davanstrien/model_cards_with_readmes · Datasets at Hugging Face

sarahmiller137/distilbert-base-uncased-ft-m3-lc

sarahmiller137

distilbert

8

1

transformers

0

text-classification

true

false

cc

['en']

['MIMIC-III']

null

0

['text classification']

false

true

1,459

false

## Model information: This model is the [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts into two classes. Labels of [0,1] were assigned to radiology reports in MIMIC-III that were linked to an ICD9 diagnosis code for lung cancer = 1 and a random sample of reports which were not linked to any type of cancer diagnosis code at all = 0. ## Intended uses: This model is intended to be used to classify texts to identify the presence of lung cancer. The model will predict lables of [0,1]. ## Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before use - - [MIMIC-III](https://www.nature.com/articles/sdata201635.pdf) - [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ## How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-m3-lc") model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-m3-lc") ```

57d75d1b672ccd42b98de073a93e9c49

jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-2_female-8_s364

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

498

false

# exp_w2v2r_de_vp-100k_gender_male-2_female-8_s364 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d1c78de5a18fa342c65e4652359158ac

pfluo/k2fsa-zipformer-chinese-english-mixed

pfluo

null

25

0

null

1

null

false

apache-2.0

null

2

0

2

0

[]

false

true

3,425

false

## Chinese-English ASR model using k2-zipformer-streaming ### AIShell-1 and Wenetspeech testset results with modified-beam-search streaming decode using epoch-14.pt | decode_chunk_len | AIShell-1 | TEST_NET | TEST_MEETING | |------------------|-----------|----------|--------------| | 32 | 3.19 | 9.58 | 9.51 || | 64 | 3.04 | 8.97 | 8.83 || ### Training and decoding commands ``` nohup ./pruned_transducer_stateless7_streaming/train.py --world-size 8 --num-epochs 30 --start-epoch 1 --feedforward-dims "1024,1024,1536,1536,1024" --exp-dir pruned_transducer_stateless7_streaming/exp --max-duration 360 > pruned_transducer_stateless7_streaming/exp/nohup.zipformer & nohup ./pruned_transducer_stateless7_streaming/decode.py --epoch 6 --avg 1 --exp-dir ./pruned_transducer_stateless7_streaming/exp --max-duration 600 --decode-chunk-len 32 --decoding-method modified_beam_search --beam-size 4 > nohup.zipformer.deocode & ``` ### Model unit is char+bpe as `data/lang_char_bpe/tokens.txt` ### Tips some k2-fsa version and parameter is ``` {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'lo g_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.23.2', 'k2-build -type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'a74f59dba1863cd9386ba4d8815850421260eee7', 'k2-git-date': 'Fri Dec 2 08:32:22 2022', 'lhotse-version': '1.5.0.dev+gi t.8ce38fc.dirty', 'torch-version': '1.11.0+cu113', 'torch-cuda-available': True, 'torch-cuda-version': '11.3', 'python-version': '3.7', 'icefall-git-branch': 'master', 'icef all-git-sha1': '11b08db-dirty', 'icefall-git-date': 'Thu Jan 12 10:19:21 2023', 'icefall-path': '/opt/conda/lib/python3.7/site-packages', 'k2-path': '/opt/conda/lib/python3. 7/site-packages/k2/__init__.py', 'lhotse-path': '/opt/conda/lib/python3.7/site-packages/lhotse/__init__.py', 'hostname': 'xxx', 'IP add ress': 'x.x.x.x'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 30, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_trans ducer_stateless7_streaming/exp'), 'lang_dir': 'data/lang_char_bpe', 'base_lr': 0.01, 'lr_batches': 5000, 'lr_epochs': 3.5, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0 .25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 2000, 'keep_last_k': 30, 'average_period': 200, ' use_fp16': False, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,1536,1536,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_ dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder _dim': 512, 'joiner_dim': 512, 'short_chunk_size': 50, 'num_left_chunks': 4, 'decode_chunk_len': 32, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 360, 'bucketing _sampler': True, 'num_buckets': 300, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_wor kers': 8, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'training_subset': '12k_hour', 'blank_id': 0, 'vocab_size': 6254} ```

fa5e48f9551d52ca2422b614da4070a6

hugogolastico/finetuning-sentiment-model-3000-samples

hugogolastico

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,040

false

# finetuning-sentiment-model-3000-samples 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: 0.3185 - Accuracy: 0.8667 - F1: 0.8675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2

e56c2df2a6a05469de0931dea9931408

sd-concepts-library/guttestreker

sd-concepts-library

null

18

0

null

8

null

false

mit

null

0

[]

false

true

2,062

false

### guttestreker on Stable Diffusion This is the `<guttestreker>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<guttestreker> 0](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/9.jpeg) ![<guttestreker> 1](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/10.jpeg) ![<guttestreker> 2](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/3.jpeg) ![<guttestreker> 3](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/1.jpeg) ![<guttestreker> 4](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/4.jpeg) ![<guttestreker> 5](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/8.jpeg) ![<guttestreker> 6](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/11.jpeg) ![<guttestreker> 7](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/6.jpeg) ![<guttestreker> 8](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/5.jpeg) ![<guttestreker> 9](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/0.jpeg) ![<guttestreker> 10](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/2.jpeg) ![<guttestreker> 11](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/7.jpeg) ![<guttestreker> 12](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/12.jpeg)

d23ed7998bf843adf276e43e8dc76686

MaryaAI/opus-mt-ar-en-finetunedTanzil-v5-ar-to-en

MaryaAI

marian

9

5

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,907

false

# opus-mt-ar-en-finetunedTanzil-v5-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8101 - Validation Loss: 0.9477 - Train Bleu: 9.3241 - Train Gen Len: 88.73 - Train Rouge1: 56.4906 - Train Rouge2: 34.2668 - Train Rougel: 53.2279 - Train Rougelsum: 53.7836 - Epoch: 2 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Bleu | Train Gen Len | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Epoch | |:----------:|:---------------:|:----------:|:-------------:|:------------:|:------------:|:------------:|:---------------:|:-----:| | 0.8735 | 0.9809 | 11.0863 | 78.68 | 56.4557 | 33.3673 | 53.4828 | 54.1197 | 0 | | 0.8408 | 0.9647 | 9.8543 | 88.955 | 57.3797 | 34.3539 | 53.8783 | 54.3714 | 1 | | 0.8101 | 0.9477 | 9.3241 | 88.73 | 56.4906 | 34.2668 | 53.2279 | 53.7836 | 2 | ### Framework versions - Transformers 4.17.0.dev0 - TensorFlow 2.7.0 - Datasets 1.18.4.dev0 - Tokenizers 0.10.3

33f7f642d95f93003020bd8ab0888dac

kingabzpro/wav2vec2-large-xls-r-1b-Indonesian

kingabzpro

wav2vec2

11

7

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['id']

['mozilla-foundation/common_voice_8_0']

null

0

['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event']

true

1,576

false

# wav2vec2-large-xls-r-1b-Indonesian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9550 - Wer: 0.4551 - Cer: 0.1643 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.663 | 7.69 | 200 | 0.7898 | 0.6039 | 0.1848 | | 0.7424 | 15.38 | 400 | 1.0215 | 0.5615 | 0.1924 | | 0.4494 | 23.08 | 600 | 1.0901 | 0.5249 | 0.1932 | | 0.5075 | 30.77 | 800 | 1.1013 | 0.5079 | 0.1935 | | 0.4671 | 38.46 | 1000 | 1.1034 | 0.4916 | 0.1827 | | 0.1928 | 46.15 | 1200 | 0.9550 | 0.4551 | 0.1643 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

be9baf1ef1e20b45880f9c5ba79775f9

helloway/lenet

helloway

null

5

22

mindspore

0

image-classification

false

apache-2.0

null

['mnist']

null

0

['image-classification']

false

true

888

false

## MindSpore Image Classification models with MNIST on the 🤗Hub! This repository contains the model from [this notebook on image classification with MNIST dataset using LeNet architecture](https://gitee.com/mindspore/mindspore/blob/r1.2/model_zoo/official/cv/lenet/README.md#). ## LeNet Description Lenet-5 is one of the earliest pre-trained models proposed by Yann LeCun and others in the year 1998, in the research paper Gradient-Based Learning Applied to Document Recognition. They used this architecture for recognizing the handwritten and machine-printed characters. The main reason behind the popularity of this model was its simple and straightforward architecture. It is a multi-layer convolution neural network for image classification. ![LeNet Architecture](./lenetarchitecture.jpeg) [source](https://www.analyticsvidhya.com/blog/2021/03/the-architecture-of-lenet-5/)

e1c796dc1c1d4c96216577041344ff70

elopezlopez/Bio_ClinicalBERT_fold_6_ternary_v1

elopezlopez

bert

13

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,668

false

# Bio_ClinicalBERT_fold_6_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7302 - F1: 0.8128 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 292 | 0.5359 | 0.7833 | | 0.5585 | 2.0 | 584 | 0.5376 | 0.8026 | | 0.5585 | 3.0 | 876 | 0.6117 | 0.8038 | | 0.2314 | 4.0 | 1168 | 0.8036 | 0.7974 | | 0.2314 | 5.0 | 1460 | 0.9467 | 0.8179 | | 0.1093 | 6.0 | 1752 | 1.2957 | 0.7923 | | 0.0384 | 7.0 | 2044 | 1.3423 | 0.8026 | | 0.0384 | 8.0 | 2336 | 1.2644 | 0.8218 | | 0.021 | 9.0 | 2628 | 1.3093 | 0.8231 | | 0.021 | 10.0 | 2920 | 1.3282 | 0.8179 | | 0.0129 | 11.0 | 3212 | 1.3853 | 0.8295 | | 0.0078 | 12.0 | 3504 | 1.4705 | 0.8154 | | 0.0078 | 13.0 | 3796 | 1.5063 | 0.8167 | | 0.0064 | 14.0 | 4088 | 1.5293 | 0.8179 | | 0.0064 | 15.0 | 4380 | 1.6303 | 0.8128 | | 0.0085 | 16.0 | 4672 | 1.5945 | 0.8115 | | 0.0085 | 17.0 | 4964 | 1.6899 | 0.8103 | | 0.0056 | 18.0 | 5256 | 1.6952 | 0.8064 | | 0.0055 | 19.0 | 5548 | 1.7550 | 0.7936 | | 0.0055 | 20.0 | 5840 | 1.6779 | 0.8141 | | 0.003 | 21.0 | 6132 | 1.7064 | 0.8128 | | 0.003 | 22.0 | 6424 | 1.7192 | 0.8154 | | 0.0013 | 23.0 | 6716 | 1.8188 | 0.7974 | | 0.0014 | 24.0 | 7008 | 1.7273 | 0.8128 | | 0.0014 | 25.0 | 7300 | 1.7302 | 0.8128 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

dd6bff728f506563faa19306a8719591

weijiahaha/t5-small-summarization

weijiahaha

t5

28

0

transformers

0

text2text-generation

true

false

apache-2.0

null

['cnn_dailymail']

null

0

['generated_from_trainer']

true

796

false

# t5-small-summarization This model is a fine-tuned version of t5-small (https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6477 ## Model description The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9195 | 1.0 | 718 | 1.6477 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

0289993ea0c0311b4367e58b503c83f5

anas-awadalla/bart-base-few-shot-k-128-finetuned-squad-seq2seq-seed-0

anas-awadalla

bart

23

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

963

false

# bart-base-few-shot-k-128-finetuned-squad-seq2seq-seed-0 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

86d6373a9103891a7d9b72b71cbf1bfb

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

anas-awadalla

bert

16

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

998

false

# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

ba3391316ba44d4532f8249e820c207e

fathyshalab/massive_general-roberta-large-v1-5-95

fathyshalab

roberta

14

22

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,464

false

# fathyshalab/massive_general-roberta-large-v1-5-95 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_general-roberta-large-v1-5-95") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

1acf2c8135ba6251bef14d014b8aea7a

redfoo/stable-diffusion-2-inpainting-endpoint-foo

redfoo

null

21

4

diffusers

0

null

false

openrail++

null

0

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

false

true

2,570

false

# Fork of [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) > Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. > For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). For more information about the model, license and limitations check the original model card at [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting). --- This repository implements a custom `handler` task for `text-guided-to-image-inpainting` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [handler.py](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py` ![thubmnail](Stable%20Diffusion%20Inference%20endpoints%20-%20inpainting.png) ### expected Request payload ```json { "inputs": "A prompt used for image generation", "image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC", "mask_image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC", } ``` below is an example on how to run a request using Python and `requests`. ## Run Request ```python import json from typing import List import requests as r import base64 from PIL import Image from io import BytesIO ENDPOINT_URL = "" HF_TOKEN = "" # helper image utils def encode_image(image_path): with open(image_path, "rb") as i: b64 = base64.b64encode(i.read()) return b64.decode("utf-8") def predict(prompt, image, mask_image): image = encode_image(image) mask_image = encode_image(mask_image) # prepare sample payload request = {"inputs": prompt, "image": image, "mask_image": mask_image} # headers headers = { "Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json", "Accept": "image/png" # important to get an image back } response = r.post(ENDPOINT_URL, headers=headers, json=payload) img = Image.open(BytesIO(response.content)) return img prediction = predict( prompt="Face of a bengal cat, high resolution, sitting on a park bench", image="dog.png", mask_image="mask_dog.png" ) ``` expected output ![sample](result.png)

b3ff7a97f01a88587144d8f2d271b7ff

buruzaemon/test-minilm-finetuned-emotion

buruzaemon

bert

13

9

transformers

0

text-classification

true

false

bsd-3-clause

['en']

['SetFit/emotion']

null

0

['microsoft/MiniLM-L12-H384-uncased']

false

true

603

false

# test-minilm-finetuned-emotion fine-tuned model (uncased) This model is a fine-tuned extension of the [Microsoft MiniLM distilled model](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased). This is the result of the learning exercise for [Simple Training with the 🤗 Transformers Trainer](https://www.youtube.com/watch?v=u--UVvH-LIQ&t=198s) and also going through Chapter 2, Text Classification in [Natural Language Processing with Transformers](https://transformersbook.com/), Revised Color Edition, May 2022. This model is uncased: it does not make a difference between english and English.

b188bd8502ec108a1b1af9c2e5f6bf2b

sd-concepts-library/anime-background-style-v2

sd-concepts-library

null

19

0

null

12

null

false

mit

null

0

[]

false

true

3,049

false

### Anime Background style (v2) on Stable Diffusion This is the `<anime-background-style-v2>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<anime-background-style-v2> 0](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/5.jpeg) ![<anime-background-style-v2> 1](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/13.jpeg) ![<anime-background-style-v2> 2](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/9.jpeg) ![<anime-background-style-v2> 3](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/10.jpeg) ![<anime-background-style-v2> 4](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/6.jpeg) ![<anime-background-style-v2> 5](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/4.jpeg) ![<anime-background-style-v2> 6](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/1.jpeg) ![<anime-background-style-v2> 7](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/3.jpeg) ![<anime-background-style-v2> 8](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/12.jpeg) ![<anime-background-style-v2> 9](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/2.jpeg) ![<anime-background-style-v2> 10](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/0.jpeg) ![<anime-background-style-v2> 11](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/7.jpeg) ![<anime-background-style-v2> 12](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/8.jpeg) ![<anime-background-style-v2> 13](https://huggingface.co/sd-concepts-library/anime-background-style-v2/resolve/main/concept_images/11.jpeg) Here are images generated with this style: ![the facade of a café in the style of <anime-background-style-v2>](https://i.imgur.com/EE89tm9.png) ![painting of a lush jungle in the style of <anime-background-style-v2>](https://i.imgur.com/peoQF5n.png) ![urban street with brownstones in the style of <anime-background-style-v2>](https://i.imgur.com/zuFgFP9.png) ![wide angle image of a castle made of ice in the style of <anime-background-style-v2>](https://i.imgur.com/uyopxyv.png)

cc8b7190cd8440412cc4d911ddef42f5

Helsinki-NLP/opus-mt-eo-ru

Helsinki-NLP

marian

11

250

transformers

0

translation

true

false

apache-2.0

['eo', 'ru']

null

1

0

['translation']

false

true

1,999

false

### epo-rus * source group: Esperanto * target group: Russian * OPUS readme: [epo-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-rus/README.md) * model: transformer-align * source language(s): epo * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.epo.rus | 17.7 | 0.379 | ### System Info: - hf_name: epo-rus - source_languages: epo - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'ru'] - src_constituents: {'epo'} - tgt_constituents: {'rus'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-rus/opus-2020-06-16.test.txt - src_alpha3: epo - tgt_alpha3: rus - short_pair: eo-ru - chrF2_score: 0.379 - bleu: 17.7 - brevity_penalty: 0.9179999999999999 - ref_len: 71288.0 - src_name: Esperanto - tgt_name: Russian - train_date: 2020-06-16 - src_alpha2: eo - tgt_alpha2: ru - prefer_old: False - long_pair: epo-rus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

47e290ba1960ea72a83629964fe041c6

jakub014/bert-base-uncased-finetuned-convincingness-IBM

jakub014

bert

15

16

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,473

false

# bert-base-uncased-finetuned-convincingness-IBM This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6537 - Accuracy: 0.7511 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 270 | 0.5707 | 0.7337 | | 0.4673 | 2.0 | 540 | 0.6059 | 0.7221 | | 0.4673 | 3.0 | 810 | 0.6537 | 0.7511 | | 0.2218 | 4.0 | 1080 | 0.8485 | 0.7467 | | 0.2218 | 5.0 | 1350 | 0.9221 | 0.7438 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

ba7a0355039717b48dd4f425ac222129

responsibility-framing/predict-perception-bert-cause-human

responsibility-framing

bert

12

19

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

9,145

false

# predict-perception-bert-cause-human This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7139 - Rmse: 1.2259 - Rmse Cause::a Causata da un essere umano: 1.2259 - Mae: 1.0480 - Mae Cause::a Causata da un essere umano: 1.0480 - R2: 0.4563 - R2 Cause::a Causata da un essere umano: 0.4563 - Cos: 0.4783 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.3953 - Rsa: nan ## 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: 20 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un essere umano | Mae | Mae Cause::a Causata da un essere umano | R2 | R2 Cause::a Causata da un essere umano | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:------:|:--------------------------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0874 | 1.0 | 15 | 1.2615 | 1.6296 | 1.6296 | 1.3836 | 1.3836 | 0.0393 | 0.0393 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan | | 0.9577 | 2.0 | 30 | 1.1988 | 1.5886 | 1.5886 | 1.3017 | 1.3017 | 0.0870 | 0.0870 | 0.4783 | 0.0 | 0.5 | 0.3944 | nan | | 0.8414 | 3.0 | 45 | 0.9870 | 1.4414 | 1.4414 | 1.1963 | 1.1963 | 0.2483 | 0.2483 | 0.3913 | 0.0 | 0.5 | 0.3048 | nan | | 0.7291 | 4.0 | 60 | 0.9098 | 1.3839 | 1.3839 | 1.1297 | 1.1297 | 0.3071 | 0.3071 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.5949 | 5.0 | 75 | 0.9207 | 1.3921 | 1.3921 | 1.2079 | 1.2079 | 0.2988 | 0.2988 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.4938 | 6.0 | 90 | 0.8591 | 1.3448 | 1.3448 | 1.1842 | 1.1842 | 0.3458 | 0.3458 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.3611 | 7.0 | 105 | 0.8176 | 1.3119 | 1.3119 | 1.1454 | 1.1454 | 0.3774 | 0.3774 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.2663 | 8.0 | 120 | 0.6879 | 1.2034 | 1.2034 | 1.0300 | 1.0300 | 0.4761 | 0.4761 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.1833 | 9.0 | 135 | 0.7704 | 1.2735 | 1.2735 | 1.1031 | 1.1031 | 0.4133 | 0.4133 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.1704 | 10.0 | 150 | 0.7097 | 1.2222 | 1.2222 | 1.0382 | 1.0382 | 0.4596 | 0.4596 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.1219 | 11.0 | 165 | 0.6872 | 1.2027 | 1.2027 | 1.0198 | 1.0198 | 0.4767 | 0.4767 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.1011 | 12.0 | 180 | 0.7201 | 1.2312 | 1.2312 | 1.0466 | 1.0466 | 0.4516 | 0.4516 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.0849 | 13.0 | 195 | 0.7267 | 1.2368 | 1.2368 | 1.0454 | 1.0454 | 0.4466 | 0.4466 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0818 | 14.0 | 210 | 0.7361 | 1.2448 | 1.2448 | 1.0565 | 1.0565 | 0.4394 | 0.4394 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0634 | 15.0 | 225 | 0.7158 | 1.2275 | 1.2275 | 1.0384 | 1.0384 | 0.4549 | 0.4549 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.065 | 16.0 | 240 | 0.7394 | 1.2475 | 1.2475 | 1.0659 | 1.0659 | 0.4369 | 0.4369 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0541 | 17.0 | 255 | 0.7642 | 1.2683 | 1.2683 | 1.0496 | 1.0496 | 0.4181 | 0.4181 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0577 | 18.0 | 270 | 0.7137 | 1.2257 | 1.2257 | 1.0303 | 1.0303 | 0.4565 | 0.4565 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0474 | 19.0 | 285 | 0.7393 | 1.2475 | 1.2475 | 1.0447 | 1.0447 | 0.4370 | 0.4370 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0494 | 20.0 | 300 | 0.7157 | 1.2274 | 1.2274 | 1.0453 | 1.0453 | 0.4550 | 0.4550 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0434 | 21.0 | 315 | 0.7248 | 1.2352 | 1.2352 | 1.0462 | 1.0462 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.049 | 22.0 | 330 | 0.7384 | 1.2467 | 1.2467 | 1.0613 | 1.0613 | 0.4377 | 0.4377 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0405 | 23.0 | 345 | 0.7420 | 1.2498 | 1.2498 | 1.0653 | 1.0653 | 0.4349 | 0.4349 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0398 | 24.0 | 360 | 0.7355 | 1.2442 | 1.2442 | 1.0620 | 1.0620 | 0.4399 | 0.4399 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0398 | 25.0 | 375 | 0.7570 | 1.2623 | 1.2623 | 1.0698 | 1.0698 | 0.4235 | 0.4235 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan | | 0.0345 | 26.0 | 390 | 0.7359 | 1.2446 | 1.2446 | 1.0610 | 1.0610 | 0.4396 | 0.4396 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan | | 0.0345 | 27.0 | 405 | 0.7417 | 1.2495 | 1.2495 | 1.0660 | 1.0660 | 0.4352 | 0.4352 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | | 0.0386 | 28.0 | 420 | 0.7215 | 1.2323 | 1.2323 | 1.0514 | 1.0514 | 0.4506 | 0.4506 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan | | 0.0372 | 29.0 | 435 | 0.7140 | 1.2260 | 1.2260 | 1.0477 | 1.0477 | 0.4562 | 0.4562 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan | | 0.0407 | 30.0 | 450 | 0.7139 | 1.2259 | 1.2259 | 1.0480 | 1.0480 | 0.4563 | 0.4563 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

aecf8c99c0779d8a73cb46a501d08085

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256

gokuls

distilbert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,313

false

# distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256 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: 0.5199 - Accuracy: 0.3284 - F1: 0.0616 - Combined Score: 0.1950 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5375 | 1.0 | 15 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5305 | 2.0 | 30 | 0.5292 | 0.3162 | 0.0 | 0.1581 | | 0.5294 | 3.0 | 45 | 0.5293 | 0.3162 | 0.0 | 0.1581 | | 0.5283 | 4.0 | 60 | 0.5284 | 0.3162 | 0.0 | 0.1581 | | 0.5258 | 5.0 | 75 | 0.5260 | 0.3162 | 0.0 | 0.1581 | | 0.519 | 6.0 | 90 | 0.5199 | 0.3284 | 0.0616 | 0.1950 | | 0.5036 | 7.0 | 105 | 0.5200 | 0.3848 | 0.2462 | 0.3155 | | 0.4916 | 8.0 | 120 | 0.5226 | 0.4167 | 0.3239 | 0.3703 | | 0.4725 | 9.0 | 135 | 0.5298 | 0.4289 | 0.3581 | 0.3935 | | 0.4537 | 10.0 | 150 | 0.5333 | 0.6152 | 0.6736 | 0.6444 | | 0.4382 | 11.0 | 165 | 0.5450 | 0.6201 | 0.6906 | 0.6554 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

422cbb5fae0fbf277c11f2e38ceb9ef8

jonatasgrosman/exp_w2v2t_fa_vp-es_s533

jonatasgrosman

wav2vec2

10

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fa']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_fa_vp-es_s533 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](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.

adb8ede84b89eadc3a25d457dbe4e05f

distilbert-base-uncased-distilled-squad

null

distilbert

14

27,917

transformers

20

question-answering

true

false

apache-2.0

['en']

['squad']

null

3

0

3

0

[]

false

true

8,586

false

# DistilBERT base uncased distilled SQuAD ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than *bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark. This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned using (a second step of) knowledge distillation on [SQuAD v1.1](https://huggingface.co/datasets/squad). - **Developed by:** Hugging Face - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Apache 2.0 - **Related Models:** [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased) - **Resources for more information:** - See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including this model) - See [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure ## How to Get Started with the Model Use the code below to get started with the model. ```python >>> from transformers import pipeline >>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad') >>> context = r""" ... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a ... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune ... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script. ... """ >>> result = question_answerer(question="What is a good example of a question answering dataset?", context=context) >>> print( ... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}" ...) Answer: 'SQuAD dataset', score: 0.4704, start: 147, end: 160 ``` Here is how to use this model in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering import torch tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad') model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" inputs = tokenizer(question, text, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) answer_start_index = torch.argmax(outputs.start_logits) answer_end_index = torch.argmax(outputs.end_logits) predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) ``` And in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering import tensorflow as tf tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-distilled-squad") model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased-distilled-squad") question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" inputs = tokenizer(question, text, return_tensors="tf") outputs = model(**inputs) answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0]) answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0]) predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) ``` ## Uses This model can be used for question answering. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline >>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad') >>> context = r""" ... Alice is sitting on the bench. Bob is sitting next to her. ... """ >>> result = question_answerer(question="Who is the CEO?", context=context) >>> print( ... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}" ...) Answer: 'Bob', score: 0.4183, start: 32, end: 35 ``` Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Training #### Training Data The [distilbert-base-uncased model](https://huggingface.co/distilbert-base-uncased) model describes it's training data as: > DistilBERT pretrained on the same data as BERT, which is [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). To learn more about the SQuAD v1.1 dataset, see the [SQuAD v1.1 data card](https://huggingface.co/datasets/squad). #### Training Procedure ##### Preprocessing See the [distilbert-base-uncased model card](https://huggingface.co/distilbert-base-uncased) for further details. ##### Pretraining See the [distilbert-base-uncased model card](https://huggingface.co/distilbert-base-uncased) for further details. ## Evaluation As discussed in the [model repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md) > This model reaches a F1 score of 86.9 on the [SQuAD v1.1] dev set (for comparison, Bert bert-base-uncased version reaches a F1 score of 88.5). ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1910.01108.pdf). Note that these details are just for training DistilBERT, not including the fine-tuning with SQuAD. - **Hardware Type:** 8 16GB V100 GPUs - **Hours used:** 90 hours - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/abs/1910.01108) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @inproceedings{sanh2019distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas}, booktitle={NeurIPS EMC^2 Workshop}, year={2019} } ``` APA: - Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. ## Model Card Authors This model card was written by the Hugging Face team.

4fbbfa7dc732fdd9378b108516909a2a

kompactss/JeBERT_je_ko

kompactss

encoder-decoder

7

1

transformers

0

text2text-generation

true

false

afl-3.0

null

0

[]

false

true

728

false

# 🍊 제주 방언 번역 모델 🍊 - 제주어 -> 표준어 - Made by. 구름 자연어처리 과정 3기 3조!! - github link : https://github.com/Goormnlpteam3/JeBERT ## 1. Seq2Seq Transformer Model - encoder : BertConfig - decoder : BertConfig - Tokenizer : WordPiece Tokenizer ## 2. Dataset - Jit Dataset - AI HUB(+아래아 문자) ## 3. Hyper Parameters - Epoch : 10 epochs(best at 8 epoch) - Random Seed : 42 - Learning Rate : 5e-5 - Warm up Ratio : 0.1 - Batch Size : 32 ## 4. BLEU Score - Jit + AI HUB(+아래아 문자) Dataset : 79.0 --- ### CREDIT - 주형준 : wngudwns2798@gmail.com - 강가람 : 1st9aram@gmail.com - 고광연 : rhfprl11@gmail.com - 김수연 : s01090445778@gmail.com - 이원경 : hjtwin2@gmail.com - 조성은 : eun102476@gmail.com

110d02c079ac9951ca1680e2e6d08038

jcmc/aw-gpt

jcmc

gpt2

17

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,087

false

# aw-gpt 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: 3.2452 ## 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 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 200 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu116 - Datasets 2.6.1 - Tokenizers 0.11.6

a136a4e6d36421225697d8b3e55790e1

jannatul17/squad-bn-qgen-mt5-all-metric

jannatul17

t5

16

3

transformers

0

text2text-generation

true

false

apache-2.0

null

['squad_bn']

null

0

['generated_from_trainer']

true

3,507

false

# squad-bn-qgen-mt5-all-metric This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the squad_bn dataset. It achieves the following results on the evaluation set: - Loss: 0.7273 - Rouge1 Precision: 35.8589 - Rouge1 Recall: 29.7041 - Rouge1 Fmeasure: 31.6373 - Rouge2 Precision: 15.4203 - Rouge2 Recall: 12.5155 - Rouge2 Fmeasure: 13.3978 - Rougel Precision: 34.4684 - Rougel Recall: 28.5887 - Rougel Fmeasure: 30.4627 - Rougelsum Precision: 34.4252 - Rougelsum Recall: 28.5362 - Rougelsum Fmeasure: 30.4053 - Sacrebleu: 6.4143 - Meteor: 0.1416 - Gen Len: 16.7199 ## 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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:| | 0.8449 | 1.0 | 16396 | 0.7340 | 31.6476 | 26.8901 | 28.2871 | 13.621 | 11.3545 | 11.958 | 30.3276 | 25.7754 | 27.1048 | 30.3426 | 25.7489 | 27.0991 | 5.9655 | 0.1336 | 16.8685 | | 0.7607 | 2.0 | 32792 | 0.7182 | 33.7173 | 28.6115 | 30.1049 | 14.8227 | 12.2059 | 12.9453 | 32.149 | 27.2036 | 28.6617 | 32.2479 | 27.2261 | 28.7272 | 6.6093 | 0.138 | 16.8522 | | 0.7422 | 3.0 | 49188 | 0.7083 | 34.6128 | 29.0223 | 30.7248 | 14.9888 | 12.3092 | 13.1021 | 33.2507 | 27.8154 | 29.4599 | 33.2848 | 27.812 | 29.5064 | 6.2407 | 0.1416 | 16.5806 | | 0.705 | 4.0 | 65584 | 0.7035 | 34.156 | 29.0012 | 30.546 | 14.72 | 12.0251 | 12.8161 | 32.7527 | 27.6511 | 29.1955 | 32.7692 | 27.6627 | 29.231 | 6.1784 | 0.1393 | 16.7793 | | 0.6859 | 5.0 | 81980 | 0.7038 | 35.1405 | 29.6033 | 31.2614 | 15.5108 | 12.6414 | 13.5059 | 33.8335 | 28.4264 | 30.0745 | 33.8782 | 28.4349 | 30.0901 | 6.5896 | 0.144 | 16.6651 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

7e536941c9934f7a3a666b77fa1d9d03

jonatasgrosman/exp_w2v2t_th_vp-nl_s253

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['th']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_th_vp-nl_s253 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

60edf672ab5b484ee92b9687e023cd20

Loc/lucky-model

Loc

vit

8

4

transformers

0

image-classification

true

apache-2.0

null

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

null

0

['vision', 'image-classification']

false

true

5,273

false

# Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#). ## Training data The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224. ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```

cd26cde0781997e95c1a82884123e308

okep/distilbert-base-uncased-finetuned-emotion

okep

distilbert

22

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,339

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: 0.2269 - Accuracy: 0.9245 - F1: 0.9245 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.853 | 1.0 | 250 | 0.3507 | 0.8925 | 0.8883 | | 0.2667 | 2.0 | 500 | 0.2269 | 0.9245 | 0.9245 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

9a3fb3f3a14e7293e02ac9de8a5d3381

SiddharthaM/hasoc19-bert-base-multilingual-cased-HatredStatement-new

SiddharthaM

bert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,860

false

# hasoc19-bert-base-multilingual-cased-HatredStatement-new This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6565 - Accuracy: 0.7319 - Precision: 0.7320 - Recall: 0.7319 - F1: 0.7307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5540 | 0.7110 | 0.7147 | 0.7110 | 0.7067 | | 0.5551 | 2.0 | 592 | 0.5345 | 0.7224 | 0.7673 | 0.7224 | 0.7038 | | 0.5551 | 3.0 | 888 | 0.5752 | 0.7272 | 0.7430 | 0.7272 | 0.7183 | | 0.4252 | 4.0 | 1184 | 0.5697 | 0.7376 | 0.7384 | 0.7376 | 0.7359 | | 0.4252 | 5.0 | 1480 | 0.6335 | 0.7319 | 0.7388 | 0.7319 | 0.7269 | | 0.3401 | 6.0 | 1776 | 0.6565 | 0.7319 | 0.7320 | 0.7319 | 0.7307 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

53dcc8376de924bf3e23234845c4a8dc

infinitejoy/wav2vec2-large-xls-r-300m-basaa-cv8

infinitejoy

wav2vec2

19

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['bas']

['mozilla-foundation/common_voice_8_0']

null

0

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

true

1,702

false

# wav2vec2-large-xls-r-300m-basaa-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset. It achieves the following results on the evaluation set: - Loss: 0.4648 - Wer: 0.5472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - 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: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9421 | 12.82 | 500 | 2.8894 | 1.0 | | 1.1872 | 25.64 | 1000 | 0.6688 | 0.7460 | | 0.8894 | 38.46 | 1500 | 0.4868 | 0.6516 | | 0.769 | 51.28 | 2000 | 0.4960 | 0.6507 | | 0.6936 | 64.1 | 2500 | 0.4781 | 0.5384 | | 0.624 | 76.92 | 3000 | 0.4643 | 0.5430 | | 0.5966 | 89.74 | 3500 | 0.4530 | 0.5591 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

9a6dad3b40cafc923e44ccf964047494

azizbarank/mbert-finnic-ner

azizbarank

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,578

false

# mbert-finnic-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the Finnish and Estonian parts of the "WikiANN" dataset. It achieves the following results on the evaluation set: - Loss: 0.1427 - Precision: 0.9090 - Recall: 0.9156 - F1: 0.9123 - Accuracy: 0.9672 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1636 | 1.0 | 2188 | 0.1385 | 0.8906 | 0.9000 | 0.8953 | 0.9601 | | 0.0991 | 2.0 | 4376 | 0.1346 | 0.9099 | 0.9095 | 0.9097 | 0.9660 | | 0.0596 | 3.0 | 6564 | 0.1427 | 0.9090 | 0.9156 | 0.9123 | 0.9672 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

05d62f41f4695fae6493d5a796f7f5fa

DOOGLAK/Article_50v0_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['article50v0_wikigold_split']

null

0

['generated_from_trainer']

true

1,557

false

# Article_50v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5912 - Precision: 0.0975 - Recall: 0.0183 - F1: 0.0308 - Accuracy: 0.7915 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 14 | 0.7204 | 0.0 | 0.0 | 0.0 | 0.7803 | | No log | 2.0 | 28 | 0.6230 | 0.0743 | 0.0081 | 0.0145 | 0.7869 | | No log | 3.0 | 42 | 0.5912 | 0.0975 | 0.0183 | 0.0308 | 0.7915 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

31ebe7e55f7b7fae71bdb0d6c1dff37f

cakiki/distilbert-base-uncased-finetuned-tweet-sentiment

cakiki

distilbert

9

14

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,746

false

# cakiki/distilbert-base-uncased-finetuned-tweet-sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1025 - Train Sparse Categorical Accuracy: 0.9511 - Validation Loss: 0.1455 - Validation Sparse Categorical Accuracy: 0.9365 - Epoch: 2 ## 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': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.5409 | 0.8158 | 0.2115 | 0.9265 | 0 | | 0.1442 | 0.9373 | 0.1411 | 0.9380 | 1 | | 0.1025 | 0.9511 | 0.1455 | 0.9365 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.9.0-rc0 - Datasets 2.1.0 - Tokenizers 0.12.1

9efe633a9994db7179576a2eeeb72825

muhtasham/bert-small-finetuned-ner-to-multilabel-wnut-17-new

muhtasham

bert

12

11

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,463

false

# bert-small-finetuned-ner-to-multilabel-wnut-17-new This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2039 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2006 | 1.18 | 500 | 0.2043 | | 0.1247 | 2.35 | 1000 | 0.1960 | | 0.0935 | 3.53 | 1500 | 0.1893 | | 0.0742 | 4.71 | 2000 | 0.2003 | | 0.0552 | 5.88 | 2500 | 0.2106 | | 0.0405 | 7.06 | 3000 | 0.2039 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

610cf4033f2664a0eef24aa6aeec233f

navteca/nli-deberta-v3-large

navteca

deberta-v2

9

33

transformers

2

zero-shot-classification

true

false

apache-2.0

['en']

['multi_nli', 'snli']

null

0

['microsoft/deberta-v3-large']

false

true

2,617

false

# Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) ## Training Data The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. ## Performance - Accuracy on SNLI-test dataset: 92.20 - Accuracy on MNLI mismatched set: 90.49 For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli). ## Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-deberta-v3-large') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) #Convert scores to labels label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] ``` ## Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large') tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large') features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] print(labels) ``` ## Zero-Shot Classification This model can also be used for zero-shot-classification: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", "politics"] res = classifier(sent, candidate_labels) print(res) ```

464d59d38dfbbc815381d52c62627433

yanaiela/roberta-base-epoch_45

yanaiela

roberta

9

2

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_45']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 45 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_45. ## 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}, } ```

ed4d67081d6aeb7a9b8cd78f79df86ba

jonatasgrosman/exp_w2v2r_es_xls-r_age_teens-2_sixties-8_s772

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

475

false

# exp_w2v2r_es_xls-r_age_teens-2_sixties-8_s772 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

1d1820382b3dff89ecbf4bd0a08a6dfd

jonatasgrosman/exp_w2v2t_pl_r-wav2vec2_s996

jonatasgrosman

wav2vec2

10

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['pl']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

462

false

# exp_w2v2t_pl_r-wav2vec2_s996 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pl)](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.

378607e3d4299d4f7468a4d4b0e2b99f

harshit345/xlsr-53-wav2vec-hi

harshit345

wav2vec2

8

10

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['Interspeech 2021']

null

0

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

true

3,432

false

# Wav2Vec2-Large-XLSR-53-hindi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). 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", "hi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the hindi 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", "hi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) 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"]) 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**:20.22 % ## Training The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1nY5WMj1oNlexD_qDeNYL7ZM427A021CV?usp=sharing)

773b66c192a4abe871eb4cee2f4a6408

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

FAICAM

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: 0.5725 - Wer: 0.3413 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.508 | 1.0 | 500 | 1.9315 | 0.9962 | | 0.8832 | 2.01 | 1000 | 0.5552 | 0.5191 | | 0.4381 | 3.01 | 1500 | 0.4451 | 0.4574 | | 0.2983 | 4.02 | 2000 | 0.4096 | 0.4265 | | 0.2232 | 5.02 | 2500 | 0.4280 | 0.4083 | | 0.1811 | 6.02 | 3000 | 0.4307 | 0.3942 | | 0.1548 | 7.03 | 3500 | 0.4453 | 0.3889 | | 0.1367 | 8.03 | 4000 | 0.5043 | 0.4138 | | 0.1238 | 9.04 | 4500 | 0.4530 | 0.3807 | | 0.1072 | 10.04 | 5000 | 0.4435 | 0.3660 | | 0.0978 | 11.04 | 5500 | 0.4739 | 0.3676 | | 0.0887 | 12.05 | 6000 | 0.5052 | 0.3761 | | 0.0813 | 13.05 | 6500 | 0.5098 | 0.3619 | | 0.0741 | 14.06 | 7000 | 0.4666 | 0.3602 | | 0.0654 | 15.06 | 7500 | 0.5642 | 0.3657 | | 0.0589 | 16.06 | 8000 | 0.5489 | 0.3638 | | 0.0559 | 17.07 | 8500 | 0.5260 | 0.3598 | | 0.0562 | 18.07 | 9000 | 0.5250 | 0.3640 | | 0.0448 | 19.08 | 9500 | 0.5215 | 0.3569 | | 0.0436 | 20.08 | 10000 | 0.5117 | 0.3560 | | 0.0412 | 21.08 | 10500 | 0.4910 | 0.3570 | | 0.0336 | 22.09 | 11000 | 0.5221 | 0.3524 | | 0.031 | 23.09 | 11500 | 0.5278 | 0.3480 | | 0.0339 | 24.1 | 12000 | 0.5353 | 0.3486 | | 0.0278 | 25.1 | 12500 | 0.5342 | 0.3462 | | 0.0251 | 26.1 | 13000 | 0.5399 | 0.3439 | | 0.0242 | 27.11 | 13500 | 0.5626 | 0.3431 | | 0.0214 | 28.11 | 14000 | 0.5749 | 0.3408 | | 0.0216 | 29.12 | 14500 | 0.5725 | 0.3413 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

28fc28ad60466f70cce042995edd5946

Manseo/Colorful-v4.5

Manseo

null

24

25

diffusers

1

text-to-image

false

creativeml-openrail-m

['en']

null

0

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

false

true

3,648

false

# **Colorful-v4.5** **Colorful-v4.5** is a model merge between [Anything-v4.5](https://huggingface.co/andite/anything-v4.0), [AbyssOrangeMix2](https://huggingface.co/WarriorMama777/OrangeMixs) and [ProtogenInfinity](https://huggingface.co/darkstorm2150/Protogen_Infinity_Official_Release) Colorful-v4.5 is named how it is because of the fact that it is similar to Anything-v4.5 and that it improves the bland color pallet it comes with (atleast for me), producing much livelier images. It also improves some other things like environments, fingers, facial emotions and somewhat clothing (it also fixes the purple blobs 🤫) *Technically i could name it Anything-v5.0 but that would be rather cheesy* *It is highly recommended to run this model locally on your computer because running it from the web-ui api will produce lower quality images than intended* # Examples: # **Example #1:** *10 steps* ![02040-774768794-masterpiece, best quality, 1girl, (green eyes), black hair, (black shorts), white shirt, glossy lips, small nose, standing up, i.png](https://s3.amazonaws.com/moonup/production/uploads/1676021469760-637dd5466b7ee62df4622eeb.png) ``` Prompt: masterpiece, best quality, 1girl, (green eyes), black hair, (black shorts), white shirt, glossy lips, small nose, standing up, in park, trees, full body, smiling Other Details: Steps: 10, Sampler: DPM++ 2S a Karras, CFG scale: 8, Seed: 774768794, Size: 512x512, Model hash: b5de490700, Model: Colorful-v4.5, Denoising strength: 0.6, Hires upscale: 2, Hires steps: 10, Hires upscaler: SwinIR_4x Negative Prompt: The negative prompt is very long and specific so it will be listed in the model's repo. ( The negative prompt comes from another model called Hentai Difussion so it will contain NSFW. A curated version of the negative prompt will also be in the repo for those who want SFW) ``` # **Example #2:** *20 steps* ![02041-774768794-masterpiece, best quality, lady, in red and black yukata, pink hair, blue eyes, in dojo, smiling, sitting, hands on lap.png](https://s3.amazonaws.com/moonup/production/uploads/1676021491678-637dd5466b7ee62df4622eeb.png) ``` Prompt: masterpiece, best quality, lady, in red and black yukata, pink hair, blue eyes, in dojo, smiling, sitting, hands on lap Other Details: Steps: 20, Sampler: DPM++ 2S a Karras, CFG scale: 8, Seed: 774768794, Size: 512x512, Model hash: b5de490700, Model: Colorful-v4.5, Denoising strength: 0.6, Hires upscale: 2, Hires steps: 20, Hires upscaler: SwinIR_4x Negative Prompt: Same thing as in Example #1 ``` # **Example #3:** *30 steps* ![02048-774768794-masterpiece, best quality, girl, black hair, blue eyes, black t-shirt, black pants, smiling, standing up, solo, facing viewer, n.png](https://s3.amazonaws.com/moonup/production/uploads/1676108642730-637dd5466b7ee62df4622eeb.png) # Anything-v4.5: (for comparison) ![02049-774768794-masterpiece, best quality, girl, black hair, blue eyes, black t-shirt, black pants, smiling, standing up, solo, facing viewer, n.png](https://s3.amazonaws.com/moonup/production/uploads/1676108845574-637dd5466b7ee62df4622eeb.png) ``` Prompt: masterpiece, best quality, girl, black hair, blue eyes, black t-shirt, black pants, smiling, standing up, solo, facing viewer, near blossomed tree Other Details: Steps: 30, Sampler: DPM++ 2S a Karras, CFG scale: 8, Seed: 774768794, Size: 512x512, Model hash: b5de490700, Model: Colorful-v4.5, Denoising strength: 0.6, Hires upscale: 2, Hires steps: 30, Hires upscaler: SwinIR_4x Negative Prompt: Same thing as in Example #1 ``` *Note: I didnt use any vae for the examples, but i did try the anything-v4.0 vae and it barely made a difference*

893f5ade5dbb46a67f00d770de4c82e6

YoussefSaad/dresses

YoussefSaad

vit

9

8

transformers

2

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,945

false

# dresses 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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4588 - Accuracy: 0.9014 ## 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: 64 - eval_batch_size: 8 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2458 | 1.23 | 100 | 0.4519 | 0.8633 | | 0.0937 | 2.47 | 200 | 0.4285 | 0.8754 | | 0.0802 | 3.7 | 300 | 0.4683 | 0.8754 | | 0.041 | 4.94 | 400 | 0.4088 | 0.9031 | | 0.0277 | 6.17 | 500 | 0.3979 | 0.8945 | | 0.0459 | 7.41 | 600 | 0.4253 | 0.9014 | | 0.024 | 8.64 | 700 | 0.4680 | 0.8893 | | 0.0267 | 9.88 | 800 | 0.4575 | 0.8945 | | 0.019 | 11.11 | 900 | 0.4470 | 0.8893 | | 0.0235 | 12.35 | 1000 | 0.4380 | 0.9066 | | 0.0129 | 13.58 | 1100 | 0.4557 | 0.9048 | | 0.0211 | 14.81 | 1200 | 0.4588 | 0.9014 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

6e5fe63a9c99e72a5a93f9ac23c47865

jonatasgrosman/exp_w2v2t_fa_r-wav2vec2_s129

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fa']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

462

false

# exp_w2v2t_fa_r-wav2vec2_s129 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (fa)](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.

8f4d505d44d4e705291f1d40f6306ca4

HuyenNguyen/FPT-P3-6000

HuyenNguyen

whisper

15

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,331

false

# FPT-P3-6000 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4654 - Wer: 24.2203 ## 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: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3748 | 1.14 | 500 | 0.5442 | 26.9412 | | 0.1952 | 2.28 | 1000 | 0.4654 | 24.2203 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

0cdea0221aa2b07f91a14a4c2cc84ff5

HDKCL/izamizam

HDKCL

null

64

55

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

608

false

### izamizam Dreambooth model trained by HDKCL 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:

30e8f00df716dc63b86b1072bebaf5ba

sd-dreambooth-library/bolsonaro

sd-dreambooth-library

null

3

0

null

1

null

false

mit

null

0

[]

false

true

419

false

### Bolsonaro on Stable Diffusion via Dreambooth #### model by rodrigocoelho' language: - pt-BR - en-US thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1665418455423-632486a8767557375b7078a6.png" tags: - Bolsonaro license: "Apache 2.0" datasets: - Bolsonaro - stable diffusion ![image 0](https://s3.amazonaws.com/moonup/production/uploads/1665418455423-632486a8767557375b7078a6.png)

efa69472ab1d83defc5d339b66833b23

w11wo/javanese-distilbert-small-imdb

w11wo

distilbert

8

9

transformers

0

fill-mask

true

false

mit

['jv']

['w11wo/imdb-javanese']

null

0

['javanese-distilbert-small-imdb']

false

true

3,093

false

## Javanese DistilBERT Small IMDB Javanese DistilBERT Small IMDB is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on Javanese IMDB movie reviews. The model was originally the pretrained [Javanese DistilBERT Small model](https://huggingface.co/w11wo/javanese-distilbert-small) and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 21.01 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger). Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless. ## Model | Model | #params | Arch. | Training/Validation data (text) | |----------------------------------|----------|----------------------|---------------------------------| | `javanese-distilbert-small-imdb` | 66M | DistilBERT Small | Javanese IMDB (47.5 MB of text) | ## Evaluation Results The model was trained for 5 epochs and the following is the final result once the training ended. | train loss | valid loss | perplexity | total time | |------------|------------|------------|-------------| | 3.126 | 3.039 | 21.01 | 5:6:4 | ## How to Use ### As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/javanese-distilbert-small-imdb" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Aku mangan sate ing [MASK] bareng konco-konco") ``` ### Feature Extraction in PyTorch ```python from transformers import DistilBertModel, DistilBertTokenizerFast pretrained_name = "w11wo/javanese-distilbert-small-imdb" model = DistilBertModel.from_pretrained(pretrained_name) tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name) prompt = "Indonesia minangka negara gedhe." encoded_input = tokenizer(prompt, return_tensors='pt') output = model(**encoded_input) ``` ## Disclaimer Do consider the biases which came from the IMDB review that may be carried over into the results of this model. ## Author Javanese DistilBERT Small was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. ## Citation If you use any of our models in your research, please cite: ```bib @inproceedings{wongso2021causal, title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures}, author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin}, booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, pages={1--7}, year={2021}, organization={IEEE} } ```

d9ae3f8e873b0659c8a6b63f9a88194c

tmsreekanth98/keyphrase-extractions_bart-large

tmsreekanth98

bart

16

5

transformers

0

text2text-generation

true

false

apache-2.0

null

['kp20k']

null

0

['generated_from_trainer']

true

1,797

false

# keyphrase-extractions_bart-large This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the kp20k dataset. It achieves the following results on the evaluation set: - Loss: 1.7257 - Rouge1: 0.4713 - Rouge2: 0.2385 - Rougel: 0.384 - Rougelsum: 0.3841 - Gen Len: 18.3164 - Phrase match: 0.1917 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Phrase match | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------------:| | 2.5104 | 1.0 | 730 | 1.8021 | 0.464 | 0.2336 | 0.3765 | 0.3766 | 18.9074 | 0.1784 | | 1.8436 | 2.0 | 1460 | 1.7473 | 0.4709 | 0.2381 | 0.3834 | 0.3836 | 17.8127 | 0.1891 | | 1.6864 | 3.0 | 2190 | 1.7257 | 0.4713 | 0.2385 | 0.384 | 0.3841 | 18.3164 | 0.1917 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

ff5025b7944463c8b61f678039bd4475

recklessrecursion/2008_Sichuan_earthquake-clustered

recklessrecursion

distilbert

8

22

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,886

false

# recklessrecursion/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5049 - Train End Logits Accuracy: 0.8507 - Train Start Logits Accuracy: 0.7778 - Validation Loss: 0.3830 - Validation End Logits Accuracy: 0.9474 - Validation Start Logits Accuracy: 0.8947 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.5049 | 0.8507 | 0.7778 | 0.3830 | 0.9474 | 0.8947 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

0d4025b92abe72130361bc4e8daa82dd

andreaparker/flan-t5-base-samsum

andreaparker

t5

7

14

transformers

0

text2text-generation

true

false

apache-2.0

null

['samsum']

null

1

0

['generated_from_trainer']

true

2,109

false

# flan-t5-base-samsum This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.3772 - Rouge1: 47.4798 - Rouge2: 23.9756 - Rougel: 40.0392 - Rougelsum: 43.6545 - Gen Len: 17.3162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.4403 | 1.0 | 1842 | 1.3829 | 46.5346 | 23.1326 | 39.4401 | 42.8272 | 17.0977 | | 1.3534 | 2.0 | 3684 | 1.3732 | 47.0911 | 23.5074 | 39.5951 | 43.2279 | 17.4554 | | 1.2795 | 3.0 | 5526 | 1.3709 | 46.8895 | 23.3243 | 39.5909 | 43.1286 | 17.2027 | | 1.2313 | 4.0 | 7368 | 1.3736 | 47.4946 | 23.7802 | 39.9999 | 43.5903 | 17.2198 | | 1.1934 | 5.0 | 9210 | 1.3772 | 47.4798 | 23.9756 | 40.0392 | 43.6545 | 17.3162 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2 ### Papers With Code Results As of 2 February 2023 the Papers with Code page for this task has the following leaderboard. Our score (Rouge 1 score of 47.4798) puts this model's performance between fourth and fifth place on the leaderboard: ![PwC leaderboard](https://i.imgur.com/Nea77uL.jpg)

fbf4445fa1cb43344fdbf9c593329436

RuiqianLi/wav2vec2-large-xls-r-300m-chinese-taiwan-colab

RuiqianLi

wav2vec2

15

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,330

false

# wav2vec2-large-xls-r-300m-chinese-taiwan-colab !!!this model has just been trained with very high learning rate and small epochs, please do not use this to do the speech to text. !!!It's just a test, I'll retrain this model with more time later when I have time. 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: 0.1 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

f14428421b20112375db198617d243c9

Rattana/wav2vec2-thai-colab

Rattana

wav2vec2

12

10

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

996

false

# wav2vec2-thai-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: 0.0003 - 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: 20 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

b8afee4e59344e2746059b8902bdfa27

hfl/chinese-bert-wwm-ext

hfl

bert

11

533,397

transformers

50

fill-mask

true

apache-2.0

['zh']

null

0

[]

false

true

1,947

false

## Chinese BERT with Whole Word Masking For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. **[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)** Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu This repository is developed based on：https://github.com/google-research/bert You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - 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/ymcui/HFL-Anthology ## Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. - Primary: https://arxiv.org/abs/2004.13922 ``` @inproceedings{cui-etal-2020-revisiting, title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", author = "Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", pages = "657--668", } ``` - Secondary: https://arxiv.org/abs/1906.08101 ``` @article{chinese-bert-wwm, title={Pre-Training with Whole Word Masking for Chinese BERT}, author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping}, journal={arXiv preprint arXiv:1906.08101}, year={2019} } ```

93902e433ddb944e6d50651668b355e6

moghis/xlm-roberta-base-finetuned-panx-de-data

moghis

xlm-roberta

12

6

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,340

false

# xlm-roberta-base-finetuned-panx-de-data 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: 0.1372 - F1 Score: 0.8621 ## 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 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

97e4ba5c64dfddcab33b49594d7feca6

roberta-large-openai-detector

null

roberta

8

27,024

transformers

5

text-classification

true

false

true

mit

['en']

['bookcorpus', 'wikipedia']

null

1

0

1

0

1

0

['exbert']

false

true

9,097

false

# RoBERTa Large OpenAI Detector ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) - [How To Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details **Model Description:** RoBERTa large OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa large model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version. - **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list - **Model Type:** Fine-tuned transformer-based language model - **Language(s):** English - **License:** MIT - **Related Models:** [RoBERTa large](https://huggingface.co/roberta-large), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection). - [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) - [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - [Explore the detector model here](https://huggingface.co/openai-detector ) ## Uses #### Direct Use The model is a classifier that can be used to detect text generated by GPT-2 models. #### Downstream Use The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research. In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write: > We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective. The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness. #### Bias Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by RoBERTa large and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa large](https://huggingface.co/roberta-large) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). ## Training #### Training Data The model is a sequence classifier based on RoBERTa large (see the [RoBERTa large model card](https://huggingface.co/roberta-large) for more details on the RoBERTa large training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)). #### Training Procedure The model developers write that: > We based a sequence classifier on RoBERTaLARGE (355 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model. They later state: > To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). #### Testing Data, Factors and Metrics The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by: > testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training. #### Results The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf): > Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results. ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Unknown - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications The model developers write that: See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details. ## Citation Information ```bibtex @article{solaiman2019release, title={Release strategies and the social impacts of language models}, author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others}, journal={arXiv preprint arXiv:1908.09203}, year={2019} } ``` APA: - Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. ## Model Card Authors This model card was written by the team at Hugging Face. ## How to Get Started with the Model More information needed

073d629786f9574d585a38dfee0a9582

Helsinki-NLP/opus-mt-en-dra

Helsinki-NLP

marian

11

548

transformers

1

translation

true

false

apache-2.0

['en', 'ta', 'kn', 'ml', 'te', 'dra']

null

1

0

['translation']

false

true

2,371

false

### eng-dra * source group: English * target group: Dravidian languages * OPUS readme: [eng-dra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-dra/README.md) * model: transformer * source language(s): eng * target language(s): kan mal tam tel * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-dra/opus-2020-07-26.zip) * test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-dra/opus-2020-07-26.test.txt) * test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-dra/opus-2020-07-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-kan.eng.kan | 4.7 | 0.348 | | Tatoeba-test.eng-mal.eng.mal | 13.1 | 0.515 | | Tatoeba-test.eng.multi | 10.7 | 0.463 | | Tatoeba-test.eng-tam.eng.tam | 9.0 | 0.444 | | Tatoeba-test.eng-tel.eng.tel | 7.1 | 0.363 | ### System Info: - hf_name: eng-dra - source_languages: eng - target_languages: dra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-dra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'ta', 'kn', 'ml', 'te', 'dra'] - src_constituents: {'eng'} - tgt_constituents: {'tam', 'kan', 'mal', 'tel'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-dra/opus-2020-07-26.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-dra/opus-2020-07-26.test.txt - src_alpha3: eng - tgt_alpha3: dra - short_pair: en-dra - chrF2_score: 0.46299999999999997 - bleu: 10.7 - brevity_penalty: 1.0 - ref_len: 7928.0 - src_name: English - tgt_name: Dravidian languages - train_date: 2020-07-26 - src_alpha2: en - tgt_alpha2: dra - prefer_old: False - long_pair: eng-dra - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

bef0968740904b33b785ac68037a3717

Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa

Mizuiro-sakura

deberta-v2

12

19

transformers

0

multiple-choice

true

false

mit

['ja']

['wikipedia', 'cc100', 'oscar']

null

0

['pytorch', 'deberta', 'deberta-v2', 'commonsenseqa', 'commonsense_qa', 'commonsense-qa', 'CommonsenseQA']

false

true

2,096

false

# このモデルはdeberta-v2-base-japaneseをファインチューニングしてCommonsenseQA(選択式の質問)に用いれるようにしたものです。このモデルはdeberta-v2-base-japaneseをyahoo japan/JGLUEのJCommonsenseQA( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。 # This model is fine-tuned model for CommonsenseQA which is based on deberta-v2-base-japanese This model is fine-tuned by using JGLUE/JCommonsenseQA dataset. You could use this model for CommonsenseQA tasks. # How to use 使い方 transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。以下のコードを実行することで、CommonsenseQAタスクを解かせることができます。 please execute this code. ```python from transformers import AutoTokenizer, AutoModelForMultipleChoice import torch import numpy as np # modelのロード tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa') model = AutoModelForMultipleChoice.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa') # 質問と選択肢の代入 question = '電子機器で使用される最も主要な電子回路基板の事をなんと言う？' choice1 = '掲示板' choice2 = 'パソコン' choice3 = 'マザーボード' choice4 = 'ハードディスク' choice5 = 'まな板' # トークン化（エンコーディング・形態素解析）する token = tokenizer([question,question,question,question,question],[choice1,choice2,choice3,choice4,choice5],return_tensors='pt',padding=True) leng=len(token['input_ids'][0]) # modelに入力するための下準備 X1 = np.empty(shape=(1, 5, leng)) X2 = np.empty(shape=(1, 5, leng)) X1[0, :, :] = token['input_ids'] X2[0, :, :] = token['attention_mask'] # modelにトークンを入力する results = model(torch.tensor(X1).to(torch.int64),torch.tensor(X2).to(torch.int64)) # 最も高い値のインデックスを取得する max_result=torch.argmax(results.logits) print(max_result) ``` # モデルの精度 accuracy of model 79.80339588918764 （参考　BERT : 72.0, XLM RoBERTa base : 68.7, LUKE : 80.0) # deberta-v2-base-japaneseとは？日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。京都大学黒橋研究室が公表されました。 # Model description This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. # Acknowledgments 謝辞モデルを公開してくださった京都大学黒橋研究室には感謝いたします。 I would like to thank Kurohashi Lab at Kyoto University.

c8292058b0082d5d8161d98bec7be357

anas-awadalla/t5-base-few-shot-k-512-finetuned-squad-seed-0

anas-awadalla

t5

17

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

957

false

# t5-base-few-shot-k-512-finetuned-squad-seed-0 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 35.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

34249c010d7cc93e7ca6bfdaf6eedc2e

limsc/reqroberta-tapt-epoch33

limsc

roberta

4

2

transformers

0

fill-mask

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,325

false

# reqroberta-tapt-epoch33 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 37100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

0300075cd6fc647379c6030d5765f62e

Nanatan/distilbert-base-uncased-finetuned-emotion

Nanatan

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,344

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: 0.2169 - Accuracy: 0.9215 - F1: 0.9215 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.798 | 1.0 | 250 | 0.3098 | 0.899 | 0.8956 | | 0.2422 | 2.0 | 500 | 0.2169 | 0.9215 | 0.9215 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

35da484db079823b9c69ae5030136269

Arashasg/WikiBert2WikiBert

Arashasg

encoder-decoder

6

0

transformers

1

text2text-generation

true

false

['apache-2.0']

['fa']

['pn-summary', 'XL-Sum']

null

0

['Wikipedia', 'Summarizer', 'bert2bert', 'Summarization']

false

true

1,612

false

# WikiBert2WikiBert Bert language models can be employed for Summarization tasks. WikiBert2WikiBert is an encoder-decoder transformer model that is initialized using the Persian WikiBert Model weights. The WikiBert Model is a Bert language model which is fine-tuned on Persian Wikipedia. After using the WikiBert weights for initialization, the model is trained for five epochs on PN-summary and Persian BBC datasets. ## How to Use: You can use the code below to get the model's outputs, or you can simply use the demo on the right. ``` from transformers import ( BertTokenizerFast, EncoderDecoderConfig, EncoderDecoderModel, BertConfig ) model_name = 'Arashasg/WikiBert2WikiBert' tokenizer = BertTokenizerFast.from_pretrained(model_name) config = EncoderDecoderConfig.from_pretrained(model_name) model = EncoderDecoderModel.from_pretrained(model_name, config=config) def generate_summary(text): inputs = tokenizer(text, padding="max_length", truncation=True, max_length=512, return_tensors="pt") input_ids = inputs.input_ids.to("cuda") attention_mask = inputs.attention_mask.to("cuda") outputs = model.generate(input_ids, attention_mask=attention_mask) output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True) return output_str input = 'your input comes here' summary = generate_summary(input) ``` ## Evaluation I separated 5 percent of the pn-summary for evaluation of the model. The rouge scores of the model are as follows: | Rouge-1 | Rouge-2 | Rouge-l | | ------------- | ------------- | ------------- | | 38.97% | 18.42% | 34.50% |

3245cef6032b3490c0a51adfab16539b

Lvxue/distilled-mt5-small-b1.5

Lvxue

mt5

17

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,035

false

# distilled-mt5-small-b1.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.7938 - Bleu: 7.5422 - Gen Len: 44.3267 ## 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

35b949c25f291f9284355a63585e11e5

jonatasgrosman/exp_w2v2t_ja_wav2vec2_s834

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

456

false

# exp_w2v2t_ja_wav2vec2_s834 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (ja)](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.

0bbbd177749afe922a25ab702d0a7a4c

jonatasgrosman/exp_w2v2t_de_unispeech_s62

jonatasgrosman

unispeech

10

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

468

false

# exp_w2v2t_de_unispeech_s62 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

afaee7e921eb371e6f17a44eb169613a

kadirnar/yolov8m-v8.0

kadirnar

null

3

0

null

0

object-detection

false

gpl-3.0

null

['detection-datasets/coco']

null

0

['object-detection', 'computer-vision', 'yolov8', 'yolov5']

false

true

457

false

### Model Description [Ultralytics:](https://github.com/ultralytics/ultralytics/) YOLOv8 in PyTorch > ONNX > CoreML > TFLite] ### Installation ``` pip install ultralytics ``` ### Yolov8 Inference ```python from ultralytics import YOLO model = YOLO('kadirnar/yolov8m-v8.0') model.conf = conf_threshold model.iou = iou_threshold prediction = model.predict(image, imgsz=image_size, show=False, save=False) ``` ### BibTeX Entry and Citation Info ``` ```

017ede23e612ad82b4daf3d619955461

yanaiela/roberta-base-epoch_58

yanaiela

roberta

9

3

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_58']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 58 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_58. ## 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}, } ```

4d0543a905779a1b260bd398ca7ab02b

lantianai/Stable_Diffusion_Inpainting_Mask_EulerA

lantianai

null

17

9

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

14,470

false

Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) :-------------------------:|:-------------------------:| ## Examples: You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```python from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, ) prompt = "Face of a yellow cat, high resolution, sitting on a park bench" #image and mask_image should be PIL images. #The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0] image.save("./yellow_cat_on_park_bench.png") ``` **How it works:** `image` | `mask_image` :-------------------------:|:-------------------------:| <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/> `prompt` | `Output` :-------------------------:|:-------------------------:| Face of a yellow cat, high resolution, sitting on a park bench | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/> ### Original GitHub Repository 1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt) 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/runwayml/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt` which were trained as follows, - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-4.ckpt`: Resumed from stable-diffusion-v1-2.225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. - `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Inpainting Evaluation To assess the performance of the inpainting model, we used the same evaluation protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed prompt of `photograph of a beautiful empty scene, highest quality settings`. | Model | FID | LPIPS | |-----------------------------|------|------------------| | Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) | | Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) | | CoModGAN | 1.82 | 0.15 | | LaMa | 2.21 | 0.134 (+- 0.080) | ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

dfee420109622375cef8e550c576a5e3

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

Akshat

xlm-roberta

24

5

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

1

0

['generated_from_trainer']

true

1,320

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: 0.1405 - F1: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2542 | 1.0 | 787 | 0.1788 | 0.8083 | | 0.1307 | 2.0 | 1574 | 0.1371 | 0.8488 | | 0.0784 | 3.0 | 2361 | 0.1405 | 0.8611 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

bec9cc10fa2786cbfc4b0d8364a62f02

KoichiYasuoka/deberta-small-coptic-upos

KoichiYasuoka

deberta-v2

9

5

transformers

0

token-classification

true

false

cc-by-sa-4.0

['cop']

['universal_dependencies']

null

0

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

false

true

891

false

# deberta-small-coptic-upos ## Model Description This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-small-coptic](https://huggingface.co/KoichiYasuoka/deberta-small-coptic). 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/deberta-small-coptic-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-small-coptic-upos") ``` or ``` import esupar nlp=esupar.load("KoichiYasuoka/deberta-small-coptic-upos") ``` ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

896cc8b867076087d4b3bdeed82e232b

research-backup/t5-small-squadshifts-vanilla-new_wiki-qg

research-backup

t5

34

1

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_squadshifts']

null

0

['question generation']

true

4,153

false

# Model Card of `research-backup/t5-small-squadshifts-vanilla-new_wiki-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="research-backup/t5-small-squadshifts-vanilla-new_wiki-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-squadshifts-vanilla-new_wiki-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squadshifts-vanilla-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset | |:-----------|--------:|:---------|:---------------------------------------------------------------------------| | BERTScore | 83.08 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 6.9 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 2.75 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 1.38 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 0.81 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 8.26 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 52.25 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 8.85 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 32 - lr: 1e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-squadshifts-vanilla-new_wiki-qg/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", } ```

8a8783966dbdd5551d6bf08b71060e73

apatidar0/bert-finetuned-squad

apatidar0

bert

12

9

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

930

false

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

1497fc25ce86602416899db224685490

soorya12/t5-small-finetuned-on-cloudsek-data-assignment

soorya12

t5

7

3

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,392

false

# t5-small-finetuned-on-cloudsek-data-assignment This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.8961 - Validation Loss: 1.8481 - Epoch: 1 ## 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': 6744, '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 | |:----------:|:---------------:|:-----:| | 1.8958 | 1.8481 | 0 | | 1.8961 | 1.8481 | 1 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

f9c79e88b84205719724b5ef66efff38

tomekkorbak/serene_goldberg

tomekkorbak

null

2

0

null

0

null

false

mit

['en']

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

null

0

['generated_from_trainer']

true

8,797

false

# serene_goldberg This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 25000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0'}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'serene_goldberg', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/sj097lix

ade338acb558ac9b7768a3a81c644b7a

frtna/jwt300_mt-Italian-to-Spanish_transformers

frtna

t5

10

3

transformers

0

text2text-generation

true

false

apache-2.0

null

['new_dataset']

null

0

['generated_from_trainer']

true

1,293

false

# jwt300_mt-Italian-to-Spanish_transformers This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the new_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.4425 - Sacrebleu: 0.9057 - Gen Len: 18.1276 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 2.7545 | 1.0 | 2229 | 2.4425 | 0.9057 | 18.1276 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6

8871f6aad5a3721d0751fb52e5489f49

xinranyyyy/roberta_checkpoint-finetuned-squad

xinranyyyy

roberta

13

5

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

1,285

false

# roberta_checkpoint-finetuned-squad This model is a fine-tuned version of [WillHeld/roberta-base-coqa](https://huggingface.co/WillHeld/roberta-base-coqa) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.8969 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 0.8504 | 1.0 | 5536 | 0.8424 | | 0.6219 | 2.0 | 11072 | 0.8360 | | 0.4807 | 3.0 | 16608 | 0.8969 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

9d63a4dd5eccef854026bd9714f377c6

MultiversexPeeps/art-of-wave

MultiversexPeeps

null

21

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

866

false

### Art of Wave Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Information on this model will be here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk wvert1 (use that on your prompt)

47c506bef435bfe5bdfef2f9ff38eb83

jonatasgrosman/exp_w2v2t_it_unispeech_s156

jonatasgrosman

unispeech

10

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_it_unispeech_s156 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (it)](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.

09434c314d446797613cd1a4bc291132

BSC-LT/roberta-large-bne-sqac

BSC-LT

roberta

9

0

transformers

3

question-answering

true

false

apache-2.0

['es']

['BSC-TeMU/SQAC']

null

0

['national library of spain', 'spanish', 'bne', 'qa', 'question answering']

false

true

1,627

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-sqac # Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) 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 [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC). ## Evaluation and results F1 Score: 0.7993 (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} } ```

2a5316c84b941b77b2a27697575325c7

mrm8488/wav2vec2-large-xlsr-53-euskera

mrm8488

wav2vec2

11

10

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['eu']

['common_voice']

null

0

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

true

3,328

false

# Wav2Vec2-Large-XLSR-53-euskera Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Euskera using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "eu", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Euskera 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", "eu", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 24.03 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found ???

d2047408958137520ac739d879343ee9

sgugger/finetuned-bert

sgugger

bert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

false

true

1,378

false

# finetuned-bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3916 - Accuracy: 0.875 - F1: 0.9125 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.581 | 1.0 | 230 | 0.4086 | 0.8260 | 0.8711 | | 0.366 | 2.0 | 460 | 0.3758 | 0.8480 | 0.8963 | | 0.2328 | 3.0 | 690 | 0.3916 | 0.875 | 0.9125 | ### Framework versions - Transformers 4.9.0.dev0 - Pytorch 1.8.1+cu111 - Datasets 1.8.1.dev0 - Tokenizers 0.10.1

8902b10dd345b87030d2a1d9560b81cd

poloclub/RobArch

poloclub

null

5

0

null

2

null

false

mit

null

['imagenet-1k']

null

0

['adversarial machine learning']

false

true

3,920

false

## RobArch: Designing Robust Architectures against Adversarial Attacks *ShengYun Peng, Weilin Xu, Cory Cornelius, Kevin Li, Rahul Duggal, Duen Horng Chau, Jason Martin* Check https://github.com/ShengYun-Peng/RobArch for the complete code. ### Abstract Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the adversarial training process, there are few investigations into how architecture components affect robustness, and they rarely constrain model capacity. Thus, it is unclear where robustness precisely comes from. In this work, we present the first large-scale systematic study on the robustness of DNN architecture components under fixed parameter budgets. Through our investigation, we distill 18 actionable robust network design guidelines that empower model developers to gain deep insights. We demonstrate these guidelines' effectiveness by introducing the novel Robust Architecture (RobArch) model that instantiates the guidelines to build a family of top-performing models across parameter capacities against strong adversarial attacks. RobArch achieves the new state-of-the-art AutoAttack accuracy on the RobustBench ImageNet leaderboard. ### Prerequisites 1. Register Weights & Biases [account](https://wandb.ai/site) 2. Prepare ImageNet via [Fast AT - Installation step 3 & 4](https://github.com/locuslab/fast_adversarial/tree/master/ImageNet) > Run step 4 only if you want to use Fast-AT. 3. Set up venv: ```bash make .venv_done ``` ### Training Fast-AT is much faster than standard PGD AT. For RobArch-S, Fast-AT takes ~1.5 days on 2 Nvidia A100s, but ~5 days on 4 Nvidia A100s. #### Torchvision models - Fast AT (e.g., ResNet-50) ```bash make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/Torch_ResNet50/.done_test_pgd ``` If you want to test other off-the-shelf models in [torchvision](https://pytorch.org/vision/stable/models.html#classification), add the model name in [MODEL.mk](MODEL.mk), and create a new make target by following other ResNets/WideResNets in [Makefile](Makefile). #### RobArch - Fast AT (e.g., RobArch-S) ```bash make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/RobArch_S/.done_test_pgd ``` #### RobArch - Standard PGD AT (e.g., RobArch-S) ```bash # Training make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_train # Evaluation on PGD make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_pgd # Evaluation on AutoAttack make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_aa # Pretrained models evaluated on AutoAttack make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_pretrained ``` ### Pretrained models - ImageNet $\ell_\infty$-norm | Architecture | #Param | Natural | AutoAttack | PGD10-4 | PGD50-4 | PGD100-4 | PGD100-2 | PGD100-8 | | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | | [RobArch-S](https://huggingface.co/poloclub/RobArch/resolve/main/pretrained/robarch_s.pt) | 26M | 70.17% | 44.14% | 48.19% | 47.78% | 47.77% | 60.06% | 21.77% | | [RobArch-M](https://huggingface.co/poloclub/RobArch/resolve/main/pretrained/robarch_m.pt) | 46M | 71.88% | 46.26% | 49.84% | 49.32% | 49.30% | 61.89% | 23.01% | | [RobArch-L](https://huggingface.co/poloclub/RobArch/resolve/main/pretrained/robarch_l.pt) | 104M | 73.44% | 48.94% | 51.72% | 51.04% | 51.03% | 63.49% | 25.31% | ### Citation ```bibtex @misc{peng2023robarch, title={RobArch: Designing Robust Architectures against Adversarial Attacks}, author={ShengYun Peng and Weilin Xu and Cory Cornelius and Kevin Li and Rahul Duggal and Duen Horng Chau and Jason Martin}, year={2023}, eprint={2301.03110}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

3a9703ff3b32d6c37c93b4712d9aa03a

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_128

gokuls

mobilebert

17

1

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,601

false

# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5913 - Accuracy: 0.1408 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3404 | 1.0 | 435 | 0.5913 | 0.1408 | | 0.3027 | 2.0 | 870 | 0.5985 | 0.1127 | | 0.2935 | 3.0 | 1305 | 0.6351 | 0.1127 | | 0.2884 | 4.0 | 1740 | 0.6013 | 0.0986 | | 0.2838 | 5.0 | 2175 | 0.6154 | 0.0986 | | 0.2788 | 6.0 | 2610 | 0.6608 | 0.0845 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

8f21f0cbd093e88415a44bde235bd404

fathyshalab/massive_play-roberta-large-v1-4-71

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,458

false

# fathyshalab/massive_play-roberta-large-v1-4-71 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_play-roberta-large-v1-4-71") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

990a3fc065f7b9bfb738c742f8d40994

jonatasgrosman/exp_w2v2t_ar_wavlm_s95

jonatasgrosman

wavlm

10

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

438

false

# exp_w2v2t_ar_wavlm_s95 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

8f9f0a51bff280b0a40b5fc87811e51e

SayakRana/finetuning-hate-model-electra

SayakRana

electra

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,107

false

# finetuning-hate-model-electra This model is a fine-tuned version of [cross-encoder/ms-marco-electra-base](https://huggingface.co/cross-encoder/ms-marco-electra-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1691 - Accuracy: 0.9597 - F1: 0.3448 - Precision: 0.4545 - Recall: 0.2778 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

03bb7dd4922dc3ef93f5140120d23b53

cointegrated/rubert-tiny

cointegrated

bert

8

7,117

transformers

12

feature-extraction

true

false

mit

['ru', 'en']

null

0

['russian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'tiny', 'feature-extraction', 'sentence-similarity']

false

true

2,169

false

This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), with a larger vocabulary and better quality on practically all Russian NLU tasks. This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English. It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus), [OPUS-100](https://huggingface.co/datasets/opus100) and [Tatoeba](https://huggingface.co/datasets/tatoeba), using MLM loss (distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)), translation ranking loss, and `[CLS]` embeddings distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), [rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence), Laser and USE. There is a more detailed [description in Russian](https://habr.com/ru/post/562064/). Sentence embeddings can be produced as follows: ```python # pip install transformers sentencepiece import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny") model = AutoModel.from_pretrained("cointegrated/rubert-tiny") # model.cuda() # uncomment it if you have a GPU def embed_bert_cls(text, model, tokenizer): t = tokenizer(text, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): model_output = model(**{k: v.to(model.device) for k, v in t.items()}) embeddings = model_output.last_hidden_state[:, 0, :] embeddings = torch.nn.functional.normalize(embeddings) return embeddings[0].cpu().numpy() print(embed_bert_cls('привет мир', model, tokenizer).shape) # (312,) ```

0777b4fbb92b37f510d8b2bf91ad370f

NahedAbdelgaber/evaluating-student-writing-distibert-ner-with-metric

NahedAbdelgaber

distilbert

12

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,625

false

# evaluating-student-writing-distibert-ner-with-metric This model is a fine-tuned version of [NahedAbdelgaber/evaluating-student-writing-distibert-ner](https://huggingface.co/NahedAbdelgaber/evaluating-student-writing-distibert-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7535 - Precision: 0.0614 - Recall: 0.2590 - F1: 0.0993 - Accuracy: 0.6188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7145 | 1.0 | 1755 | 0.7683 | 0.0546 | 0.2194 | 0.0875 | 0.6191 | | 0.6608 | 2.0 | 3510 | 0.7504 | 0.0570 | 0.2583 | 0.0934 | 0.6136 | | 0.5912 | 3.0 | 5265 | 0.7535 | 0.0614 | 0.2590 | 0.0993 | 0.6188 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

a6748dc709d36949a695de8c52a8dbb1

danieleV9H/hubert-base-libri-clean-ft100h-v3

danieleV9H

hubert

12

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['librispeech_asr']

null

0

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

true

4,781

false

# hubert-base-libri-clean-ft100h-v3 This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.1120 - Wer: 0.1332 ## 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: 16 - 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: 600 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.201 | 0.14 | 250 | 3.9799 | 1.0 | | 2.8893 | 0.28 | 500 | 3.4838 | 1.0 | | 2.8603 | 0.42 | 750 | 3.3505 | 1.0 | | 2.7216 | 0.56 | 1000 | 2.1194 | 0.9989 | | 1.3372 | 0.7 | 1250 | 0.8124 | 0.6574 | | 0.8238 | 0.84 | 1500 | 0.5712 | 0.5257 | | 0.6449 | 0.98 | 1750 | 0.4442 | 0.4428 | | 0.5241 | 1.12 | 2000 | 0.3442 | 0.3672 | | 0.4458 | 1.26 | 2250 | 0.2850 | 0.3186 | | 0.3959 | 1.4 | 2500 | 0.2507 | 0.2882 | | 0.3641 | 1.54 | 2750 | 0.2257 | 0.2637 | | 0.3307 | 1.68 | 3000 | 0.2044 | 0.2434 | | 0.2996 | 1.82 | 3250 | 0.1969 | 0.2313 | | 0.2794 | 1.96 | 3500 | 0.1823 | 0.2193 | | 0.2596 | 2.1 | 3750 | 0.1717 | 0.2096 | | 0.2563 | 2.24 | 4000 | 0.1653 | 0.2000 | | 0.2532 | 2.38 | 4250 | 0.1615 | 0.1971 | | 0.2376 | 2.52 | 4500 | 0.1559 | 0.1916 | | 0.2341 | 2.66 | 4750 | 0.1494 | 0.1855 | | 0.2102 | 2.8 | 5000 | 0.1464 | 0.1781 | | 0.2222 | 2.94 | 5250 | 0.1399 | 0.1732 | | 0.2081 | 3.08 | 5500 | 0.1450 | 0.1707 | | 0.1963 | 3.22 | 5750 | 0.1337 | 0.1655 | | 0.2107 | 3.36 | 6000 | 0.1344 | 0.1633 | | 0.1866 | 3.5 | 6250 | 0.1339 | 0.1611 | | 0.186 | 3.64 | 6500 | 0.1311 | 0.1563 | | 0.1703 | 3.78 | 6750 | 0.1307 | 0.1537 | | 0.1819 | 3.92 | 7000 | 0.1277 | 0.1555 | | 0.176 | 4.06 | 7250 | 0.1280 | 0.1515 | | 0.1837 | 4.2 | 7500 | 0.1249 | 0.1504 | | 0.1678 | 4.34 | 7750 | 0.1236 | 0.1480 | | 0.1624 | 4.48 | 8000 | 0.1194 | 0.1456 | | 0.1631 | 4.62 | 8250 | 0.1215 | 0.1462 | | 0.1736 | 4.76 | 8500 | 0.1192 | 0.1451 | | 0.1752 | 4.9 | 8750 | 0.1206 | 0.1432 | | 0.1578 | 5.04 | 9000 | 0.1151 | 0.1415 | | 0.1537 | 5.18 | 9250 | 0.1185 | 0.1402 | | 0.1771 | 5.33 | 9500 | 0.1165 | 0.1414 | | 0.1481 | 5.47 | 9750 | 0.1152 | 0.1413 | | 0.1509 | 5.61 | 10000 | 0.1152 | 0.1382 | | 0.146 | 5.75 | 10250 | 0.1133 | 0.1385 | | 0.1464 | 5.89 | 10500 | 0.1139 | 0.1371 | | 0.1442 | 6.03 | 10750 | 0.1162 | 0.1365 | | 0.128 | 6.17 | 11000 | 0.1147 | 0.1371 | | 0.1381 | 6.31 | 11250 | 0.1148 | 0.1378 | | 0.1343 | 6.45 | 11500 | 0.1113 | 0.1363 | | 0.1325 | 6.59 | 11750 | 0.1134 | 0.1355 | | 0.1442 | 6.73 | 12000 | 0.1142 | 0.1358 | | 0.1286 | 6.87 | 12250 | 0.1133 | 0.1352 | | 0.1349 | 7.01 | 12500 | 0.1129 | 0.1344 | | 0.1338 | 7.15 | 12750 | 0.1131 | 0.1328 | | 0.1403 | 7.29 | 13000 | 0.1124 | 0.1338 | | 0.1314 | 7.43 | 13250 | 0.1141 | 0.1335 | | 0.1283 | 7.57 | 13500 | 0.1124 | 0.1332 | | 0.1347 | 7.71 | 13750 | 0.1107 | 0.1332 | | 0.1195 | 7.85 | 14000 | 0.1119 | 0.1332 | | 0.1326 | 7.99 | 14250 | 0.1120 | 0.1332 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

fd4250de656f0dfd56003c0ee782634d

paola-md/distil-tis

paola-md

roberta

6

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,578

false

# distil-tis This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6061 - Rmse: 0.7785 - Mse: 0.6061 - Mae: 0.6003 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.7173 | 1.0 | 492 | 0.7060 | 0.8403 | 0.7060 | 0.5962 | | 0.5955 | 2.0 | 984 | 0.6585 | 0.8115 | 0.6585 | 0.5864 | | 0.5876 | 3.0 | 1476 | 0.6090 | 0.7804 | 0.6090 | 0.6040 | | 0.5871 | 4.0 | 1968 | 0.6247 | 0.7904 | 0.6247 | 0.5877 | | 0.5871 | 5.0 | 2460 | 0.6061 | 0.7785 | 0.6061 | 0.6003 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

2250a88967c4b4f1c7b6d5f743a23243

SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-5

SetFit

distilbert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,833

false

# distilbert-base-uncased__hate_speech_offensive__train-8-5 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: 1.7214 - Accuracy: 0.37 ## 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0995 | 1.0 | 5 | 1.1301 | 0.0 | | 1.0227 | 2.0 | 10 | 1.1727 | 0.0 | | 1.0337 | 3.0 | 15 | 1.1734 | 0.2 | | 0.9137 | 4.0 | 20 | 1.1829 | 0.2 | | 0.8065 | 5.0 | 25 | 1.1496 | 0.4 | | 0.7038 | 6.0 | 30 | 1.1101 | 0.4 | | 0.6246 | 7.0 | 35 | 1.0982 | 0.2 | | 0.4481 | 8.0 | 40 | 1.0913 | 0.2 | | 0.3696 | 9.0 | 45 | 1.0585 | 0.4 | | 0.3137 | 10.0 | 50 | 1.0418 | 0.4 | | 0.2482 | 11.0 | 55 | 1.0078 | 0.4 | | 0.196 | 12.0 | 60 | 0.9887 | 0.6 | | 0.1344 | 13.0 | 65 | 0.9719 | 0.6 | | 0.1014 | 14.0 | 70 | 1.0053 | 0.6 | | 0.111 | 15.0 | 75 | 0.9653 | 0.6 | | 0.0643 | 16.0 | 80 | 0.9018 | 0.6 | | 0.0559 | 17.0 | 85 | 0.9393 | 0.6 | | 0.0412 | 18.0 | 90 | 1.0210 | 0.6 | | 0.0465 | 19.0 | 95 | 0.9965 | 0.6 | | 0.0328 | 20.0 | 100 | 0.9739 | 0.6 | | 0.0289 | 21.0 | 105 | 0.9796 | 0.6 | | 0.0271 | 22.0 | 110 | 0.9968 | 0.6 | | 0.0239 | 23.0 | 115 | 1.0143 | 0.6 | | 0.0201 | 24.0 | 120 | 1.0459 | 0.6 | | 0.0185 | 25.0 | 125 | 1.0698 | 0.6 | | 0.0183 | 26.0 | 130 | 1.0970 | 0.6 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

d708c5cba870a84727fe0421179502a6

globuslabs/ScholarBERT_1

globuslabs

bert

8

10

transformers

1

fill-mask

true

false

apache-2.0

['en']

null

0

1

0

1

['science', 'multi-displinary']

false

true

1,756

false

# ScholarBERT_1 Model This is the **ScholarBERT_1** variant of the ScholarBERT model family. The model is pretrained on a large collection of scientific research articles (**2.2B tokens**). This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default. The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters. # Model Architecture | Hyperparameter | Value | |-----------------|:-------:| | Layers | 24 | | Hidden Size | 1024 | | Attention Heads | 16 | | Total Parameters | 340M | # Training Dataset The vocab and the model are pertrained on **1% of the PRD** scientific literature dataset. The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”), a nonprofit organization based in California. This dataset was constructed from a corpus of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals. The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences, Social Sciences, and Technology. The distribution of articles is shown below. ![corpus pie chart](https://huggingface.co/globuslabs/ScholarBERT/resolve/main/corpus_pie_chart.png) # BibTeX entry and citation info If using this model, please cite this paper: ``` @misc{hong2022scholarbert, doi = {10.48550/ARXIV.2205.11342}, url = {https://arxiv.org/abs/2205.11342}, author = {Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian}, title = {ScholarBERT: Bigger is Not Always Better}, publisher = {arXiv}, year = {2022} } ```

90f336c65c82322f5980e3bebe033937

SampsonChris/sentiment_analysis_on_covid_tweets

SampsonChris

distilbert

18

15

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,122

false

# sentiment_analysis_on_covid_tweets 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: - eval_loss: 0.6053 - eval_accuracy: 0.7625 - eval_runtime: 33.7416 - eval_samples_per_second: 59.274 - eval_steps_per_second: 7.409 - 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: 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 ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

533037e7019cad1f35a8d1a26d3e3920

MultiBertGunjanPatrick/multiberts-seed-0-60k

MultiBertGunjanPatrick

bert

7

3

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-0']

false

true

6,479

false

# MultiBERTs Seed 0 Checkpoint 60k (uncased) Seed 0 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-60k') model = BertModel.from_pretrained("multiberts-seed-0-60k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

d51ce7d3f89b6b5a3b6e8a3a220f2baa

google/t5-efficient-small-dl2

google

t5

12

10

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,250

false

# T5-Efficient-SMALL-DL2 (Deep-Narrow version) T5-Efficient-SMALL-DL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-small-dl2** - is of model type **Small** with the following variations: - **dl** is **2** It has **43.73** million parameters and thus requires *ca.* **174.93 MB** of memory in full precision (*fp32*) or **87.46 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

d027195cb1e0587808259bbb79d18099

anas-awadalla/roberta-base-few-shot-k-512-finetuned-squad-seed-8

anas-awadalla

roberta

17

5

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

983

false

# roberta-base-few-shot-k-512-finetuned-squad-seed-8 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

010375c8fead0e7206b353f921037cc2

ShadoWxShinigamI/Midjourney-v4-PaintArt

ShadoWxShinigamI

null

18

154

diffusers

31

null

false

openrail

null

5

0

4

1

0

[]

false

true

870

false

##MODEL BY ShadoWxShinigamI Use Token - mdjrny-pntrt illustration style at the beginning of your prompt; If some object doesn't work, provide more context in your prompt [eg:- 'ocean,ship,waves' instead of just 'ship'] Training - 2080 steps, Batch size 4, 512x512, v1-5 Base, 26 images Examples:- ![tmp5533s1nv.png](https://s3.amazonaws.com/moonup/production/uploads/1669303345997-633a520aecbd8b19357b4806.png) ![tmppgdfjq3w.png](https://s3.amazonaws.com/moonup/production/uploads/1669303363601-633a520aecbd8b19357b4806.png) ![tmpq14p0iqo.png](https://s3.amazonaws.com/moonup/production/uploads/1669303380519-633a520aecbd8b19357b4806.png) ![tmpqvml7g3_.png](https://s3.amazonaws.com/moonup/production/uploads/1669303393613-633a520aecbd8b19357b4806.png) ![tmpvjxs8d39.png](https://s3.amazonaws.com/moonup/production/uploads/1669303406989-633a520aecbd8b19357b4806.png)

35659921b45bb65a7797a9d0f35e9b38

MultiBertGunjanPatrick/multiberts-seed-3-60k

MultiBertGunjanPatrick

bert

7

4

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-3']

false

true

6,479

false

# MultiBERTs Seed 3 Checkpoint 60k (uncased) Seed 3 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-3](https://hf.co/multberts-seed-3). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-60k') model = BertModel.from_pretrained("multiberts-seed-3-60k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

776aab8986c6354cd28606e54e90d820

FredZhang7/google-safesearch-mini

FredZhang7

inceptionv3

13

0

keras

1

image-classification

true

false

creativeml-openrail-m

null

0

['safety-checker', 'tensorflow', 'node.js']

false

true

6,729

false

# Google Safesearch Mini Model Card <a href="https://huggingface.co/FredZhang7/google-safesearch-mini-v2"> <bold> Version 2 is here! </bold> </a> This model is trained on 2,220,000+ images scraped from Google Images, Reddit, Imgur, and Github. The InceptionV3 and Xception models have been fine-tuned to predict the likelihood of an image falling into one of three categories: nsfw_gore, nsfw_suggestive, and safe. After 20 epochs on PyTorch, the finetuned InceptionV3 model achieves 94% acc on both training and test data. After 3.3 epochs on Keras, the finetuned Xception model scores 94% acc on training set and 92% on test set. Not only is this model accurate, but it also offers a significant advantage over stable diffusion safety checkers. By using our model, users can save 1.12GB of RAM and disk space. # PyTorch The PyTorch model runs much slower with transformers, so downloading it externally is a better option. ```bash pip install --upgrade torchvision ``` ```python import torch, os, warnings, requests from io import BytesIO from PIL import Image from urllib.request import urlretrieve from torchvision import transforms PATH_TO_IMAGE = 'https://images.unsplash.com/photo-1594568284297-7c64464062b1' USE_CUDA = False warnings.filterwarnings("ignore") def download_model(): print("Downloading google_safesearch_mini.bin...") urlretrieve("https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/pytorch_model.bin", "google_safesearch_mini.bin") def eval(): if not os.path.exists("google_safesearch_mini.bin"): download_model() model = torch.jit.load('./google_safesearch_mini.bin') img = Image.open(PATH_TO_IMAGE).convert('RGB') if not (PATH_TO_IMAGE.startswith('http://') or PATH_TO_IMAGE.startswith('https://')) else Image.open(BytesIO(requests.get(PATH_TO_IMAGE).content)).convert('RGB') transform = transforms.Compose([transforms.Resize(299), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) img = transform(img).unsqueeze(0) if USE_CUDA: img, model = img.cuda(), model.cuda() else: img, model = img.cpu(), model.cpu() model.eval() with torch.no_grad(): out, _ = model(img) _, predicted = torch.max(out.data, 1) classes = {0: 'nsfw_gore', 1: 'nsfw_suggestive', 2: 'safe'} # account for edge cases if predicted[0] != 2 and abs(out[0][2] - out[0][predicted[0]]) > 0.20: img = Image.new('RGB', image.size, color = (0, 255, 255)) print("\033[93m" + "safe" + "\033[0m") else: print('\n\033[1;31m' + classes[predicted.item()] + '\033[0m' if predicted.item() != 2 else '\033[1;32m' + classes[predicted.item()] + '\033[0m\n') if __name__ == '__main__': eval() ``` Output Example: ![prediction](./output_example.png) # Keras ```python import tensorflow as tf from PIL import Image import requests, os # download the model url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/saved_model.pb" r = requests.get(url, allow_redirects=True) if not os.path.exists('tensorflow'): os.makedirs('tensorflow') open('tensorflow/saved_model.pb', 'wb').write(r.content) # download the variables url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.data-00000-of-00001" r = requests.get(url, allow_redirects=True) if not os.path.exists('tensorflow/variables'): os.makedirs('tensorflow/variables') open('tensorflow/variables/variables.data-00000-of-00001', 'wb').write(r.content) url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.index" r = requests.get(url, allow_redirects=True) open('tensorflow/variables/variables.index', 'wb').write(r.content) # load the model model = tf.saved_model.load('./tensorflow') image = Image.open('cat.jpg') image = image.resize((299, 299)) image = tf.convert_to_tensor(image) image = tf.expand_dims(image, 0) # run the model tensor = model(image) classes = ['nsfw_gore', 'nsfw_suggestive', 'safe'] prediction = classes[tf.argmax(tensor, 1)[0]] print('\033[1;32m' + prediction + '\033[0m' if prediction == 'safe' else '\033[1;33m' + prediction + '\033[0m') ``` Output Example: ![prediction](./output_example.png) # Tensorflow.js ```bash npm i @tensorflow/tfjs-node ``` ```javascript const tf = require('@tensorflow/tfjs-node'); const fs = require('fs'); const { pipeline } = require('stream'); const { promisify } = require('util'); const download = async (url, path) => { // Taken from https://levelup.gitconnected.com/how-to-download-a-file-with-node-js-e2b88fe55409 const streamPipeline = promisify(pipeline); const response = await fetch(url); if (!response.ok) { throw new Error(`unexpected response ${response.statusText}`); } await streamPipeline(response.body, fs.createWriteStream(path)); }; async function run() { // download saved model and variables from https://huggingface.co/FredZhang7/google-safesearch-mini/tree/main/tensorflow if (!fs.existsSync('tensorflow')) { fs.mkdirSync('tensorflow'); await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/saved_model.pb', 'tensorflow/saved_model.pb'); fs.mkdirSync('tensorflow/variables'); await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.data-00000-of-00001', 'tensorflow/variables/variables.data-00000-of-00001'); await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.index', 'tensorflow/variables/variables.index'); } // load model and image const model = await tf.node.loadSavedModel('./tensorflow/'); const image = tf.node.decodeImage(fs.readFileSync('cat.jpg'), 3); // predict const input = tf.expandDims(image, 0); const tensor = model.predict(input); const max = tensor.argMax(1); const classes = ['nsfw_gore', 'nsfw_suggestive', 'safe']; console.log('\x1b[32m%s\x1b[0m', classes[max.dataSync()[0]], '\n'); } run(); ``` Output Example: ![tfjs output](./tfjs_output.png) # Bias and Limitations Each person's definition of "safe" is different. The images in the dataset are classified as safe/unsafe by Google SafeSearch, Reddit, and Imgur. It is possible that some images may be safe to others but not to you. Also, when a model encounters an image with things it hasn't seen, it likely makes wrong predictions. This is why in the PyTorch example, I accounted for the "edge cases" before printing the predictions.

8d89f5f1885b80bbf46151789d4dfae6

vitouphy/wav2vec2-xls-r-300m-timit-phoneme

vitouphy

wav2vec2

9

113

transformers

3

automatic-speech-recognition

true

false

apache-2.0

['en']

null

1

0

1

0

4

1

3

['automatic-speech-recognition', 'pytorch', 'transformers', 'en', 'generated_from_trainer']

true

3,028

false

## Model This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Timit dataset. Check [this notebook](https://www.kaggle.com/code/vitouphy/phoneme-recognition-with-wav2vec2) for training detail. ## Usage **Approach 1:** Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output. ```python from transformers import pipeline # Load the model pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-timit-phoneme") # Process raw audio output = pipe("audio_file.wav", chunk_length_s=10, stride_length_s=(4, 2)) ``` **Approach 2:** More custom way to predict phonemes. ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch import soundfile as sf # load model and processor processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme") model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme") # Read and process the input audio_input, sample_rate = sf.read("audio_file.wav") inputs = processor(audio_input, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits # Decode id into string predicted_ids = torch.argmax(logits, axis=-1) predicted_sentences = processor.batch_decode(predicted_ids) print(predicted_sentences) ``` ## Training and evaluation data We use [DARPA TIMIT dataset](https://www.kaggle.com/datasets/mfekadu/darpa-timit-acousticphonetic-continuous-speech) for this model. - We split into **80/10/10** for training, validation, and testing respectively. - That roughly corresponds to about **137/17/17** minutes. - The model obtained **7.996%** on this test set. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 ### Citation ``` @misc { phy22-phoneme, author = {Phy, Vitou}, title = {{Automatic Phoneme Recognition on TIMIT Dataset with Wav2Vec 2.0}}, year = 2022, note = {{If you use this model, please cite it using these metadata.}}, publisher = {Hugging Face}, version = {1.0}, doi = {10.57967/hf/0125}, url = {https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-timit-phoneme} } ```

d2ef61e8063ac127b375421f6c53acab

MartinoMensio/racism-models-raw-label-epoch-2

MartinoMensio

bert

4

5

transformers

0

text-classification

true

false

mit

['es']

null

0

[]

false

true

4,088

false

### Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `raw-label-epoch-2` ### Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'raw-label-epoch-2' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) # [{'label': 'racist', 'score': 0.8982619643211365}, {'label': 'non-racist', 'score': 0.6703745126724243}] ``` For more details, see https://github.com/preyero/neatclass22

4e20db0069333ca8ead5b8e5bfab0b35