called - ▁again - ▁point - ▁twenty - ▁percent - ▁nine - ▁find - ▁app - ▁after - ▁long - ▁eight - ▁imp - ▁gene - ▁design - ▁today - ▁should - ▁made - ious - ▁came - ▁learn - ▁last - ▁own - way - ▁turn - ▁seven - ▁high - ▁question - ▁person - ▁brain - ▁important - ▁another - ▁thought - ▁trans - ▁create - ness - ▁hu - ▁power - ▁act - land - ▁play - ▁sort - ▁old - ▁before - ▁course - ▁understand - ▁feel - ▁might - ▁each - ▁million - ▁better - ▁together - ▁ago - ▁example - ▁help - ▁story - ▁next - ▁hand - ▁school - ▁water - ▁develop - ▁technology - que - ▁second - ▁grow - ▁still - ▁cell - ▁believe - ▁number - ▁small - ▁between - qui - ▁data - ▁become - ▁america - ▁maybe - ▁space - ▁project - ▁organ - ▁vo - ▁children - ▁book - graph - ▁open - ▁fifty - ▁picture - ▁health - ▁thirty - ▁africa - ▁reason - ▁large - ▁hard - ▁computer - ▁always - ▁sense - ▁money - ▁women - ▁everything - ▁information - ▁country - ▁teach - ▁energy - ▁experience - ▁food - ▁process - qua - ▁interesting - ▁future - ▁science - q - '0' - '5' - '6' - '9' - '3' - '8' - '4' - N - A - '7' - S - G - F - R - L - U - E - T - H - _ - B - D - J - M - ă - ō - ť - '2' - '-' - '1' - C - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: joint_space_size: 320 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 report_cer: false report_wer: false preencoder: null preencoder_conf: {} encoder: e_branchformer encoder_conf: output_size: 256 attention_heads: 4 attention_layer_type: rel_selfattn pos_enc_layer_type: rel_pos rel_pos_type: latest cgmlp_linear_units: 1024 cgmlp_conv_kernel: 31 use_linear_after_conv: false gate_activation: identity num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d layer_drop_rate: 0.0 linear_units: 1024 positionwise_layer_type: linear use_ffn: true macaron_ffn: true merge_conv_kernel: 31 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 256 dropout: 0.1 dropout_embed: 0.2 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202301' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

a55efdd3e53469de7ae0654fdea3978c

sayakpaul/glpn-nyu-finetuned-diode-230119-100058

sayakpaul

glpn

7

0

transformers

0

depth-estimation

true

false

apache-2.0

null

0

['vision', 'depth-estimation', 'generated_from_trainer']

true

11,010

false

# glpn-nyu-finetuned-diode-230119-100058 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4305 - Mae: 0.4203 - Rmse: 0.6123 - Abs Rel: 0.4280 - Log Mae: 0.1694 - Log Rmse: 0.2214 - Delta1: 0.3813 - Delta2: 0.6446 - Delta3: 0.8152 ## 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: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 75 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.2807 | 1.0 | 72 | 0.9866 | 0.8312 | 1.0131 | 0.7179 | 0.5655 | 0.5924 | 0.0087 | 0.0200 | 0.0552 | | 0.7396 | 2.0 | 144 | 0.4976 | 0.4741 | 0.6670 | 0.5279 | 0.1989 | 0.2567 | 0.3070 | 0.5470 | 0.7943 | | 0.5018 | 3.0 | 216 | 0.4811 | 0.4630 | 0.6367 | 0.5198 | 0.1929 | 0.2446 | 0.3211 | 0.5440 | 0.7506 | | 0.482 | 4.0 | 288 | 0.4726 | 0.4556 | 0.6337 | 0.4951 | 0.1893 | 0.2410 | 0.3306 | 0.5636 | 0.7663 | | 0.4874 | 5.0 | 360 | 0.4813 | 0.4662 | 0.6355 | 0.5265 | 0.1941 | 0.2446 | 0.3179 | 0.5385 | 0.7278 | | 0.4648 | 6.0 | 432 | 0.4681 | 0.4512 | 0.6309 | 0.4783 | 0.1869 | 0.2383 | 0.3430 | 0.5757 | 0.7527 | | 0.4346 | 7.0 | 504 | 0.4637 | 0.4499 | 0.6292 | 0.4710 | 0.1859 | 0.2357 | 0.3453 | 0.5671 | 0.7644 | | 0.4018 | 8.0 | 576 | 0.4790 | 0.4638 | 0.6349 | 0.5161 | 0.1928 | 0.2436 | 0.3255 | 0.5408 | 0.7338 | | 0.4092 | 9.0 | 648 | 0.4559 | 0.4449 | 0.6267 | 0.4540 | 0.1827 | 0.2319 | 0.3541 | 0.5814 | 0.7692 | | 0.3891 | 10.0 | 720 | 0.4619 | 0.4433 | 0.6259 | 0.4748 | 0.1823 | 0.2351 | 0.3579 | 0.5870 | 0.7742 | | 0.3707 | 11.0 | 792 | 0.4624 | 0.4500 | 0.6269 | 0.4828 | 0.1851 | 0.2350 | 0.3421 | 0.5672 | 0.7638 | | 0.4129 | 12.0 | 864 | 0.4648 | 0.4468 | 0.6265 | 0.4836 | 0.1836 | 0.2358 | 0.3533 | 0.5786 | 0.7625 | | 0.4108 | 13.0 | 936 | 0.4474 | 0.4312 | 0.6187 | 0.4501 | 0.1752 | 0.2280 | 0.3801 | 0.6088 | 0.7887 | | 0.3948 | 14.0 | 1008 | 0.4619 | 0.4498 | 0.6263 | 0.4853 | 0.1844 | 0.2344 | 0.3401 | 0.5721 | 0.7645 | | 0.4009 | 15.0 | 1080 | 0.4619 | 0.4440 | 0.6244 | 0.4889 | 0.1820 | 0.2351 | 0.3563 | 0.5841 | 0.7751 | | 0.3657 | 16.0 | 1152 | 0.4636 | 0.4491 | 0.6260 | 0.4936 | 0.1846 | 0.2360 | 0.3422 | 0.5734 | 0.7644 | | 0.3605 | 17.0 | 1224 | 0.4353 | 0.4255 | 0.6153 | 0.4248 | 0.1715 | 0.2218 | 0.3844 | 0.6207 | 0.8008 | | 0.3937 | 18.0 | 1296 | 0.4756 | 0.4609 | 0.6310 | 0.5281 | 0.1909 | 0.2423 | 0.3220 | 0.5461 | 0.7538 | | 0.3453 | 19.0 | 1368 | 0.4698 | 0.4517 | 0.6270 | 0.5145 | 0.1863 | 0.2392 | 0.3360 | 0.5702 | 0.7689 | | 0.3883 | 20.0 | 1440 | 0.4349 | 0.4240 | 0.6145 | 0.4311 | 0.1712 | 0.2230 | 0.3841 | 0.6321 | 0.8030 | | 0.3482 | 21.0 | 1512 | 0.4339 | 0.4209 | 0.6146 | 0.4223 | 0.1694 | 0.2223 | 0.3967 | 0.6337 | 0.8036 | | 0.3374 | 22.0 | 1584 | 0.4400 | 0.4289 | 0.6167 | 0.4431 | 0.1737 | 0.2254 | 0.3743 | 0.6191 | 0.7971 | | 0.3516 | 23.0 | 1656 | 0.4395 | 0.4280 | 0.6171 | 0.4426 | 0.1737 | 0.2259 | 0.3710 | 0.6241 | 0.7998 | | 0.3901 | 24.0 | 1728 | 0.4444 | 0.4324 | 0.6184 | 0.4562 | 0.1758 | 0.2280 | 0.3665 | 0.6118 | 0.7991 | | 0.3587 | 25.0 | 1800 | 0.4326 | 0.4200 | 0.6129 | 0.4281 | 0.1690 | 0.2222 | 0.3920 | 0.6403 | 0.8073 | | 0.3425 | 26.0 | 1872 | 0.4371 | 0.4231 | 0.6152 | 0.4341 | 0.1709 | 0.2242 | 0.3852 | 0.6372 | 0.7974 | | 0.3252 | 27.0 | 1944 | 0.4381 | 0.4225 | 0.6140 | 0.4399 | 0.1705 | 0.2245 | 0.3851 | 0.6396 | 0.8065 | | 0.3586 | 28.0 | 2016 | 0.4441 | 0.4304 | 0.6162 | 0.4488 | 0.1746 | 0.2258 | 0.3674 | 0.6179 | 0.7929 | | 0.3389 | 29.0 | 2088 | 0.4240 | 0.4112 | 0.6100 | 0.4017 | 0.1640 | 0.2173 | 0.4152 | 0.6599 | 0.8128 | | 0.3418 | 30.0 | 2160 | 0.4312 | 0.4195 | 0.6126 | 0.4211 | 0.1687 | 0.2206 | 0.3899 | 0.6435 | 0.8123 | | 0.3454 | 31.0 | 2232 | 0.4301 | 0.4176 | 0.6126 | 0.4167 | 0.1674 | 0.2203 | 0.3974 | 0.6479 | 0.8089 | | 0.3499 | 32.0 | 2304 | 0.4262 | 0.4154 | 0.6115 | 0.4081 | 0.1661 | 0.2184 | 0.3997 | 0.6578 | 0.8083 | | 0.3649 | 33.0 | 2376 | 0.4429 | 0.4313 | 0.6171 | 0.4507 | 0.1753 | 0.2263 | 0.3641 | 0.6134 | 0.7982 | | 0.3341 | 34.0 | 2448 | 0.4292 | 0.4207 | 0.6127 | 0.4161 | 0.1689 | 0.2192 | 0.3874 | 0.6415 | 0.8007 | | 0.3323 | 35.0 | 2520 | 0.4402 | 0.4266 | 0.6148 | 0.4434 | 0.1728 | 0.2247 | 0.3754 | 0.6254 | 0.7983 | | 0.3374 | 36.0 | 2592 | 0.4336 | 0.4233 | 0.6139 | 0.4277 | 0.1706 | 0.2219 | 0.3810 | 0.6362 | 0.8008 | | 0.334 | 37.0 | 2664 | 0.4310 | 0.4230 | 0.6138 | 0.4240 | 0.1703 | 0.2209 | 0.3826 | 0.6345 | 0.8034 | | 0.3471 | 38.0 | 2736 | 0.4372 | 0.4250 | 0.6144 | 0.4397 | 0.1720 | 0.2240 | 0.3780 | 0.6303 | 0.8046 | | 0.3283 | 39.0 | 2808 | 0.4421 | 0.4301 | 0.6168 | 0.4497 | 0.1743 | 0.2259 | 0.3654 | 0.6209 | 0.7993 | | 0.3418 | 40.0 | 2880 | 0.4340 | 0.4224 | 0.6137 | 0.4334 | 0.1703 | 0.2228 | 0.3857 | 0.6351 | 0.8054 | | 0.3455 | 41.0 | 2952 | 0.4294 | 0.4174 | 0.6118 | 0.4212 | 0.1675 | 0.2203 | 0.3959 | 0.6469 | 0.8109 | | 0.3229 | 42.0 | 3024 | 0.4291 | 0.4165 | 0.6121 | 0.4199 | 0.1671 | 0.2207 | 0.4035 | 0.6464 | 0.8103 | | 0.352 | 43.0 | 3096 | 0.4393 | 0.4266 | 0.6154 | 0.4462 | 0.1729 | 0.2253 | 0.3744 | 0.6287 | 0.8049 | | 0.3163 | 44.0 | 3168 | 0.4250 | 0.4113 | 0.6098 | 0.4112 | 0.1647 | 0.2187 | 0.4041 | 0.6620 | 0.8201 | | 0.3284 | 45.0 | 3240 | 0.4358 | 0.4245 | 0.6138 | 0.4379 | 0.1716 | 0.2233 | 0.3745 | 0.6306 | 0.8106 | | 0.3359 | 46.0 | 3312 | 0.4321 | 0.4217 | 0.6124 | 0.4283 | 0.1699 | 0.2210 | 0.3770 | 0.6412 | 0.8129 | | 0.3406 | 47.0 | 3384 | 0.4238 | 0.4127 | 0.6104 | 0.4084 | 0.1653 | 0.2183 | 0.3982 | 0.6617 | 0.8177 | | 0.3207 | 48.0 | 3456 | 0.4375 | 0.4275 | 0.6147 | 0.4435 | 0.1733 | 0.2243 | 0.3658 | 0.6262 | 0.8071 | | 0.3338 | 49.0 | 3528 | 0.4331 | 0.4223 | 0.6142 | 0.4310 | 0.1705 | 0.2228 | 0.3846 | 0.6374 | 0.8071 | | 0.3203 | 50.0 | 3600 | 0.4308 | 0.4212 | 0.6136 | 0.4253 | 0.1695 | 0.2213 | 0.3878 | 0.6407 | 0.8054 | | 0.3238 | 51.0 | 3672 | 0.4379 | 0.4267 | 0.6148 | 0.4416 | 0.1727 | 0.2241 | 0.3723 | 0.6244 | 0.8036 | | 0.3209 | 52.0 | 3744 | 0.4289 | 0.4187 | 0.6121 | 0.4178 | 0.1681 | 0.2198 | 0.3920 | 0.6461 | 0.8096 | | 0.3198 | 53.0 | 3816 | 0.4376 | 0.4264 | 0.6145 | 0.4402 | 0.1724 | 0.2237 | 0.3708 | 0.6279 | 0.8066 | | 0.3137 | 54.0 | 3888 | 0.4294 | 0.4180 | 0.6115 | 0.4242 | 0.1681 | 0.2208 | 0.3888 | 0.6494 | 0.8152 | | 0.3238 | 55.0 | 3960 | 0.4416 | 0.4294 | 0.6158 | 0.4521 | 0.1743 | 0.2261 | 0.3645 | 0.6205 | 0.8069 | | 0.3173 | 56.0 | 4032 | 0.4257 | 0.4142 | 0.6116 | 0.4145 | 0.1661 | 0.2198 | 0.4016 | 0.6586 | 0.8136 | | 0.3173 | 57.0 | 4104 | 0.4303 | 0.4193 | 0.6123 | 0.4246 | 0.1687 | 0.2210 | 0.3879 | 0.6451 | 0.8118 | | 0.3297 | 58.0 | 4176 | 0.4302 | 0.4219 | 0.6132 | 0.4259 | 0.1700 | 0.2211 | 0.3792 | 0.6394 | 0.8122 | | 0.3261 | 59.0 | 4248 | 0.4319 | 0.4220 | 0.6131 | 0.4312 | 0.1702 | 0.2221 | 0.3781 | 0.6407 | 0.8142 | | 0.3082 | 60.0 | 4320 | 0.4340 | 0.4234 | 0.6136 | 0.4346 | 0.1710 | 0.2228 | 0.3754 | 0.6373 | 0.8106 | | 0.31 | 61.0 | 4392 | 0.4225 | 0.4120 | 0.6104 | 0.4073 | 0.1646 | 0.2181 | 0.4054 | 0.6626 | 0.8168 | | 0.3065 | 62.0 | 4464 | 0.4313 | 0.4197 | 0.6125 | 0.4280 | 0.1690 | 0.2216 | 0.3854 | 0.6472 | 0.8127 | | 0.3046 | 63.0 | 4536 | 0.4316 | 0.4202 | 0.6127 | 0.4268 | 0.1691 | 0.2213 | 0.3849 | 0.6448 | 0.8131 | | 0.303 | 64.0 | 4608 | 0.4352 | 0.4241 | 0.6137 | 0.4373 | 0.1712 | 0.2231 | 0.3760 | 0.6364 | 0.8097 | | 0.3094 | 65.0 | 4680 | 0.4318 | 0.4205 | 0.6128 | 0.4304 | 0.1695 | 0.2220 | 0.3828 | 0.6438 | 0.8140 | | 0.3035 | 66.0 | 4752 | 0.4351 | 0.4233 | 0.6136 | 0.4386 | 0.1709 | 0.2235 | 0.3781 | 0.6388 | 0.8099 | | 0.327 | 67.0 | 4824 | 0.4307 | 0.4203 | 0.6131 | 0.4280 | 0.1693 | 0.2216 | 0.3828 | 0.6463 | 0.8143 | | 0.3175 | 68.0 | 4896 | 0.4325 | 0.4219 | 0.6137 | 0.4314 | 0.1701 | 0.2222 | 0.3809 | 0.6406 | 0.8135 | | 0.3188 | 69.0 | 4968 | 0.4299 | 0.4203 | 0.6126 | 0.4271 | 0.1694 | 0.2214 | 0.3827 | 0.6440 | 0.8141 | | 0.3158 | 70.0 | 5040 | 0.4304 | 0.4203 | 0.6126 | 0.4274 | 0.1694 | 0.2215 | 0.3832 | 0.6443 | 0.8133 | | 0.3298 | 71.0 | 5112 | 0.4315 | 0.4219 | 0.6135 | 0.4292 | 0.1700 | 0.2218 | 0.3792 | 0.6423 | 0.8136 | | 0.3246 | 72.0 | 5184 | 0.4323 | 0.4219 | 0.6129 | 0.4322 | 0.1703 | 0.2223 | 0.3769 | 0.6418 | 0.8133 | | 0.3116 | 73.0 | 5256 | 0.4301 | 0.4198 | 0.6124 | 0.4264 | 0.1691 | 0.2213 | 0.3833 | 0.6459 | 0.8141 | | 0.3192 | 74.0 | 5328 | 0.4301 | 0.4200 | 0.6125 | 0.4266 | 0.1691 | 0.2213 | 0.3819 | 0.6464 | 0.8156 | | 0.3172 | 75.0 | 5400 | 0.4305 | 0.4203 | 0.6123 | 0.4280 | 0.1694 | 0.2214 | 0.3813 | 0.6446 | 0.8152 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

9af60b4520910c053b8e850e9c8e2682

sd-concepts-library/dragonborn

sd-concepts-library

null

12

0

null

0

null

false

mit

null

0

[]

false

true

1,354

false

### Dragonborn on Stable Diffusion This is the `<dragonborn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<dragonborn> 0](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/5.jpeg) ![<dragonborn> 1](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/6.jpeg) ![<dragonborn> 2](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/3.jpeg) ![<dragonborn> 3](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/0.jpeg) ![<dragonborn> 4](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/2.jpeg) ![<dragonborn> 5](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/1.jpeg) ![<dragonborn> 6](https://huggingface.co/sd-concepts-library/dragonborn/resolve/main/concept_images/4.jpeg)

9891734d10d8866b4b7d10f1e302ff4b

sriAryan18/tf_bert_uncased_emotion_detection

sriAryan18

bert

4

5

transformers

1

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

2,202

false

# tf_bert_uncased_emotion_detection This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0659 - Train Accuracy: 0.9661 - Validation Loss: 0.1150 - Validation Accuracy: 0.9370 - Epoch: 9 ## 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': 5e-05, 'decay_steps': 6000, '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-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3703 | 0.8683 | 0.1511 | 0.9315 | 0 | | 0.1208 | 0.9414 | 0.1145 | 0.9380 | 1 | | 0.0820 | 0.9561 | 0.1150 | 0.9370 | 2 | | 0.0656 | 0.9681 | 0.1150 | 0.9370 | 3 | | 0.0643 | 0.9671 | 0.1150 | 0.9370 | 4 | | 0.0652 | 0.9697 | 0.1150 | 0.9370 | 5 | | 0.0646 | 0.9689 | 0.1150 | 0.9370 | 6 | | 0.0651 | 0.9678 | 0.1150 | 0.9370 | 7 | | 0.0651 | 0.9691 | 0.1150 | 0.9370 | 8 | | 0.0659 | 0.9661 | 0.1150 | 0.9370 | 9 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.0 - Tokenizers 0.13.2

5d1544d2458333f194d60107ead1bd88

shirshakach/function-arg-swap-model-148k-files-365k-samples

shirshakach

distilbert

15

0

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,101

false

# function-arg-swap-model-148k-files-365k-samples 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: 0.4783 - Accuracy: 0.7679 - Precision: 0.7641 - Recall: 0.7812 - F1 score: 0.7725 ## 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: 10 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

6f0b3c8a8d594586cc8adb9997830a13

spacy/xx_sent_ud_sm

spacy

null

17

79

spacy

0

null

false

cc-by-sa-3.0

['multilingual']

null

0

['spacy']

false

true

1,509

false

### Details: https://spacy.io/models/xx#xx_sent_ud_sm Multi-language pipeline optimized for CPU. Components: senter. | Feature | Description | | --- | --- | | **Name** | `xx_sent_ud_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `senter` | | **Components** | `senter` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.8 (UD_Afrikaans-AfriBooms, UD_Croatian-SET, UD_Czech-CAC, UD_Czech-CLTT, UD_Danish-DDT, UD_Dutch-Alpino, UD_Dutch-LassySmall, UD_English-EWT, UD_Finnish-FTB, UD_Finnish-TDT, UD_French-GSD, UD_French-Spoken, UD_German-GSD, UD_Indonesian-GSD, UD_Irish-IDT, UD_Italian-TWITTIRO, UD_Korean-GSD, UD_Korean-Kaist, UD_Latvian-LVTB, UD_Lithuanian-ALKSNIS, UD_Lithuanian-HSE, UD_Marathi-UFAL, UD_Norwegian-Bokmaal, UD_Norwegian-Nynorsk, UD_Norwegian-NynorskLIA, UD_Persian-Seraji, UD_Portuguese-Bosque, UD_Portuguese-GSD, UD_Romanian-Nonstandard, UD_Romanian-RRT, UD_Russian-GSD, UD_Russian-Taiga, UD_Serbian-SET, UD_Slovak-SNK, UD_Spanish-GSD, UD_Swedish-Talbanken, UD_Telugu-MTG, UD_Vietnamese-VTB)](https://universaldependencies.org/) (Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell; et al.) | | **License** | `CC BY-SA 3.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 98.59 | | `TOKEN_P` | 95.31 | | `TOKEN_R` | 95.72 | | `TOKEN_F` | 95.52 | | `SENTS_P` | 90.66 | | `SENTS_R` | 81.58 | | `SENTS_F` | 85.88 |

6bc789d787d98f103b24b997a4f7efc8

seonghyeonye/flipped_11B

seonghyeonye

t5

12

4

transformers

6

text2text-generation

true

false

apache-2.0

['en']

['bigscience/P3']

null

0

[]

false

true

5,693

false

**Official repository**: [seonghyeonye/Flipped-Learning](https://github.com/seonghyeonye/Flipped-Learning) # Model Description FLIPPED uses a unique meta-learning method to show zero-shot task generalization on classification natural language prompts, outperforming GPT-3 and T0-11B on many tasks with a 4x smaller scale. It is a series of encoder-decoder model trained on a numerous classification dataset. We show inputs and its corresponding outputs of each instances in each dataset to FLIPPED, and train it to generate its possible instruction. We add unlikelihood loss in order **not** to generate the instruction when given the same input, but a wrong output. To obtain FLIPPED, we fine-tune a T5 model in a given scale on a multitask mixture covering many different classification NLP tasks. # Intended uses You can use the models to perform inference on tasks by specifying your input-output NLP query in a "input: {input}\noutput: {output}" form , and the model will predict the instruction. For example, You can try *"input: <extra_id_0> this is the best cast iron skillet you will ever buy<extra_id_1>\noutput: Positive"* as an input, and the model will hopefully generate *"Title: Review:"*. # How to use Our overall explanation models along with ablations can be found in our [paper](https://arxiv.org/abs/2210.02969). We recommend using the [FLIPPED-11B](seonghyeonye/flipped_11B) checkpoint as it leads (on average) to the best performances on a variety of NLP tasks. |Model|Number of parameters| |-|-| |[Flipped_11B](https://huggingface.co/seonghyeonye/flipped_11B)|11 billion| |[Flipped_3B](https://huggingface.co/seonghyeonye/flipped_3B)|3 billion| Here is how to download the model in PyTorch: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("seonghyeonye/flipped_11B") tokenizer = T5Tokenizer.from_pretrained("seonghyeonye/flipped_11B") ``` If you want to use another checkpoint, please replace the path in `T5Tokenizer` and `T5ForConditionalGeneration`. We also provide a quick [Jupyter Notebook](https://github.com/seonghyeonye/Flipped-Learning/blob/master/flipped_inference.ipynb) where you can inference with our method. **Note: the model was trained with bfloat16 activations. As such, we highly discourage running inference with fp16.** # Training procedure FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-xxl), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). At a high level, the input text along with output label is fed to the encoder and the instruction text is produced by the decoder. The model is fine-tuned to autoregressively generate the target. We also feed input text along with a wrong input, adding an unlikelihood loss in order not to make model produce the proper instruction in that case. Here are our training details. Training details: - Fine-tuning steps: 5'000 - Input sequence length: 384 - Target sequence length: 64 - Batch size: 240 - Optimizer: Adafactor - Learning rate: 5e-5 - Dropout: 0.1 - Sampling strategy: proportional to the number of examples in each dataset (we randomly sampled any dataset if it has over 500'000 examples so that it has at most 500'000 examples. Also, we randomly choose which instruction to generate for each training steps, so ideally each instruction appears *num_examples/num_templates* while training.) # Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |FLIPPED-11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Topic Classification: AG News, DBPedia<br>- Paraphrase Identification: MRPC, PAWS, QQP| |FLIPPED_3B|Same as FLIPPED-11B| We only choose prompts examples that has output lables, which can be found on the dataset page. # Evaluation data We evaluate our models on following datasets: |Task category|Datasets| |-|-| |Natural language inference|ANLI(R1, R2, R3), CB, RTE| |Coreference resolution|WSC, Winogrande| |Word sense disambiguation|WiC| |Sentence completion|COPA, HellaSwag, Story Cloze| |QA|PIQA, ARC-Challenge, OpenbookQA| We also evaluate FLIPPED on a subset of [BIG-bench benchmark](https://github.com/google/BIG-bench): - Code description task - Conceptual combinations - Hindu knowledge json - Known unknowns - Language identification - Logic grid puzzle task - Logical deduction - Common misconceptions - Movie dialog same or different - Novel concepts - Strategyqa - Formal fallacies syllogisms negation - VitaminC - Winowhy multiple choice # Label generalization We evaluate the robustness of models on following datasets with changing the output label of the datasets. The substitute words can be found in our [paper](https://arxiv.org/abs/2210.02969). |Task category|(Datasets, Template name)| |-|-| |Unseen tasks|(WSC, does the pronoun refer to), (CB, can we infer), (RTE, MNLI crowdsource)| |Seen tasks|(IMDB, Reviewer Enjoyment Yes No), (PAWS, Meaning) | The template name we used can be found in the [promptsource template library](https://github.com/bigscience-workshop/promptsource/tree/main/promptsource/templates). # BibTeX entry and citation info ```bibtex @article{ye2022guess, title={Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners}, author={Ye, Seonghyeon and Kim, Doyoung and Jang, Joel and Shin, Joongbo and Seo, Minjoon}, journal={arXiv preprint arXiv:2210.02969}, year={2022} } ```

47ee4b27bf1ce3b44bc936574449cd9e

shed-e/MLM

shed-e

distilbert

9

5

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

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: 2.4353 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.6954 | 1.0 | 157 | 2.5243 | | 2.563 | 2.0 | 314 | 2.4738 | | 2.5258 | 3.0 | 471 | 2.4369 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

03cfccdefd4b5e4292e13a2924f726f5

McGill-NLP/bart-qg-nq-checkpoint

McGill-NLP

bart

7

1

transformers

0

text2text-generation

true

false

cc-by-4.0

null

0

[]

false

true

4,687

false

# BART-base fine-tuned on NaturalQuestions for **Question Generation** [BART Model](https://arxiv.org/pdf/1910.13461.pdf) fine-tuned on [Google NaturalQuestions](https://ai.google.com/research/NaturalQuestions/) for **Question Generation** by treating long answer as input, and question as output. ## Details of BART The **BART** model was presented in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by *Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer* in Here the abstract: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance. ## Details of the downstream task (QG) - Dataset 📚 🧐 Dataset: ```NaturalQuestions``` from Google (https://ai.google.com/research/NaturalQuestions/) | Dataset | Split | # samples | | -------- | ----- | --------- | | NaturalQuestions | train | 97650 | | NaturalQuestions | valid | 10850 | ## Model fine-tuning 🏋️‍ The training script can be found [here](https://github.com/McGill-NLP/MLQuestions/blob/main/QG/train.py) ## Model in Action 🚀 ```python from transformers import AutoModel, BartTokenizer #Load the tokenizer tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') #Load the model model = AutoModelForSeq2SeqLM.from_pretrained("McGill-NLP/bart-qg-nq-checkpoint") ``` ## Citation If you want to cite this model you can use this: ```bibtex @inproceedings{kulshreshtha-etal-2021-back, title = "Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval", author = "Kulshreshtha, Devang and Belfer, Robert and Serban, Iulian Vlad and Reddy, Siva", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.566", pages = "7064--7078", abstract = "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.", } ``` > Created by [Devang Kulshreshtha](https://geekydevu.netlify.app/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain

6a46da7ac00d29ceafda71253ac10da2

madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1

madlag

bert

83

42

transformers

0

question-answering

true

false

mit

['en']

['squad']

null

0

['question-answering', 'bert', 'bert-base']

false

true

2,700

false

## BERT-base uncased model fine-tuned on SQuAD v1 This model is block sparse: the **linear** layers contains **7.5%** of the original weights. The model contains **28.2%** of the original weights **overall**. The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method. That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.92x** faster than an dense networks on the evaluation, at the price of some impact on the accuracy (see below). This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the equivalent model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1). This model is case-insensitive: it does not make a difference between english and English. ## Pruning details A side-effect of the block pruning is that some of the attention heads are completely removed: 106 heads were removed on a total of 144 (73.6%). Here is a detailed view on how the remaining heads are distributed in the network after pruning. ![Pruning details](https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1/raw/main/model_card/pruning.svg) ## Density plot <script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1/raw/main/model_card/density.js" id="9301e950-59b1-497b-a2c5-25c24e07b3a0"></script> ## Details | Dataset | Split | # samples | | -------- | ----- | --------- | | SQuAD1.1 | train | 90.6K | | SQuAD1.1 | eval | 11.1k | ### Fine-tuning - Python: `3.8.5` - Machine specs: ```CPU: Intel(R) Core(TM) i7-6700K CPU Memory: 64 GiB GPUs: 1 GeForce GTX 3090, with 24GiB memory GPU driver: 455.23.05, CUDA: 11.1 ``` ### Results **Pytorch model file size**: `335M` (original BERT: `438M`) | Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| | ------ | --------- | --------- | | **EM** | **71.88** | **80.8** | | **F1** | **81.36** | **88.5** | ## Example Usage ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1", tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1" ) predictions = qa_pipeline({ 'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.", 'question': "Who is Frederic Chopin?", }) print(predictions) ```

970ddfba19b9e4816816b9ae508344e6

cahya/wav2vec2-large-xlsr-turkish-artificial

cahya

wav2vec2

9

7

transformers

1

automatic-speech-recognition

true

false

true

apache-2.0

['tr']

['common_voice']

null

0

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

true

3,445

false

# Wav2Vec2-Large-XLSR-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 66.98 % ## Training The Artificial Common Voice `train`, `validation` is used to fine tune the model The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)

c0c3fb6f38007e10e70dc5d24311729c

gagan3012/pickuplines

gagan3012

gpt2

27

4

transformers

1

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

966

false

# pickuplines 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: 5.7873 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

7ad9f8240f03974886f7bb5e98a6bbb2

romainlhardy/t5-small-booksum

romainlhardy

t5

10

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,210

false

# t5-small-booksum 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: 3.1700 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3266 | 1.0 | 29228 | 3.1859 | | 3.2947 | 2.0 | 58456 | 3.1700 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.12.1

36a66ee4c6cc5c67bc9d273d009bdb86

Joeythemonster/test

Joeythemonster

null

18

14

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

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

false

true

614

false

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

2d0b00971c7b0e45210e557d78e1ed48

sd-dreambooth-library/Origtron

sd-dreambooth-library

null

22

15

diffusers

4

text-to-image

false

mit

null

4

0

4

0

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

false

true

1,012

false

Model trained in the [Shivam Shrirao](https://colab.research.google.com/github/ShivamShrirao/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb?authuser=2#scrollTo=jXgi8HM4c-DA) google colab, dreambooth. It was made with various screen captures I took from videos of TRON from 1982. The original trailer and 2 long movie clips. Download the **origtron.ckpt** file to: _stable-diffusion-webui\models\Stable-diffusion_ once it's downloaded just use the prompt **origtron** and you'll get some great results. The file size is 2.3gb. ### Images I created ![Cityscape made with it](https://huggingface.co/Nutronic/origtron/resolve/main/randomcity-origtron.png) ![Henry](https://huggingface.co/Nutronic/origtron/resolve/main/HenryCavill-origtron.png) ![Scarlett](https://huggingface.co/Nutronic/origtron/resolve/main/ScarlettJo-origtron.png) ![Tom](https://huggingface.co/Nutronic/origtron/resolve/main/00038-1614352352-Tom%20Cruise%20running%20quickly%20through%20an%20origtron%20city.png)

65604e0754b9c07879b445e9ab559e7b

ajaiswal1008/wav2vec2-large-xls-r-300m-hi-colab_new

ajaiswal1008

wav2vec2

13

9

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,104

false

# wav2vec2-large-xls-r-300m-hi-colab_new This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

7f55875d58d921665ccdb20401ead24e

RichVip/Cute_RichStyle_1.5

RichVip

null

5

0

null

3

null

false

apache-2.0

null

0

['BABY', 'BABIES', 'LITTLE', 'SD2.1', 'DIGITAL ART', 'CUTE', 'MIDJOURNEY', 'DOLLS', 'CHARACTER', 'CARTOON']

false

true

3,610

false

# Cute RichStyle - 512x512 Model trained in SD 1.5 with photos generated with Midjourney, created to generate people, animals/creatures... You can also make objects... landscapes, etc, but maybe you need more tries: - 30 steps - 7cfg - euler a,ddim, dpm++sde... - you can use different resolutions, you can generate interesting things Characters rendered with the model: ![alt text](https://huggingface.co/RichVip/Cute_RichStyle_1.5/resolve/main/cbzbb%201.5%20(2).jpg) ![alt text](https://huggingface.co/RichVip/Cute_RichStyle_1.5/resolve/main/cbzbb%201.5%20(1).jpg) **TOKEN**: cbzbb, cbzbb style, cbzbb style of _____ , you can put the token , (it is not required) but it is better to put it. Many times the token between () works better possible positives: cute, little, baby, beautiful, fantasy art, devian art, trending artstation, digital art, detailed, cute, realistic, humanoide, character, tiny, film still of "____" , cinematic shot, "__" environment, beautiful landspace of _____, cinematic portrait of ______, cute character as a "_".... most important negatives (not mandatory but they help a lot) : pencil draw, bad photo, bad draw other possible negatives: cartoon, woman, man, person, people, character, super hero, iron man, baby, anime... ((When you generate the photo, there are times when it tries to create a person/character, that's why the negative character prompts etc...)) - landscape prompts better between ( ) or more parentheses, although it is not always necessary - you can use other styles, removing the "cbzbb" token and adding pencil draw, lego style.. watercolor etc etc, it doesn't make the exact photo style with which I trained it but they look great too!! - Most of the photos are daytime, to create nights it once worked with: - positive: (dark), (black sky) (dark sky) etc etc - negative: (blue day), (day light), (day) (sun) etc etc - To increase quality: send the photo that you like the most to img2img (30-steps), 0.60-80, generate 4 photos, choose one or repeat (with less donoising to make it look more like the original, or more to make it change more ), resend via img2img (you can raise the ratio/aspect of the image a bit), lower the denoising to 0.40-0.50, generate 2/4 images, choose the one you like the most and have more detail, send to img2img uploading the photo scale (same ratio/aspect,) and at 0.15-0.30 50 steps, generate 1 photo, if you want you can continue rescaling it for more detail and more resolution - Change person/character in the image: if you like the photo but want to change the character, send a photo to img2img, change the name of the character or person or animal and between 0.7-1 denoising **Prompt examples:** cbzbb style of a pennywise michael jackson, cbzbb, detailed, fantasy,super cute, trending on artstation cbzbb style of angry baby groot cute panda reading a book, cbzbb style ## ENJOY !!!! The creations of the images are absolutely yours! But if you can share them with me on Twitter or Instagram or reddit, anywhere , I'd LOVE to SEE what you can do with the model! - **Twitter:** @RichViip - **Instagram**: richviip - **Reddit:** Richviip Thank you for the support and great help of ALL the people on Patricio's discord, who were at every moment of the creation of the model giving their opinion on more than 15 different types of models and making my head hurt less! Social media of Patricio, follow him!! - **Youtube:** patricio-fernandez - **Twitter:** patriciofernanf

d2afeb57af71415aefea341df092acee

marccgrau/whisper-small-allSNR-v4

marccgrau

whisper

13

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['marccgrau/sbbdata_allSNR']

null

0

['sbb-asr', 'generated_from_trainer']

true

1,659

false

# Whisper Small German SBB all SNR - v4 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set: - Loss: 0.0287 - Wer: 0.0222 ## 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-06 - 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: 100 - training_steps: 700 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.6894 | 0.71 | 100 | 0.4702 | 0.4661 | | 0.1896 | 1.42 | 200 | 0.0322 | 0.0241 | | 0.0297 | 2.13 | 300 | 0.0349 | 0.0228 | | 0.0181 | 2.84 | 400 | 0.0250 | 0.0209 | | 0.0154 | 3.55 | 500 | 0.0298 | 0.0209 | | 0.0112 | 4.26 | 600 | 0.0327 | 0.0222 | | 0.009 | 4.96 | 700 | 0.0287 | 0.0222 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.12.1

4cc12b3b46ebbac1511b5342a94b429f

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_mrpc_96

gokuls

distilbert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,100

false

# distilbert_sa_GLUE_Experiment_logit_kd_mrpc_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5290 - Accuracy: 0.3162 - F1: 0.0 - Combined Score: 0.1581 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:--------------:| | 0.5507 | 1.0 | 15 | 0.5375 | 0.3162 | 0.0 | 0.1581 | | 0.5355 | 2.0 | 30 | 0.5312 | 0.3162 | 0.0 | 0.1581 | | 0.531 | 3.0 | 45 | 0.5296 | 0.3162 | 0.0 | 0.1581 | | 0.5292 | 4.0 | 60 | 0.5290 | 0.3162 | 0.0 | 0.1581 | | 0.5278 | 5.0 | 75 | 0.5290 | 0.3162 | 0.0 | 0.1581 | | 0.5292 | 6.0 | 90 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5279 | 7.0 | 105 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5288 | 8.0 | 120 | 0.5291 | 0.3162 | 0.0 | 0.1581 | | 0.5282 | 9.0 | 135 | 0.5291 | 0.3162 | 0.0 | 0.1581 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

8849dd36fb94fcef2cba73133e73f4ea

troesy/distilbert-base-cased-3epoch-LaTTrue-updatedAlligning

troesy

distilbert

14

6

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,291

false

# distilbert-base-cased-3epoch-LaTTrue-updatedAlligning 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: 0.1790 ## 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 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 174 | 0.1690 | | No log | 2.0 | 348 | 0.1739 | | 0.1311 | 3.0 | 522 | 0.1790 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

291d364c11241277f130251c42df6977

tbasic5/distilbert-base-uncased-finetuned-emotion

tbasic5

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

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: 0.2222 - Accuracy: 0.925 - F1: 0.9250 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8521 | 1.0 | 250 | 0.3164 | 0.907 | 0.9038 | | 0.2549 | 2.0 | 500 | 0.2222 | 0.925 | 0.9250 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

e21d272d865dbb8f01a95492c8a3628a

anas-awadalla/splinter-large-few-shot-k-32-finetuned-squad-seed-4

anas-awadalla

splinter

16

1

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,006

false

# splinter-large-few-shot-k-32-finetuned-squad-seed-4 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 12 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

9ca33b6488e8e4bd28e604b39abcd305

henryscheible/eval_masked_v4_sst2

henryscheible

null

13

0

null

0

null

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,010

false

# eval_masked_v4_sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3821 - Accuracy: 0.9209 ## 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: 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 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

b627ff755b1026949f1f67fc2923ab04

ParhamAbdarzade/finetuning-sentiment-model-20000-samples-imdb-v2

ParhamAbdarzade

distilbert

12

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,416

false

# finetuning-sentiment-model-20000-samples-imdb-v2 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: 0.3694 - Accuracy: 0.924 - F1: 0.9242 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2795 | 1.0 | 2500 | 0.2224 | 0.9275 | 0.9263 | | 0.1877 | 2.0 | 5000 | 0.3141 | 0.9275 | 0.9274 | | 0.1045 | 3.0 | 7500 | 0.3694 | 0.924 | 0.9242 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

f99e56d4192a7720714d2712fb11c42c

Lvxue/distilled-mt5-small-0.03-1

Lvxue

mt5

14

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,037

false

# distilled-mt5-small-0.03-1 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: 2.8063 - Bleu: 7.1839 - Gen Len: 45.5733 ## 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

219f6b75037f0d94daf14143cf265880

LeBenchmark/wav2vec2-FR-7K-large

LeBenchmark

wav2vec2

6

1,255

transformers

5

feature-extraction

true

false

apache-2.0

['fr']

null

0

['wav2vec2']

false

true

4,537

false

# LeBenchmark: wav2vec2 large model trained on 7K hours of French speech LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information on the different benchmarks that can be used to evaluate the wav2vec2 models, please refer to our paper at: [Task Agnostic and Task Specific Self-Supervised Learning from Speech with LeBenchmark](https://openreview.net/pdf?id=TSvj5dmuSd) ## Model and data descriptions We release four different models that can be found under our HuggingFace organization. Two different wav2vec2 architectures *Base* and *Large* are coupled with our small (1K), medium (3K), and large (7K) corpus. A larger one should come later. In short: - [wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large): Large wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown). - [wav2vec2-FR-7K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-base): Base wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown). - [wav2vec2-FR-3K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large): Large wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown). - [wav2vec2-FR-3K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-base): Base wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown). - [wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2.6K-base): Base wav2vec2 trained on 2.6K hours of French speech (**no spontaneous speech**). - [wav2vec2-FR-1K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-large): Large wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females). - [wav2vec2-FR-1K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-base): Base wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females). ## Intended uses & limitations Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced. ## Fine-tune with Fairseq for ASR with CTC As our wav2vec2 models were trained with Fairseq, then can be used in the different tools that they provide to fine-tune the model for ASR with CTC. The full procedure has been nicely summarized in [this blogpost](https://huggingface.co/blog/fine-tune-wav2vec2-english). Please note that due to the nature of CTC, speech-to-text results aren't expected to be state-of-the-art. Moreover, future features might appear depending on the involvement of Fairseq and HuggingFace on this part. ## Integrate to SpeechBrain for ASR, Speaker, Source Separation ... Pretrained wav2vec models recently gained in popularity. At the same time, [SpeechBrain toolkit](https://speechbrain.github.io) came out, proposing a new and simpler way of dealing with state-of-the-art speech & deep-learning technologies. While it currently is in beta, SpeechBrain offers two different ways of nicely integrating wav2vec2 models that were trained with Fairseq i.e our LeBenchmark models! 1. Extract wav2vec2 features on-the-fly (with a frozen wav2vec2 encoder) to be combined with any speech-related architecture. Examples are: E2E ASR with CTC+Att+Language Models; Speaker Recognition or Verification, Source Separation ... 2. *Experimental:* To fully benefit from wav2vec2, the best solution remains to fine-tune the model while you train your downstream task. This is very simply allowed within SpeechBrain as just a flag needs to be turned on. Thus, our wav2vec2 models can be fine-tuned while training your favorite ASR pipeline or Speaker Recognizer. **If interested, simply follow this [tutorial](https://colab.research.google.com/drive/17Hu1pxqhfMisjkSgmM2CnZxfqDyn2hSY?usp=sharing)** ## Referencing LeBenchmark ``` @article{Evain2021LeBenchmarkAR, title={LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech}, author={Sol{\`e}ne Evain and Ha Nguyen and Hang Le and Marcely Zanon Boito and Salima Mdhaffar and Sina Alisamir and Ziyi Tong and N. Tomashenko and Marco Dinarelli and Titouan Parcollet and A. Allauzen and Y. Est{\`e}ve and B. Lecouteux and F. Portet and S. Rossato and F. Ringeval and D. Schwab and L. Besacier}, journal={ArXiv}, year={2021}, volume={abs/2104.11462} } ```

7fd45fcd3092c0481715e0e6ec90de70

BSC-LT/roberta-large-bne-capitel-pos

BSC-LT

roberta

9

0

transformers

3

token-classification

true

false

apache-2.0

['es']

['bne', 'capitel']

null

0

['national library of spain', 'spanish', 'bne', 'capitel', 'pos']

false

true

1,667

false

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-pos # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2). ## Evaluation and results F1 Score: 0.9851 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

97e7812a12193f01658541ad6340aaa2

sudo-s/exper_batch_8_e8

sudo-s

vit

14

11

transformers

0

image-classification

true

false

apache-2.0

null

0

['image-classification', 'generated_from_trainer']

true

7,648

false

# exper_batch_8_e8 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set: - Loss: 0.4608 - Accuracy: 0.9052 ## 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 - num_epochs: 8 - mixed_precision_training: Apex, opt level O1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 4.2202 | 0.08 | 100 | 4.1245 | 0.1237 | | 3.467 | 0.16 | 200 | 3.5622 | 0.2143 | | 3.3469 | 0.23 | 300 | 3.1688 | 0.2675 | | 2.8086 | 0.31 | 400 | 2.8965 | 0.3034 | | 2.6291 | 0.39 | 500 | 2.5858 | 0.4025 | | 2.2382 | 0.47 | 600 | 2.2908 | 0.4133 | | 1.9259 | 0.55 | 700 | 2.2007 | 0.4676 | | 1.8088 | 0.63 | 800 | 2.0419 | 0.4742 | | 1.9462 | 0.7 | 900 | 1.6793 | 0.5578 | | 1.5392 | 0.78 | 1000 | 1.5460 | 0.6079 | | 1.561 | 0.86 | 1100 | 1.5793 | 0.5690 | | 1.2135 | 0.94 | 1200 | 1.4663 | 0.5929 | | 1.0725 | 1.02 | 1300 | 1.2974 | 0.6534 | | 0.8696 | 1.1 | 1400 | 1.2406 | 0.6569 | | 0.8758 | 1.17 | 1500 | 1.2127 | 0.6623 | | 1.1737 | 1.25 | 1600 | 1.2243 | 0.6550 | | 0.8242 | 1.33 | 1700 | 1.1371 | 0.6735 | | 1.0141 | 1.41 | 1800 | 1.0536 | 0.7024 | | 0.9855 | 1.49 | 1900 | 0.9885 | 0.7205 | | 0.805 | 1.57 | 2000 | 0.9048 | 0.7479 | | 0.7207 | 1.64 | 2100 | 0.8842 | 0.7490 | | 0.7101 | 1.72 | 2200 | 0.8954 | 0.7436 | | 0.5946 | 1.8 | 2300 | 0.9174 | 0.7386 | | 0.6937 | 1.88 | 2400 | 0.7818 | 0.7760 | | 0.5593 | 1.96 | 2500 | 0.7449 | 0.7934 | | 0.4139 | 2.04 | 2600 | 0.7787 | 0.7830 | | 0.2929 | 2.11 | 2700 | 0.7122 | 0.7945 | | 0.4159 | 2.19 | 2800 | 0.7446 | 0.7907 | | 0.4079 | 2.27 | 2900 | 0.7354 | 0.7938 | | 0.516 | 2.35 | 3000 | 0.7499 | 0.8007 | | 0.2728 | 2.43 | 3100 | 0.6851 | 0.8061 | | 0.4159 | 2.51 | 3200 | 0.7258 | 0.7999 | | 0.3396 | 2.58 | 3300 | 0.7455 | 0.7972 | | 0.1918 | 2.66 | 3400 | 0.6793 | 0.8119 | | 0.1228 | 2.74 | 3500 | 0.6696 | 0.8134 | | 0.2671 | 2.82 | 3600 | 0.6306 | 0.8285 | | 0.4986 | 2.9 | 3700 | 0.6111 | 0.8296 | | 0.3699 | 2.98 | 3800 | 0.5600 | 0.8508 | | 0.0444 | 3.05 | 3900 | 0.6021 | 0.8331 | | 0.1489 | 3.13 | 4000 | 0.5599 | 0.8516 | | 0.15 | 3.21 | 4100 | 0.6377 | 0.8365 | | 0.2535 | 3.29 | 4200 | 0.5752 | 0.8543 | | 0.2679 | 3.37 | 4300 | 0.5677 | 0.8608 | | 0.0989 | 3.45 | 4400 | 0.6325 | 0.8396 | | 0.0825 | 3.52 | 4500 | 0.5979 | 0.8524 | | 0.0427 | 3.6 | 4600 | 0.5903 | 0.8516 | | 0.1806 | 3.68 | 4700 | 0.5323 | 0.8628 | | 0.2672 | 3.76 | 4800 | 0.5688 | 0.8604 | | 0.2674 | 3.84 | 4900 | 0.5369 | 0.8635 | | 0.2185 | 3.92 | 5000 | 0.4743 | 0.8820 | | 0.2195 | 3.99 | 5100 | 0.5340 | 0.8709 | | 0.0049 | 4.07 | 5200 | 0.5883 | 0.8608 | | 0.0204 | 4.15 | 5300 | 0.6102 | 0.8539 | | 0.0652 | 4.23 | 5400 | 0.5659 | 0.8670 | | 0.028 | 4.31 | 5500 | 0.4916 | 0.8840 | | 0.0423 | 4.39 | 5600 | 0.5706 | 0.8736 | | 0.0087 | 4.46 | 5700 | 0.5653 | 0.8697 | | 0.0964 | 4.54 | 5800 | 0.5423 | 0.8755 | | 0.0841 | 4.62 | 5900 | 0.5160 | 0.8743 | | 0.0945 | 4.7 | 6000 | 0.5532 | 0.8697 | | 0.0311 | 4.78 | 6100 | 0.4947 | 0.8867 | | 0.0423 | 4.86 | 6200 | 0.5063 | 0.8843 | | 0.1348 | 4.93 | 6300 | 0.5619 | 0.8743 | | 0.049 | 5.01 | 6400 | 0.5800 | 0.8732 | | 0.0053 | 5.09 | 6500 | 0.5499 | 0.8770 | | 0.0234 | 5.17 | 6600 | 0.5102 | 0.8874 | | 0.0192 | 5.25 | 6700 | 0.5447 | 0.8836 | | 0.0029 | 5.32 | 6800 | 0.4787 | 0.8936 | | 0.0249 | 5.4 | 6900 | 0.5232 | 0.8870 | | 0.0671 | 5.48 | 7000 | 0.4766 | 0.8975 | | 0.0056 | 5.56 | 7100 | 0.5136 | 0.8894 | | 0.003 | 5.64 | 7200 | 0.5085 | 0.8882 | | 0.0015 | 5.72 | 7300 | 0.4832 | 0.8971 | | 0.0014 | 5.79 | 7400 | 0.4648 | 0.8998 | | 0.0065 | 5.87 | 7500 | 0.4739 | 0.8978 | | 0.0011 | 5.95 | 7600 | 0.5349 | 0.8867 | | 0.0021 | 6.03 | 7700 | 0.5460 | 0.8847 | | 0.0012 | 6.11 | 7800 | 0.5309 | 0.8890 | | 0.0011 | 6.19 | 7900 | 0.4852 | 0.8998 | | 0.0093 | 6.26 | 8000 | 0.4751 | 0.8998 | | 0.003 | 6.34 | 8100 | 0.4934 | 0.8963 | | 0.0027 | 6.42 | 8200 | 0.4882 | 0.9029 | | 0.0009 | 6.5 | 8300 | 0.4806 | 0.9021 | | 0.0009 | 6.58 | 8400 | 0.4974 | 0.9029 | | 0.0009 | 6.66 | 8500 | 0.4748 | 0.9075 | | 0.0008 | 6.73 | 8600 | 0.4723 | 0.9094 | | 0.001 | 6.81 | 8700 | 0.4692 | 0.9098 | | 0.0007 | 6.89 | 8800 | 0.4726 | 0.9075 | | 0.0011 | 6.97 | 8900 | 0.4686 | 0.9067 | | 0.0006 | 7.05 | 9000 | 0.4653 | 0.9056 | | 0.0006 | 7.13 | 9100 | 0.4755 | 0.9029 | | 0.0007 | 7.2 | 9200 | 0.4633 | 0.9036 | | 0.0067 | 7.28 | 9300 | 0.4611 | 0.9036 | | 0.0007 | 7.36 | 9400 | 0.4608 | 0.9052 | | 0.0007 | 7.44 | 9500 | 0.4623 | 0.9044 | | 0.0005 | 7.52 | 9600 | 0.4621 | 0.9056 | | 0.0005 | 7.6 | 9700 | 0.4615 | 0.9056 | | 0.0005 | 7.67 | 9800 | 0.4612 | 0.9059 | | 0.0005 | 7.75 | 9900 | 0.4626 | 0.9075 | | 0.0004 | 7.83 | 10000 | 0.4626 | 0.9075 | | 0.0005 | 7.91 | 10100 | 0.4626 | 0.9075 | | 0.0006 | 7.99 | 10200 | 0.4626 | 0.9079 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.5.1 - Datasets 2.3.2 - Tokenizers 0.12.1

2c2f1c2c63062d9d0154cd08c5f9efe4

hfl/minirbt-h256

hfl

bert

6

276

transformers

4

fill-mask

true

false

apache-2.0

['zh']

null

0

['bert']

false

true

913

false

# Please use 'Bert' related functions to load this model! ## Chinese small pre-trained model MiniRBT In order to further promote the research and development of Chinese information processing, we launched a Chinese small pre-training model MiniRBT based on the self-developed knowledge distillation tool TextBrewer, combined with Whole Word Masking technology and Knowledge Distillation technology. This repository is developed based on：https://github.com/iflytek/MiniRBT You may also interested in, - Chinese LERT: https://github.com/ymcui/LERT - Chinese PERT: https://github.com/ymcui/PERT - Chinese MacBERT: https://github.com/ymcui/MacBERT - 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/iflytek/HFL-Anthology

286393dbedba4303ef33c8bf6c4bba70

Payoto/t5-small-finetuned-xsum

Payoto

t5

7

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['xsum']

null

0

['generated_from_trainer']

true

1,279

false

# t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6962 | 1.0 | 3188 | 2.5273 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0+cpu - Datasets 2.7.1 - Tokenizers 0.12.1

a95e6447551e00682cdc03a4be54da95

junjuice0/VOXO

junjuice0

null

18

302

diffusers

23

text-to-image

false

creativeml-openrail-m

['en']

null

0

[]

false

true

1,050

false

![thumbnail](https://media.discordapp.net/attachments/1002437703192821910/1073391952977989632/thumb.png) # VOXO Merged model by junjuice0. This model was originally created just for me, so I am not after quality and please don't expect too much. I may release finetune version of this model in the future, but only God knows if I am willing to do it until then. [JOIN US(日本語)](https://discord.gg/ai-art) # VOXO-Vtuber (VOXO-v0-vtuber.safetensors) This model can generate vtubers for Hololive and Nijisanji. Some vtubers may or may not come out well. It is recommended to give the name a weight of about 1.2 (e.g. (ange katrina:1.2)) # RECOMMENDED It is recommended to use TIs such as bad-images or bad-prompt for negative prompts. Also, quality prompts (e.g. masterpiece, high quality) are not required. The use of highres. fix may change the painting considerably, use according to your preference. # HOW TO USE The usage is the same as other diffusion models, and it would be easier to read other people's explanations than mine here.

33c1689e3a2d3cf66ef7831b1200fde3

anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42

anas-awadalla

bert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,056

false

# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - 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_ratio: 0.1 - training_steps: 200 ### Training results {'exact_match': 12.93282876064333, 'f1': 21.98821604201723} ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

31a1a3a2925df56d750f58f328af0d02

gokuls/bert-base-emotion-intent

gokuls

bert

13

3

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,492

false

# bert-base-emotion-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Accuracy: 0.9385 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4058 | 1.0 | 1000 | 0.2421 | 0.9265 | | 0.1541 | 2.0 | 2000 | 0.1952 | 0.9385 | | 0.1279 | 3.0 | 3000 | 0.1807 | 0.9345 | | 0.1069 | 4.0 | 4000 | 0.2292 | 0.9365 | | 0.081 | 5.0 | 5000 | 0.3315 | 0.936 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

928d4f7420b6c8beac2a5814b68d02bb

henryscheible/stsb_bert-base-uncased_144_v2

henryscheible

null

13

0

null

0

null

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,064

false

# stsb_bert-base-uncased_144_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4994 - Pearson: 0.8900 - Spearmanr: 0.8864 - Combined Score: 0.8882 ## 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: 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 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

097ef03c85f3564399f035680816c609

nitrosocke/Arcane-Diffusion

nitrosocke

null

25

25,268

diffusers

584

text-to-image

false

creativeml-openrail-m

null

15

4

6

5

14

10

4

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

false

true

3,318

false

# Arcane Diffusion This is the fine-tuned Stable Diffusion model trained on images from the TV Show Arcane. Use the tokens **_arcane style_** in your prompts for the effect. **If you enjoy my work, please consider supporting me** [![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://patreon.com/user?u=79196446) ### 🧨 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). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python #!pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/Arcane-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "arcane style, a magical princess with golden hair" image = pipe(prompt).images[0] image.save("./magical_princess.png") ``` # Gradio & Colab We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/anzorq/finetuned_diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j5YvfMZoGdDGdj3O3xRU1m4ujKYsElZO?usp=sharing) ![img](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/magical_princess.png) ### Sample images from v3: ![output Samples v3](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-v3-samples-01.jpg) ![output Samples v3](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-v3-samples-02.jpg) ### Sample images from the model: ![output Samples](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-output-images.jpg) ### Sample images used for training: ![Training Samples](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-training-images.jpg) **Version 3** (arcane-diffusion-v3): This version uses the new _train-text-encoder_ setting and improves the quality and edibility of the model immensely. Trained on 95 images from the show in 8000 steps. **Version 2** (arcane-diffusion-v2): This uses the diffusers based dreambooth training and prior-preservation loss is way more effective. The diffusers where then converted with a script to a ckpt file in order to work with automatics repo. Training was done with 5k steps for a direct comparison to v1 and results show that it needs more steps for a more prominent result. Version 3 will be tested with 11k steps. **Version 1** (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.

f3b6288b00a4072858b7a121efd4ade9

deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc

deepdoctection

null

5

0

null

0

null

false

apache-2.0

null

['Pubtabnet']

null

0

['Tensorflow']

false

true

2,951

false

# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683). The model has been trained on detecting rows and columns for tables. As rows and column bounding boxes are not a priori an element of the annotations they are calculated using the bounding boxes of the cells and the intrinsic structure of the enclosed HTML. The code has been adapted so that it can be used in a **deep**doctection pipeline. ## How this model can be used This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial. ## How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn pubtabnet = DatasetRegistry.get_dataset("pubtabnet") pubtabnet.dataflow.categories.set_cat_to_sub_cat({"ITEM":"row_col"}) pubtabnet.dataflow.categories.filter_categories(categories=["ROW","COLUMN"]) path_config_yaml=os.path.join(get_configs_dir_path(),"tp/rows/conf_frcnn_rows.yaml") path_weights = "" dataset_train = pubtabnet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50"] build_train_config=["max_datapoints=500000","rows_and_cols=True"] dataset_val = pubtabnet build_val_config = ["max_datapoints=2000","rows_and_cols=True"] coco_metric = MetricRegistry.get_metric("coco") coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]]) train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ``` ## How to fine-tune this model To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.

df8f1332bfc042531344035c4496f52c

theojolliffe/bart-paraphrase-v0.75-e1

theojolliffe

bart

12

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,456

false

# bart-paraphrase-v0.75-e1 This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1865 - Rouge1: 71.3427 - Rouge2: 66.0011 - Rougel: 69.8855 - Rougelsum: 69.9796 - Gen Len: 19.6036 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1373 | 1.0 | 2660 | 0.1865 | 71.3427 | 66.0011 | 69.8855 | 69.9796 | 19.6036 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

2a1cd90d97ca771277c71db42a402f43

Oesnim/chaper01_2

Oesnim

null

2

0

null

0

null

false

openrail

null

0

[]

false

true

604

false

text="""Dear Amazon, last week I ordered an Optimus Prime action figure from your online store in Germany. Unfortunately, when I opened the package, I discovered to my horror that I had been sent an action figure of Megatron instead! As a lifelong enemy of the Deceptions, I hope yoou can understand my dilemma. To resolve the issue, I demand an exchange of Megatron for the Optimus Prime figure I ordered. Enclosed are copies of my records concerning this purchase. I expect to hear from you soon. Sincerely, Bumblebee.""" from transformers import pipeline classifier = pipeline("text-classification")

46e9203fed693746aacb3e07f0fa6d87

Unbabel/wmt22-comet-da

Unbabel

null

5

0

null

0

translation

false

apache-2.0

['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sa', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'xh', 'yi', 'zh']

null

0

[]

false

true

2,791

false

This is a [COMET](https://github.com/Unbabel/COMET) evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference. # Paper [COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://aclanthology.org/2022.wmt-1.52) (Rei et al., WMT 2022) # License Apache-2.0 # Usage (unbabel-comet) Using this model requires unbabel-comet to be installed: ```bash pip install --upgrade pip # ensures that pip is current pip install unbabel-comet ``` Then you can use it through comet CLI: ```bash comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/wmt22-comet-da ``` Or using Python: ```python from comet import download_model, load_from_checkpoint model_path = download_model("Unbabel/wmt22-comet-da") model = load_from_checkpoint(model_path) data = [ { "src": "Dem Feuer konnte Einhalt geboten werden", "mt": "The fire could be stopped", "ref": "They were able to control the fire." }, { "src": "Schulen und Kindergärten wurden eröffnet.", "mt": "Schools and kindergartens were open", "ref": "Schools and kindergartens opened" } ] model_output = model.predict(data, batch_size=8, gpus=1) print (model_output) ``` # Intended uses Our model is intented to be used for **MT evaluation**. Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation. # Languages Covered: This model builds on top of XLM-R which cover the following languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish. Thus, results for language pairs containing uncovered languages are unreliable!

a1f517c7d3dfee8997ef4548e12a2af9

DrishtiSharma/whisper-large-v2-assamese-700-steps

DrishtiSharma

whisper

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,314

false

# Whisper Large Assamese - Drishti Sharma This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2452 - Wer: 21.4582 ## 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: 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: 100 - training_steps: 700 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0109 | 4.32 | 700 | 0.2452 | 21.4582 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

c4a0e3042cc62e9d84e515b3811edd62

Geotrend/bert-base-en-el-cased

Geotrend

bert

8

4

transformers

0

fill-mask

true

apache-2.0

['multilingual']

['wikipedia']

null

1

0

[]

false

true

1,292

false

# bert-base-en-el-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-el-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-el-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

3b8387cb8a02b24ff8488881e9b75bb1

armandnlp/distilbert-base-uncased-finetuned-emotion

armandnlp

distilbert

14

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

3

1

2

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: 0.2237 - Accuracy: 0.9275 - F1: 0.9274 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8643 | 1.0 | 250 | 0.3324 | 0.9065 | 0.9025 | | 0.2589 | 2.0 | 500 | 0.2237 | 0.9275 | 0.9274 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

0ababfcc65072d5754c0d6703a358af0

AigizK/bashkir-whisper-small

AigizK

whisper

17

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ba']

['mozilla-foundation/common_voice_11_0']

null

0

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

true

2,192

false

# Whisper Small Bashkir This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ba dataset. It achieves the following results on the evaluation set: - Loss: 0.2589 - Wer: 15.0723 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 30000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1637 | 1.01 | 2000 | 0.2555 | 26.4682 | | 0.1375 | 2.01 | 4000 | 0.2223 | 21.5394 | | 0.0851 | 3.02 | 6000 | 0.2086 | 19.6725 | | 0.0573 | 4.02 | 8000 | 0.2178 | 18.4280 | | 0.036 | 5.03 | 10000 | 0.2312 | 17.8248 | | 0.0238 | 6.04 | 12000 | 0.2621 | 17.4096 | | 0.0733 | 7.04 | 14000 | 0.2120 | 16.5656 | | 0.0111 | 8.05 | 16000 | 0.2682 | 16.2291 | | 0.0155 | 9.05 | 18000 | 0.2677 | 15.9242 | | 0.0041 | 10.06 | 20000 | 0.3178 | 15.9534 | | 0.0023 | 12.01 | 22000 | 0.3218 | 16.0536 | | 0.0621 | 13.01 | 24000 | 0.2313 | 15.6169 | | 0.0022 | 14.02 | 26000 | 0.2887 | 15.1083 | | 0.0199 | 15.02 | 28000 | 0.2553 | 15.1848 | | 0.0083 | 16.03 | 30000 | 0.2589 | 15.0723 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

8196e986536d270a92bdf26f2605da1b

alphahg/kobart-base-v2-finetuned-paper

alphahg

bart

9

4

transformers

0

text2text-generation

true

false

mit

null

['aihub_paper_summarization']

null

0

['generated_from_trainer']

true

1,658

false

# kobart-base-v2-finetuned-paper This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the aihub_paper_summarization dataset. It achieves the following results on the evaluation set: - Loss: 1.2966 - Rouge1: 6.2883 - Rouge2: 1.7038 - Rougel: 6.2556 - Rougelsum: 6.2618 - Gen Len: 20.0 ## 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: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.2215 | 1.0 | 8831 | 1.3293 | 6.2425 | 1.7317 | 6.2246 | 6.2247 | 20.0 | | 1.122 | 2.0 | 17662 | 1.3056 | 6.2298 | 1.7005 | 6.2042 | 6.2109 | 20.0 | | 1.0914 | 3.0 | 26493 | 1.2966 | 6.2883 | 1.7038 | 6.2556 | 6.2618 | 20.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2

d3f6c445a5026590d473f4ac581cbdd6

yanaiela/roberta-base-epoch_33

yanaiela

roberta

9

2

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_33']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 33 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_33. ## Model Description This model was captured during a reproduction of [RoBERTa-base](https://huggingface.co/roberta-base), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM). The intended uses, limitations, training data and training procedure for the fully trained model are similar to [RoBERTa-base](https://huggingface.co/roberta-base). Two major differences with the original model: * We trained our model for 100K steps, instead of 500K * We only use Wikipedia and the Book Corpus, as corpora which are publicly available. ### How to use Using code from [RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on PyTorch: ``` from transformers import pipeline model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10) model("Hello, I'm the <mask> RoBERTa-base language model") ``` ## Citation info ```bibtex @article{2207.14251, Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg}, Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions}, Year = {2022}, Eprint = {arXiv:2207.14251}, } ```

16b3c32902ad44b66297ebf781ae4216

henryscheible/eval_v3_mrpc

henryscheible

bert

12

1

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,136

false

# eval_v3_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6564 - eval_accuracy: 0.6649 - eval_f1: 0.7987 - eval_combined_score: 0.7318 - eval_runtime: 5.045 - eval_samples_per_second: 341.921 - eval_steps_per_second: 42.815 - step: 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: 32 - 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 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

e211e08689ba1202d6e058902648fa89

nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF

nestoralvaro

mt5

8

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,676

false

# nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3123 - Validation Loss: 2.1399 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 266360, '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, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.2631 | 2.3702 | 0 | | 2.6166 | 2.2422 | 1 | | 2.4974 | 2.2074 | 2 | | 2.4288 | 2.1843 | 3 | | 2.3837 | 2.1613 | 4 | | 2.3503 | 2.1521 | 5 | | 2.3263 | 2.1407 | 6 | | 2.3123 | 2.1399 | 7 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

6265d0c051614fde9367c8937150f2c9

glasses/resnet50

glasses

null

4

24

transformers

0

image-classification

true

false

apache-2.0

null

['imagenet']

null

0

['image-classification']

false

true

1,588

false

# resnet50 Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_ ResNet.resnet26d() ResNet.resnet34d() ResNet.resnet50d() # You can construct your own one by chaning `stem` and `block` resnet101d = ResNet.resnet101(stem=ResNetStemC, block=partial(ResNetBottleneckBlock, shortcut=ResNetShorcutD)) ``` Examples: ``` python # change activation ResNet.resnet18(activation = nn.SELU) # change number of classes (default is 1000 ) ResNet.resnet18(n_classes=100) # pass a different block ResNet.resnet18(block=SENetBasicBlock) # change the steam model = ResNet.resnet18(stem=ResNetStemC) change shortcut model = ResNet.resnet18(block=partial(ResNetBasicBlock, shortcut=ResNetShorcutD)) # store each feature x = torch.rand((1, 3, 224, 224)) # get features model = ResNet.resnet18() # first call .features, this will activate the forward hooks and tells the model you'll like to get the features model.encoder.features model(torch.randn((1,3,224,224))) # get the features from the encoder features = model.encoder.features print([x.shape for x in features]) #[torch.Size([1, 64, 112, 112]), torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14])] ```

899632756736775a1c4a8a2868c6ba03

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

Kushala

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: 0.5195 - Wer: 0.3386 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5345 | 1.0 | 500 | 2.1466 | 1.0010 | | 0.949 | 2.01 | 1000 | 0.5687 | 0.5492 | | 0.445 | 3.01 | 1500 | 0.4562 | 0.4717 | | 0.2998 | 4.02 | 2000 | 0.4154 | 0.4401 | | 0.2242 | 5.02 | 2500 | 0.3887 | 0.4034 | | 0.1834 | 6.02 | 3000 | 0.4262 | 0.3905 | | 0.1573 | 7.03 | 3500 | 0.4200 | 0.3927 | | 0.1431 | 8.03 | 4000 | 0.4194 | 0.3869 | | 0.1205 | 9.04 | 4500 | 0.4600 | 0.3912 | | 0.1082 | 10.04 | 5000 | 0.4613 | 0.3776 | | 0.0984 | 11.04 | 5500 | 0.4926 | 0.3860 | | 0.0872 | 12.05 | 6000 | 0.4869 | 0.3780 | | 0.0826 | 13.05 | 6500 | 0.5033 | 0.3690 | | 0.0717 | 14.06 | 7000 | 0.4827 | 0.3791 | | 0.0658 | 15.06 | 7500 | 0.4816 | 0.3650 | | 0.0579 | 16.06 | 8000 | 0.5433 | 0.3689 | | 0.056 | 17.07 | 8500 | 0.5513 | 0.3672 | | 0.0579 | 18.07 | 9000 | 0.4813 | 0.3632 | | 0.0461 | 19.08 | 9500 | 0.4846 | 0.3501 | | 0.0431 | 20.08 | 10000 | 0.5449 | 0.3637 | | 0.043 | 21.08 | 10500 | 0.4906 | 0.3538 | | 0.0334 | 22.09 | 11000 | 0.5081 | 0.3477 | | 0.0322 | 23.09 | 11500 | 0.5184 | 0.3439 | | 0.0316 | 24.1 | 12000 | 0.5412 | 0.3450 | | 0.0262 | 25.1 | 12500 | 0.5113 | 0.3425 | | 0.0267 | 26.1 | 13000 | 0.4888 | 0.3414 | | 0.0258 | 27.11 | 13500 | 0.5071 | 0.3371 | | 0.0226 | 28.11 | 14000 | 0.5311 | 0.3380 | | 0.0233 | 29.12 | 14500 | 0.5195 | 0.3386 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

67bd55a3b96510b4f5fd13686a88e09e

paola-md/distilroberta-recipes

paola-md

roberta

6

2

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,701

false

# recipe-lr2e05-wd0.02-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2784 - Rmse: 0.5277 - Mse: 0.2784 - Mae: 0.4161 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2774 | 1.0 | 623 | 0.2749 | 0.5243 | 0.2749 | 0.4184 | | 0.2741 | 2.0 | 1246 | 0.2741 | 0.5235 | 0.2741 | 0.4173 | | 0.2724 | 3.0 | 1869 | 0.2855 | 0.5343 | 0.2855 | 0.4428 | | 0.2713 | 4.0 | 2492 | 0.2758 | 0.5252 | 0.2758 | 0.4013 | | 0.2695 | 5.0 | 3115 | 0.2777 | 0.5270 | 0.2777 | 0.4245 | | 0.2674 | 6.0 | 3738 | 0.2784 | 0.5277 | 0.2784 | 0.4161 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

d34066b33b2121d0049b943818be7b54

AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru

AlexKay

xlm-roberta

8

960

transformers

15

question-answering

true

false

apache-2.0

['en', 'ru', 'multilingual']

null

1

0

1

0

1

[]

false

true

416

false

# XLM-RoBERTa large model whole word masking finetuned on SQuAD Pretrained model using a masked language modeling (MLM) objective. Fine tuned on English and Russian QA datasets ## Used QA Datasets SQuAD + SberQuAD [SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read! ## Evaluation results The results obtained are the following (SberQUaD): ``` f1 = 84.3 exact_match = 65.3

f9fb8a63819149d6d67987762d471d7c

edmundhui/mental_health_trainer

edmundhui

bert

12

38

transformers

2

text-classification

true

false

apache-2.0

null

0

1

0

['generated_from_trainer']

true

1,005

false

# mental_health_trainer This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [reddit_mental_health_posts](https://huggingface.co/datasets/solomonk/reddit_mental_health_posts) ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

b8683b6aeabc47edb8f243f772426e08

JonatanGk/roberta-base-bne-finetuned-sqac

JonatanGk

roberta

13

8

transformers

1

question-answering

true

false

apache-2.0

null

['sqac']

null

0

['generated_from_trainer']

true

1,284

false

# roberta-base-bne-finetuned-sqac This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the sqac dataset. It achieves the following results on the evaluation set: - Loss: 1.2066 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.9924 | 1.0 | 1196 | 0.8670 | | 0.474 | 2.0 | 2392 | 0.8923 | | 0.1637 | 3.0 | 3588 | 1.2066 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

93cedac1a13bb61841d6bd658a767a88

Devarshi/Brain_Tumor_Class_swin

Devarshi

swin

11

17

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,674

false

# Brain_Tumor_Class_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0220 - Accuracy: 0.9936 - F1: 0.9936 - Recall: 0.9936 - Precision: 0.9936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.1248 | 1.0 | 220 | 0.0610 | 0.9767 | 0.9767 | 0.9767 | 0.9767 | | 0.0887 | 2.0 | 440 | 0.0300 | 0.9920 | 0.9920 | 0.9920 | 0.9920 | | 0.0449 | 3.0 | 660 | 0.0220 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

926c89940717ad6e35f604d56c2f2654

Salesforce/blip-image-captioning-large

Salesforce

blip

9

7,776

transformers

15

image-to-text

true

false

bsd-3-clause

null

2

1

0

3

1

2

['image-captioning']

false

true

5,407

false

# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). | ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

920b716dd70fdc899586fd3ef4f499b0

muhtasham/tiny-mlm-glue-stsb

muhtasham

bert

12

2

transformers

1

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,648

false

# tiny-mlm-glue-stsb This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7830 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 4.7548 | 0.7 | 500 | 3.9253 | | 4.4535 | 1.39 | 1000 | 3.9069 | | 4.364 | 2.09 | 1500 | 3.8392 | | 4.1534 | 2.78 | 2000 | 3.7830 | | 4.2317 | 3.48 | 2500 | 3.7450 | | 4.1233 | 4.17 | 3000 | 3.7755 | | 4.0383 | 4.87 | 3500 | 3.7060 | | 4.0459 | 5.56 | 4000 | 3.8708 | | 3.9321 | 6.26 | 4500 | 3.8573 | | 4.0206 | 6.95 | 5000 | 3.7830 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

034b7610927b65615ea15a95552b27c2

zp2222/ddpm-butterflies-128

zp2222

null

12

2

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,228

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/zp2222/ddpm-butterflies-128/tensorboard?#scalars)

84fe01746b37603ea7665231ae2a8734

Helsinki-NLP/opus-mt-srn-fr

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-srn-fr * source languages: srn * target languages: fr * OPUS readme: [srn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.srn.fr | 28.9 | 0.462 |

12b69b3f8923126faac9b0a9f21d6643

testimonial/wav2vec2-base-timit-demo-colab

testimonial

wav2vec2

12

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,641

false

# wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4688 - Wer: 0.3417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4156 | 4.0 | 500 | 1.2721 | 0.8882 | | 0.6145 | 8.0 | 1000 | 0.4712 | 0.4510 | | 0.229 | 12.0 | 1500 | 0.4459 | 0.3847 | | 0.1312 | 16.0 | 2000 | 0.4739 | 0.3786 | | 0.0897 | 20.0 | 2500 | 0.4483 | 0.3562 | | 0.0608 | 24.0 | 3000 | 0.4450 | 0.3502 | | 0.0456 | 28.0 | 3500 | 0.4688 | 0.3417 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

b7fdeb8a2af19dec9d60e379a6d4073c

sweaterr/xlm-roberta-base-finetuned-panx-de

sweaterr

xlm-roberta

12

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,319

false

# xlm-roberta-base-finetuned-panx-de 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: 0.1358 - F1: 0.8638 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 | | 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 | | 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

d5d78cd542c857a21d44691a07b303ee

Intel/distilbart-cnn-12-6-int8-dynamic

Intel

bart

9

17

transformers

0

text2text-generation

true

false

apache-2.0

null

['cnn_dailymail']

null

0

['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingDynamic']

false

true

1,614

false

# INT8 DistilBart finetuned on CNN DailyMail ### Post-training dynamic quantization This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6). Below linear modules (21/133) are fallbacked to fp32 for less than 1% relative accuracy loss: **'model.decoder.layers.2.fc2'**, **'model.encoder.layers.11.fc2'**, **'model.decoder.layers.1.fc2'**, **'model.decoder.layers.0.fc2'**, **'model.decoder.layers.4.fc1'**, **'model.decoder.layers.3.fc2'**, **'model.encoder.layers.8.fc2'**, **'model.decoder.layers.3.fc1'**, **'model.encoder.layers.11.fc1'**, **'model.encoder.layers.0.fc2'**, **'model.encoder.layers.3.fc1'**, **'model.encoder.layers.10.fc2'**, **'model.decoder.layers.5.fc1'**, **'model.encoder.layers.1.fc2'**, **'model.encoder.layers.3.fc2'**, **'lm_head'**, **'model.encoder.layers.7.fc2'**, **'model.decoder.layers.0.fc1'**, **'model.encoder.layers.4.fc1'**, **'model.encoder.layers.10.fc1'**, **'model.encoder.layers.6.fc1'** ### Evaluation result | |INT8|FP32| |---|:---:|:---:| | **Accuracy (eval-rougeLsum)** | 41.4707 | 41.8117 | | **Model size** |722M|1249M| ### Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained( 'Intel/distilbart-cnn-12-6-int8-dynamic', ) ```

6fbc7a2ce7b25ba38a2855412e3c6792

harmonai/unlocked-250k

harmonai

null

6

389

diffusers

0

null

false

mit

null

0

['audio-generation']

false

true

1,313

false

[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers. ## FP32 ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/unlocked-250k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ``` ## FP16 Faster at a small loss of quality ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/unlocked-250k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ```

672038403d447492b596796a72a83cd0

theodotus/stt_uk_squeezeformer_ctc_ml

theodotus

null

3

58

nemo

1

automatic-speech-recognition

false

bsd-3-clause

['uk']

['mozilla-foundation/common_voice_10_0', 'Yehor/voa-uk-transcriptions']

null

0

['automatic-speech-recognition']

true

404

false

# Squeezeformer-CTC ML (uk-UA) <style> img { display: inline; } </style> | [![Model architecture](https://img.shields.io/badge/Model_Arch-Squeezeformer--CTC-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-uk--UA-lightgrey#model-badge)](#datasets) |

ef2dd7b8c25c1879555bb002c63f4395

tftransformers/albert-xlarge-v1

tftransformers

null

6

3

null

0

null

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

[]

false

true

6,478

false

# ALBERT XLarge v1 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. 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 ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 768 hidden dimension - 12 attention heads - 11M parameters ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: In tf_transformers ```python from tf_transformers.models import AlbertModel from transformers import AlbertTokenizer tokenizer = AlbertTokenizer.from_pretrained('albert-xlarge-v1') model = AlbertModel.from_pretrained("albert-xlarge-v1") text = "Replace me by any text you'd like." inputs_tf = {} inputs = tokenizer(text, return_tensors='tf') inputs_tf["input_ids"] = inputs["input_ids"] inputs_tf["input_type_ids"] = inputs["token_type_ids"] inputs_tf["input_mask"] = inputs["attention_mask"] outputs_tf = model(inputs_tf) ``` ## Training data The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` ### Training The ALBERT procedure follows the BERT setup. 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. ## Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |----------------|----------|----------|----------|----------|----------|----------| |V2 | |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |V1 | |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language Representations}, journal = {CoRR}, volume = {abs/1909.11942}, year = {2019}, url = {http://arxiv.org/abs/1909.11942}, archivePrefix = {arXiv}, eprint = {1909.11942}, timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

76ddf7106319e47e049981978c97161a

LiYuan/amazon-review-sentiment-analysis

LiYuan

bert

36

59,896

transformers

3

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,340

false

# distilbert-base-uncased-finetuned-mnli-amazon-query-shopping This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment?text=I+like+you.+I+love+you) on an [Amazon US Customer Reviews Dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset). The code for the fine-tuning process can be found [here](https://github.com/vanderbilt-data-science/bigdata/blob/main/06-fine-tune-BERT-on-our-dataset.ipynb). This model is uncased: it does not make a difference between english and English. It achieves the following results on the evaluation set: - Loss: 0.5202942490577698 - Accuracy: 0.8 ## Model description This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks. We replaced its head with our customer reviews to fine-tune it on 17,280 rows of training set while validating it on 4,320 rows of dev set. Finally, we evaluated our model performance on a held-out test set: 2,400 rows. ## Intended uses & limitations Bert-base 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. This fine-tuned version of BERT-base is used to predict review rating star given the review. The limitations are this trained model is focusing on reviews and products on Amazon. If you apply this model to other domains, it may perform poorly. ## How to use You can use this model directly by downloading the trained weights and configurations like the below code snippet: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LiYuan/amazon-review-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("LiYuan/amazon-review-sentiment-analysis") ``` ## Training and evaluation data Download all the raw [dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset) from the Kaggle website. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.555400 | 1.0 | 1080 | 0.520294 | 0.800000 | | 0.424300 | 2.0 | 1080 | 0.549649 | 0.798380 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

b2a68e085fa7f02919c93024e05f2bbf

muhtasham/small-mlm-wikitext-target-rotten_tomatoes

muhtasham

bert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,583

false

# small-mlm-wikitext-target-rotten_tomatoes This model is a fine-tuned version of [muhtasham/small-mlm-wikitext](https://huggingface.co/muhtasham/small-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3909 - Accuracy: 0.8021 - F1: 0.8017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4528 | 1.87 | 500 | 0.4296 | 0.8030 | 0.8028 | | 0.2265 | 3.75 | 1000 | 0.5558 | 0.8096 | 0.8096 | | 0.1111 | 5.62 | 1500 | 0.9042 | 0.8039 | 0.8039 | | 0.0584 | 7.49 | 2000 | 1.1252 | 0.8058 | 0.8058 | | 0.0405 | 9.36 | 2500 | 1.3909 | 0.8021 | 0.8017 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

a6bf3a7d5c92137953f5c32066a8b16b

stevhliu/my_awesome_asr_mind_model

stevhliu

wav2vec2

90

76

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,406

false

# my_awesome_asr_mind_model 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: 6.8626 - Wer: 1.0299 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7266 | 499.8 | 1000 | 5.8888 | 0.9403 | | 0.166 | 999.8 | 2000 | 6.8626 | 1.0299 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

d0d407698c96d5e076f79826ea839c49

conan1024hao/cjkbert-small

conan1024hao

bert

10

4

transformers

2

fill-mask

true

false

cc-by-sa-4.0

['ja', 'zh', 'ko']

['wikipedia']

null

0

[]

false

true

984

false

### Model description - This model was trained on **ZH, JA, KO**'s Wikipedia (5 epochs). ### How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("conan1024hao/cjkbert-small") model = AutoModelForMaskedLM.from_pretrained("conan1024hao/cjkbert-small") ``` - Before you fine-tune downstream tasks, you don't need any text segmentation. - (Though you may obtain better results if you applied morphological analysis to the data before fine-tuning) ### Morphological analysis tools - ZH: For Chinese, we use [LTP](https://github.com/HIT-SCIR/ltp). - JA: For Japanese, we use [Juman++](https://github.com/ku-nlp/jumanpp). - KO: For Korean, we use [KoNLPy](https://github.com/konlpy/konlpy)(Kkma class). ### Tokenization - We use character-based tokenization with **whole-word-masking** strategy. ### Model size - vocab_size: 15015 - num_hidden_layers: 4 - hidden_size: 512 - num_attention_heads: 8 - param_num: 25M

a28ce9d1fb047daf9e9f3cdd6650ed74

mskolesnikov/ddpm-butterflies-128

mskolesnikov

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,234

false

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

ac748d615561a94dc689a0407fea6e75

rdruce/ddpm-flowers-128-2

rdruce

null

12

1

diffusers

0

null

false

apache-2.0

['en']

['huggan/cats']

null

0

[]

false

true

1,198

false

# ddpm-flowers-128-2 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/cats` 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/rdruce/ddpm-flowers-128-2/tensorboard?#scalars)

f2ee5ad9d4b500e3e0ac6a2efb5c4063

Khanh/xlm-roberta-base-finetuned-squad

Khanh

xlm-roberta

13

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,205

false

# xlm-roberta-base-finetuned-squad 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: 0.5539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.7665 | 1.0 | 2295 | 0.5231 | | 0.5236 | 2.0 | 4590 | 0.5539 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

2d8c8eae7e5dee7462750ca722c27967

KoichiYasuoka/roberta-base-english-upos

KoichiYasuoka

roberta

10

2,169

transformers

0

token-classification

true

false

cc-by-sa-4.0

['en']

['universal_dependencies']

null

0

['english', 'token-classification', 'pos', 'dependency-parsing']

false

true

859

false

# roberta-base-english-upos ## Model Description This is a RoBERTa model pre-trained with [UD_English](https://universaldependencies.org/en/) for POS-tagging and dependency-parsing, derived from [roberta-base](https://huggingface.co/roberta-base). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). ## How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-english-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-english-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/roberta-base-english-upos") ``` ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

4749a8ec01695b83e5b8dfe48b27b571