davanstrien/model_cards_with_readmes · Datasets at Hugging Face

sd-concepts-library/carlitos-el-mago

sd-concepts-library

null

9

0

null

1

null

false

mit

null

0

[]

false

true

1,085

false

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

b6ff16262f9c5abad876050d0a78c25d

sd-concepts-library/ingmar-bergman

sd-concepts-library

null

10

0

null

6

null

false

mit

null

0

[]

false

true

1,184

false

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

0e7c79b6812c4c21d9b407cfba93d8b3

jonatasgrosman/exp_w2v2t_uk_hubert_s878

jonatasgrosman

hubert

10

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['uk']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

452

false

# exp_w2v2t_uk_hubert_s878 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (uk)](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.

ef822685940eee51935ef6ad0295dfa2

nmb-paperspace-hf/roberta-base-finetuned-swag

nmb-paperspace-hf

roberta

12

0

transformers

0

null

true

false

mit

null

['swag']

null

0

['generated_from_trainer']

true

1,467

false

# roberta-base-finetuned-swag This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.5161 - Accuracy: 0.8266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - total_eval_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1273 | 1.0 | 2298 | 0.5415 | 0.7898 | | 0.2373 | 2.0 | 4596 | 0.4756 | 0.8175 | | 0.1788 | 3.0 | 6894 | 0.5161 | 0.8266 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0+cpu - Datasets 2.7.1 - Tokenizers 0.12.1

3d67e58f42e4574c7910c80afc6063b8

vamads/distilbert-base-uncased-finetuned-preprint_full

vamads

distilbert

31

2

transformers

1

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,328

false

# distilbert-base-uncased-finetuned-preprint_full 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: 2.3258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7315 | 1.0 | 47 | 2.4462 | | 2.577 | 2.0 | 94 | 2.3715 | | 2.5386 | 3.0 | 141 | 2.3692 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.7.0 - Tokenizers 0.13.2

f21c09895cf0757659c4d67c94b64a74

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_wnli_256

gokuls

mobilebert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,592

false

# mobilebert_sa_GLUE_Experiment_logit_kd_wnli_256 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: 0.3453 - Accuracy: 0.5634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3472 | 1.0 | 5 | 0.3453 | 0.5634 | | 0.3469 | 2.0 | 10 | 0.3464 | 0.5634 | | 0.3467 | 3.0 | 15 | 0.3465 | 0.5634 | | 0.3465 | 4.0 | 20 | 0.3457 | 0.5634 | | 0.3466 | 5.0 | 25 | 0.3453 | 0.5634 | | 0.3466 | 6.0 | 30 | 0.3454 | 0.5634 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

92ec65ee171c7c22055c5a47601bc8df

IDEA-CCNL/Randeng-BART-139M

IDEA-CCNL

bart

9

166

transformers

2

text2text-generation

true

false

apache-2.0

['zh']

null

0

[]

false

true

2,703

false

# Randeng-BART-139M - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 善于处理NLT任务，中文版的BART-base。 Good at solving NLT tasks, Chinese BART-base. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | BART | 139M | 中文-Chinese | ## 模型信息 Model Information 参考论文：[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 为了得到一个中文版的BART-base，我们用悟道语料库(180G版本)进行预训练。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了8张A100约3天。 To get a Chinese BART-base, we use WuDao Corpora (180 GB version) for pre-training. Specifically, we use the [fengshen framework](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen) in the pre-training phase which cost about 3 days with 8 A100 GPUs. ## 使用 Usage ```python from transformers import BartForConditionalGeneration, AutoTokenizer, Text2TextGenerationPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Randeng-BART-139M', use_fast=false) model=BartForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-BART-139M') text = '桂林市是世界闻名<mask> ，它有悠久的<mask>' text2text_generator = Text2TextGenerationPipeline(model, tokenizer) print(text2text_generator(text, max_length=50, do_sample=False)) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

660fc2b70146570a120537b48081c430

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

likejazz

xlm-roberta

39

1

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

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: 0.1351 - F1: 0.8516 ## 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: 96 - eval_batch_size: 96 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 132 | 0.1641 | 0.8141 | | No log | 2.0 | 264 | 0.1410 | 0.8399 | | No log | 3.0 | 396 | 0.1351 | 0.8516 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu117 - Datasets 1.16.1 - Tokenizers 0.10.3

a4055cf8befa3cf2d9b6be08a2be911b

FredMath/distilbert-base-uncased-finetuned-ner

FredMath

distilbert

12

9

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,555

false

# distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0625 - Precision: 0.9243 - Recall: 0.9361 - F1: 0.9302 - Accuracy: 0.9835 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2424 | 1.0 | 878 | 0.0685 | 0.9152 | 0.9235 | 0.9193 | 0.9813 | | 0.0539 | 2.0 | 1756 | 0.0621 | 0.9225 | 0.9333 | 0.9279 | 0.9828 | | 0.0298 | 3.0 | 2634 | 0.0625 | 0.9243 | 0.9361 | 0.9302 | 0.9835 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

51e6594152a1c6d15d68944f6eeeb749

kejian/mighty-filtering

kejian

gpt2

36

4

transformers

0

null

true

false

apache-2.0

['en']

['kejian/codeparrot-train-more-filter-3.3b-cleaned']

null

0

['generated_from_trainer']

true

4,332

false

# mighty-filtering This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'mighty-filtering', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/zk4rbxx0

c05203d9c3cb86c5758554a84dfd7035

Helsinki-NLP/opus-mt-fr-mt

Helsinki-NLP

marian

10

15

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-fr-mt * source languages: fr * target languages: mt * OPUS readme: [fr-mt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-mt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-mt/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mt/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mt/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.mt | 28.7 | 0.466 |

ee9b50ff1b5224148594871dffd8d953

racai/e4a-covid-bert-base-romanian-cased-v1

racai

bert

9

4

transformers

0

fill-mask

true

false

mit

null

0

[]

false

true

1,791

false

The model generated in the Enrich4All project.<br> Evaluated the perplexity of MLM Task fine-tuned for COVID-related corpus.<br> Baseline model: https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1 <br> Scripts and corpus used for training: https://github.com/racai-ai/e4all-models Corpus --------------- The COVID-19 datasets we designed are a small corpus and a question-answer dataset. The targeted sources were official websites of Romanian institutions involved in managing the COVID-19 pandemic, like The Ministry of Health, Bucharest Public Health Directorate, The National Information Platform on Vaccination against COVID-19, The Ministry of Foreign Affairs, as well as of the European Union. We also harvested the website of a non-profit organization initiative, in partnership with the Romanian Government through the Romanian Digitization Authority, that developed an ample platform with different sections dedicated to COVID-19 official news and recommendations. News websites were avoided due to the volatile character of the continuously changing pandemic situation, but a reliable source of information was a major private medical clinic website (Regina Maria), which provided detailed medical articles on important subjects of immediate interest to the readers and patients, like immunity, the emergent treating protocols or the new Omicron variant of the virus. The corpus dataset was manually collected and revised. Data were checked for grammatical correctness, and missing diacritics were introduced. <br><br> The corpus is structured in 55 UTF-8 documents and contains 147,297 words. Results ----------------- | MLM Task | Perplexity | | ------------- | ------------- | | Baseline | 5.13 | | COVID Fine-tuning| 2.74 |

cf9bc0b7d216501f87651daf1797ec43

huggingnft/nftrex

huggingnft

null

5

19

transformers

1

unconditional-image-generation

false

mit

null

['huggingnft/nftrex']

null

0

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

false

true

2,166

false

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

a2bd66f446d154e09747382c687d806d

jonatasgrosman/exp_w2v2t_en_xls-r_s468

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

459

false

# exp_w2v2t_en_xls-r_s468 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](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.

8d9c38df256e5f0953102f2a39b863c9

google/t5-efficient-small-el8-dl2

google

t5

12

7

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,281

false

# T5-Efficient-SMALL-EL8-DL2 (Deep-Narrow version) T5-Efficient-SMALL-EL8-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-el8-dl2** - is of model type **Small** with the following variations: - **el** is **8** - **dl** is **2** It has **50.03** million parameters and thus requires *ca.* **200.11 MB** of memory in full precision (*fp32*) or **100.05 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.

f2d7609c090e479af8fcd242783b441f

anuragshas/wav2vec2-large-xlsr-53-rm-vallader

anuragshas

wav2vec2

9

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['rm-vallader']

['common_voice']

null

0

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

true

3,527

false

# Wav2Vec2-Large-XLSR-53-Romansh Vallader Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Vallader 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", "rm-vallader", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-rm-vallader") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-rm-vallader") 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 Romansh Vallader 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", "rm-vallader", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-rm-vallader") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-rm-vallader") 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('’ ',' ',batch["sentence"]) batch["sentence"] = re.sub(' ‘',' ',batch["sentence"]) batch["sentence"] = re.sub('’|‘','\'',batch["sentence"]) 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**: 32.89 % ## Training The Common Voice `train` and `validation` datasets were used for training.

90e2f95ff3f3656395b7874e929794db

aapot/wav2vec2-xlsr-1b-finnish-lm

aapot

wav2vec2

21

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fi']

['mozilla-foundation/common_voice_7_0']

null

0

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

true

9,461

false

# Wav2Vec2 XLS-R for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. **Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization. **Note**: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:----------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:| | [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.74 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 5.94 h | 2.29 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.98 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 87.84 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 2.07 % | Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.968 | 0.18 | 500 | 0.4870 | 0.4720 | | 0.6557 | 0.36 | 1000 | 0.2450 | 0.2931 | | 0.647 | 0.54 | 1500 | 0.1818 | 0.2255 | | 0.5297 | 0.72 | 2000 | 0.1698 | 0.2354 | | 0.5802 | 0.9 | 2500 | 0.1581 | 0.2355 | | 0.6351 | 1.07 | 3000 | 0.1689 | 0.2336 | | 0.4626 | 1.25 | 3500 | 0.1719 | 0.3099 | | 0.4526 | 1.43 | 4000 | 0.1434 | 0.2069 | | 0.4692 | 1.61 | 4500 | 0.1645 | 0.2192 | | 0.4584 | 1.79 | 5000 | 0.1483 | 0.1987 | | 0.4234 | 1.97 | 5500 | 0.1499 | 0.2178 | | 0.4243 | 2.15 | 6000 | 0.1345 | 0.2070 | | 0.4108 | 2.33 | 6500 | 0.1383 | 0.1850 | | 0.4048 | 2.51 | 7000 | 0.1338 | 0.1811 | | 0.4085 | 2.69 | 7500 | 0.1290 | 0.1780 | | 0.4026 | 2.87 | 8000 | 0.1239 | 0.1650 | | 0.4033 | 3.04 | 8500 | 0.1346 | 0.1657 | | 0.3986 | 3.22 | 9000 | 0.1310 | 0.1850 | | 0.3867 | 3.4 | 9500 | 0.1273 | 0.1741 | | 0.3658 | 3.58 | 10000 | 0.1219 | 0.1672 | | 0.382 | 3.76 | 10500 | 0.1306 | 0.1698 | | 0.3847 | 3.94 | 11000 | 0.1230 | 0.1577 | | 0.3691 | 4.12 | 11500 | 0.1310 | 0.1615 | | 0.3593 | 4.3 | 12000 | 0.1296 | 0.1622 | | 0.3619 | 4.48 | 12500 | 0.1285 | 0.1601 | | 0.3361 | 4.66 | 13000 | 0.1261 | 0.1569 | | 0.3603 | 4.84 | 13500 | 0.1235 | 0.1533 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models: | | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-----------------------------------------|---------------|------------------|---------------|------------------| |aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** | |aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 | |aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 | ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗

d2612bbc04ae5ef76b3e637a86f3d84b

jgammack/roberta-base-squad

jgammack

roberta

15

6

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

947

false

# roberta-base-squad 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

970cd456f7ce21b118ac7fc86adbb360

SEUNGWON1/distilgpt2-finetuned-wikitext2

SEUNGWON1

gpt2

9

4

transformers

0

text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,243

false

# distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.653 | 2.0 | 4668 | 3.6472 | | 3.6006 | 3.0 | 7002 | 3.6421 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

5aa385f2eeb9ea184e37123362c16e6b

garyw/clinical-embeddings-100d-w2v-cr

garyw

null

5

0

null

1

null

false

gpl-3.0

null

0

[]

false

true

1,547

false

Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 100 dimensions and were trained using the word2vec algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/ Citation: ``` @article{flamholz2022word, title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information}, author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E}, journal={Journal of Biomedical Informatics}, volume={125}, pages={103971}, year={2022}, publisher={Elsevier} } ``` ## Quick start Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format. First download the files from this archive. Then load the embeddings into Python. ```python from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models # Load the model model = Word2Vec.load('w2v_oa_cr_100d.bin') # Return 100-dimensional vector representations of each word model.wv.word_vec('diabetes') model.wv.word_vec('cardiac_arrest') model.wv.word_vec('lymphangioleiomyomatosis') # Try out cosine similarity model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease') model.wv.similarity('myocardial_infarction', 'heart_attack') model.wv.similarity('lymphangioleiomyomatosis', 'lam') ```

e3317287015960343723950784a550a8

beyond/genius-large-k2t

beyond

bart

9

4

transformers

0

text2text-generation

true

false

apache-2.0

['en']

['wikipedia']

null

0

['GENIUS', 'conditional text generation', 'sketch-based text generation', 'keywords-to-text generation', 'data augmentation']

false

true

1,710

false

# 💡GENIUS – generating text using sketches! - **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://github.com/beyondguo/genius/blob/master/GENIUS_gby_arxiv.pdf)** 💡**GENIUS** is a powerful conditional text generation model using sketches as input, which can fill in the missing contexts for a given **sketch** (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large- scale textual corpus with a novel *reconstruction from sketch* objective using an *extreme and selective masking* strategy, enabling it to generate diverse and high-quality texts given sketches. ![image-20221119164544165](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/hi-genius.png) - Models hosted in 🤗 Huggingface: **Model variations:** | Model | #params | Language | comment| |------------------------|--------------------------------|-------|---------| | [`genius-large`](https://huggingface.co/beyond/genius-large) | 406M | English | The version used in **paper** (recommend) | | [`genius-large-k2t`](https://huggingface.co/beyond/genius-large-k2t) | 406M | English | keywords-to-text | | [`genius-base`](https://huggingface.co/beyond/genius-base) | 139M | English | smaller version | | [`genius-base-ps`](https://huggingface.co/beyond/genius-base) | 139M | English | pre-trained both in paragraphs and short sentences | | [`genius-base-chinese`](https://huggingface.co/beyond/genius-base-chinese) | 116M | 中文 | 在一千万纯净中文段落上预训练| ![image-20221119191940969](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/202211191919005.png)

b2ef5692dd12757b03402d8a7bce21ee

sd-concepts-library/eastward

sd-concepts-library

null

20

0

null

3

null

false

mit

null

0

[]

false

true

2,162

false

### Eastward on Stable Diffusion This is the `<eastward>` 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`: ![<eastward> 0](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/1.jpeg) ![<eastward> 1](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/11.jpeg) ![<eastward> 2](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/8.jpeg) ![<eastward> 3](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/5.jpeg) ![<eastward> 4](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/9.jpeg) ![<eastward> 5](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/7.jpeg) ![<eastward> 6](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/3.jpeg) ![<eastward> 7](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/2.jpeg) ![<eastward> 8](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/6.jpeg) ![<eastward> 9](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/10.jpeg) ![<eastward> 10](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/0.jpeg) ![<eastward> 11](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/14.jpeg) ![<eastward> 12](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/13.jpeg) ![<eastward> 13](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/4.jpeg) ![<eastward> 14](https://huggingface.co/sd-concepts-library/eastward/resolve/main/concept_images/12.jpeg)

0a2f80e8cbd7757ca3445d9f95583e43

BN87/sample

BN87

null

2

0

null

0

null

false

openrail

null

0

[]

false

true

4,739

false

# Model Card for Model ID  # Model Details ## Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [More Information Needed] # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  [More Information Needed] ### Factors  [More Information Needed] ### Metrics  [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional]  [More Information Needed] # 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:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional]  [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

8c16c88e1125fe4658d8e75ce6d9fbea

jonatasgrosman/exp_w2v2t_pl_vp-es_s840

jonatasgrosman

wav2vec2

10

5

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

469

false

# exp_w2v2t_pl_vp-es_s840 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 (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.

a9dd3fdf9afbf47f3ee7335bfc80d10b

kyo/distilbert-base-uncased-finetuned-imdb

kyo

distilbert

12

4

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,319

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4718 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.707 | 1.0 | 157 | 2.4883 | | 2.572 | 2.0 | 314 | 2.4240 | | 2.5377 | 3.0 | 471 | 2.4355 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

b53e956660a729299fcb42621c1a188f

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

eduardopds

mt5

8

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,651

false

# eduardopds/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0870 - Validation Loss: 3.3925 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, '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 | |:----------:|:---------------:|:-----:| | 9.8646 | 4.3778 | 0 | | 5.9307 | 3.8057 | 1 | | 5.1494 | 3.6458 | 2 | | 4.7430 | 3.5501 | 3 | | 4.4782 | 3.4870 | 4 | | 4.2922 | 3.4339 | 5 | | 4.1536 | 3.4037 | 6 | | 4.0870 | 3.3925 | 7 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1

a5a12122524bd20a37dbe9b117f8141b

huiziy/my_awesome_qa_model

huiziy

distilbert

12

1

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,279

false

# my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an High School Health Science dataset. It achieves the following results on the evaluation set: - Loss: 5.2683 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 5.6569 | | No log | 2.0 | 6 | 5.3967 | | No log | 3.0 | 9 | 5.2683 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

dafdc56c0ad3508daadc75bc4cc28d1c

KoichiYasuoka/roberta-base-vietnamese-ud-goeswith

KoichiYasuoka

roberta

10

4

transformers

0

token-classification

true

false

cc-by-sa-4.0

['vi']

['universal_dependencies']

null

0

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

false

true

2,759

false

# roberta-base-vietnamese-ud-goeswith ## Model Description This is a RoBERTa model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-vietnamese-upos](https://huggingface.co/KoichiYasuoka/roberta-base-vietnamese-upos). ## How to Use ```py class UDgoeswith(object): def __init__(self,bert): from transformers import AutoTokenizer,AutoModelForTokenClassification self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForTokenClassification.from_pretrained(bert) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=self.tokenizer(text,return_offsets_mapping=True) v=w["input_ids"] x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)] with torch.no_grad(): e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:] r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())] e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan) g=self.model.config.label2id["X|_|goeswith"] r=numpy.tri(e.shape[0]) for i in range(e.shape[0]): for j in range(i+2,e.shape[1]): r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1 e[:,:,g]+=numpy.where(r==0,0,numpy.nan) m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan) m[1:,1:]=numpy.nanmax(e,axis=2).transpose() p=numpy.zeros(m.shape) p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() for i in range(1,m.shape[0]): m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan) m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u="# text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-base-vietnamese-ud-goeswith") print(nlp("Hai cái đầu thì tốt hơn một.")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-vietnamese-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("Hai cái đầu thì tốt hơn một.")) ```

62a24c63e77528313450ebf9ce8c82f3

IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese

IDEA-CCNL

bert

5

34

transformers

1

null

true

false

apache-2.0

['zh']

null

0

['classification']

false

true

8,644

false

# Erlangshen-TCBert-1.3B-Classification-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 1.3BM参数的Topic Classification BERT (TCBert)。 The TCBert with 1.3BM parameters is pre-trained for, not limited to, Chinese topic classification tasks. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | TCBert | 1.3BM | Chinese | ## 模型信息 Model Information 为了提高模型在话题分类上的效果，我们收集了大量话题分类数据进行基于prompts的预训练。 To improve the model performance on the topic classification task, we collected numerous topic classification datasets for pre-training based on general prompts. ### 下游效果 Performance 我们为每个数据集设计了两个prompt模板。 We customize two prompts templates for each dataset. 第一个prompt模板： For ***prompt template 1***: | Dataset | Prompt template 1 | |---------|:------------------------:| | TNEWS | 下面是一则关于__的新闻： | | CSLDCP | 这一句描述__的内容如下： | | IFLYTEK | 这一句描述__的内容如下： | 第一个prompt模板的微调实验结果： The **fine-tuning** results for prompt template 1: | Model | TNEWS | CLSDCP | IFLYTEK | |-----------------|:------:|:------:|:-------:| | Macbert-base | 55.02 | 57.37 | 51.34 | | Macbert-large | 55.77 | 58.99 | 50.31 | | Erlangshen-1.3B | 57.36 | 62.35 | 53.23 | | TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 | | TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 | | TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 | | TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 | | TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 | | TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 | 第一个prompt模板的句子相似度结果： The **sentence similarity** results for prompt template 1: | | TNEWS | | CSLDCP | | IFLYTEK | | |-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:| | Model | referece | whitening | reference | whitening | reference | whitening | | Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 | | Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 | | Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 | | TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 | | TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 | | TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 | | TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 | | TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 | | TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 | 第二个prompt模板： For ***prompt template 2***: | Dataset | Prompt template 2 | |---------|:------------------------:| | TNEWS | 接下来的新闻，是跟__相关的内容： | | CSLDCP | 接下来的学科，是跟__相关： | | IFLYTEK | 接下来的生活内容，是跟__相关： | 第二个prompt模板的微调结果： The **fine-tuning** results for prompt template 2: | Model | TNEWS | CLSDCP | IFLYTEK | |-----------------|:------:|:------:|:-------:| | Macbert-base | 54.78 | 58.38 | 50.83 | | Macbert-large | 56.77 | 60.22 | 51.63 | | Erlangshen-1.3B | 57.81 | 62.80 | 52.77 | | TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 | | TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 | | TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 | | TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 | | TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 | | TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 | 第二个prompt模板的句子相似度结果： The **sentence similarity** results for prompt template 2: | | TNEWS | | CSLDCP | | IFLYTEK | | |-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:| | Model | referece | whitening | reference | whitening | reference | whitening | | Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 | | Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 | | Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 | | TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 | | TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 | | TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 | | TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 | | TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 | | TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 | 更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。 For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo! ## 使用 Usage ### 使用示例 Usage Examples ```python # Prompt-based MLM fine-tuning from transformers import BertForMaskedLM, BertTokenizer import torch # Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese") # Prepare the data inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt") labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"] labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100) # Output the loss outputs = model(**inputs, labels=labels) loss = outputs.loss ``` ```python # Prompt-based Sentence Similarity # To extract sentence representations. from transformers import BertForMaskedLM, BertTokenizer import torch # Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Classification-Chinese") # Cosine similarity function cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8) with torch.no_grad(): # To extract sentence representations for training data training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt") training_output = BertForMaskedLM(**token_text, output_hidden_states=True) training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0) # To extract sentence representations for training data test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt") test_output = BertForMaskedLM(**token_text, output_hidden_states=True) test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0) # Calculate similarity scores similarity_score = cos(training_input, test_input) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304): If you use for your work, please cite the following paper ``` @article{han2022tcbert, title={TCBERT: A Technical Report for Chinese Topic Classification BERT}, author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing}, journal={arXiv preprint arXiv:2211.11304}, year={2022} } ``` 如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

b5a6bc8e567dd7a5a19190c181f37a44

moussaKam/barthez-sentiment-classification

moussaKam

mbart

6

1,792

transformers

2

text-classification

true

false

apache-2.0

['fr']

null

0

['text-classification', 'bart']

false

true

405

false

### Barthez model finetuned on opinion classification task. paper: https://arxiv.org/abs/2010.12321 \ github: https://github.com/moussaKam/BARThez ``` @article{eddine2020barthez, title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model}, author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis}, journal={arXiv preprint arXiv:2010.12321}, year={2020} } ```

16d2dbc7628a380ea91baddf4718fc9d

EIStakovskii/bert-base-german-cased_fluency

EIStakovskii

bert

8

3

transformers

0

text-classification

true

false

other

['de']

['news_commentary']

null

0

[]

false

true

895

false

This model was trained for evaluating linguistic acceptability and grammaticality. The finetuning was carried out based off [the bert-base-german-cased](https://huggingface.co/bert-base-german-cased). Label_1 means ACCEPTABLE - the sentence is perfectly understandable by native speakers and has no serious grammatic and syntactic flaws. Label_0 means NOT ACCEPTABLE - the sentence is flawed both orthographically and grammatically. The model was trained on 50 thousand German sentences from [the news_commentary dataset](https://huggingface.co/datasets/news_commentary). Out of 50 thousand 25 thousand sentences were algorithmically corrupted using [the open source Python library](https://github.com/eistakovskii/text_corruption_plus). The library was originally developed by [aylliote](https://github.com/aylliote/corruption), but it was slightly adapted for the purposes of this model.

ff1df663a37a7163ffa95b01b937802d

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

dperezjr

wav2vec2

13

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,791

false

# wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3783 - Wer: 0.3036 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0054 | 3.67 | 400 | 0.7096 | 0.6999 | | 0.4061 | 7.34 | 800 | 0.4152 | 0.4637 | | 0.1797 | 11.01 | 1200 | 0.4008 | 0.4164 | | 0.1201 | 14.68 | 1600 | 0.4275 | 0.4152 | | 0.0937 | 18.35 | 2000 | 0.4297 | 0.3978 | | 0.074 | 22.02 | 2400 | 0.3670 | 0.3618 | | 0.0602 | 25.69 | 2800 | 0.3875 | 0.3129 | | 0.0472 | 29.36 | 3200 | 0.3783 | 0.3036 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

d0931be344cbe0069767cff0679443b6

chcaa/xls-r-300m-danish

chcaa

wav2vec2

5

48

transformers

4

null

true

false

apache-2.0

['da']

null

0

['speech', 'xls_r', 'xls_r_pretrained', 'danish']

false

true

724

false

## XLS-R-300m-danish Continued pretraining of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for 120.000 steps on 141.000 hours of speech from Danish radio (DR P1 and Radio24Syv from 2005 to 2021). The model was pretrained on 16kHz audio using fairseq and should be fine-tuned to perform speech recognition. A fine-tuned version of this model for ASR can be found [here](https://huggingface.co/chcaa/xls-r-300m-danish-nst-cv9). The model was trained by [Lasse Hansen](https://github.com/HLasse) ([CHCAA](https://chcaa.io)) and [Alvenir](https://alvenir.ai) on the [UCloud](https:/cloud.sdu.dk) platform. Many thanks to the Royal Danish Library for providing access to the data.

bdfaf4e339a732168646125fa3ca7ff8

espnet/kan-bayashi_jsut_transformer_accent_with_pause

espnet

null

19

4

espnet

0

text-to-speech

false

cc-by-4.0

['ja']

['jsut']

null

0

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

false

true

1,814

false

## Example ESPnet2 TTS model ### `kan-bayashi/jsut_transformer_accent_with_pause` ♻️ Imported from https://zenodo.org/record/4433196/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

a1c73951a17514e6838f04cbca221652

qsnell/distilbert-base-uncased-finetuned-emotion

qsnell

distilbert

30

7

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,342

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: 0.1560 - Accuracy: 0.94 - F1: 0.9403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 1000 | 0.2056 | 0.928 | 0.9284 | | 0.3151 | 2.0 | 2000 | 0.1560 | 0.94 | 0.9403 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.10.2+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

5d1df4ecfffa22f69985ac63ae64ae70

google/bigbird-roberta-base

google

big_bird

8

23,736

transformers

26

null

true

false

true

apache-2.0

['en']

['bookcorpus', 'wikipedia', 'cc_news']

null

0

[]

false

true

2,765

false

# BigBird base model BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird). Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. ## How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BigBirdModel # by default its in `block_sparse` mode with num_random_blocks=3, block_size=64 model = BigBirdModel.from_pretrained("google/bigbird-roberta-base") # you can change `attention_type` to full attention like this: model = BigBirdModel.from_pretrained("google/bigbird-roberta-base", attention_type="original_full") # you can change `block_size` & `num_random_blocks` like this: model = BigBirdModel.from_pretrained("google/bigbird-roberta-base", block_size=16, num_random_blocks=2) text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Training Data This model is pre-trained on four publicly available datasets: **Books**, **CC-News**, **Stories** and **Wikipedia**. It used same sentencepiece vocabulary as RoBERTa (which is in turn borrowed from GPT2). ## Training Procedure Document longer than 4096 were split into multiple documents and documents that were much smaller than 4096 were joined. Following the original BERT training, 15% of tokens were masked and model is trained to predict the mask. Model is warm started from RoBERTa’s checkpoint. ## BibTeX entry and citation info ```tex @misc{zaheer2021big, title={Big Bird: Transformers for Longer Sequences}, author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed}, year={2021}, eprint={2007.14062}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

542ab2457e809e5b3b07c6c59156eb35

sd-concepts-library/aadhav-face

sd-concepts-library

null

9

0

null

0

null

false

mit

null

0

[]

false

true

1,040

false

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

0e3d5a2fdb959f6c1966db6b2774304d

npc-engine/t5-base-mse-summarization

npc-engine

t5

18

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,638

false

# t5-base-mse-summarization This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8743 - Rouge1: 45.9597 - Rouge2: 26.8086 - Rougel: 39.935 - Rougelsum: 43.8897 - Bleurt: -0.7132 - Gen Len: 18.464 ## 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: 16 - 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 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleurt | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| | 1.2568 | 1.0 | 267 | 1.0472 | 41.6829 | 21.9654 | 35.4264 | 39.5556 | -0.8231 | 18.522 | | 1.1085 | 2.0 | 534 | 0.9840 | 43.1479 | 23.3351 | 36.9244 | 40.886 | -0.7843 | 18.534 | | 1.0548 | 3.0 | 801 | 0.9515 | 44.1511 | 24.4912 | 37.9549 | 41.9984 | -0.7702 | 18.528 | | 1.0251 | 4.0 | 1068 | 0.9331 | 44.426 | 24.9439 | 38.2978 | 42.1731 | -0.7633 | 18.619 | | 0.9888 | 5.0 | 1335 | 0.9201 | 45.0385 | 25.524 | 38.8681 | 42.8998 | -0.7497 | 18.523 | | 0.9623 | 6.0 | 1602 | 0.9119 | 44.8648 | 25.469 | 38.9281 | 42.7798 | -0.7496 | 18.537 | | 0.9502 | 7.0 | 1869 | 0.9015 | 44.9668 | 25.5041 | 38.9463 | 42.9368 | -0.7412 | 18.48 | | 0.9316 | 8.0 | 2136 | 0.8973 | 45.3028 | 25.7232 | 39.1533 | 43.277 | -0.7318 | 18.523 | | 0.9191 | 9.0 | 2403 | 0.8921 | 45.2901 | 25.916 | 39.2909 | 43.3022 | -0.7296 | 18.529 | | 0.9122 | 10.0 | 2670 | 0.8889 | 45.3535 | 26.1369 | 39.4861 | 43.28 | -0.7271 | 18.545 | | 0.8993 | 11.0 | 2937 | 0.8857 | 45.5345 | 26.1669 | 39.5656 | 43.4664 | -0.7269 | 18.474 | | 0.8905 | 12.0 | 3204 | 0.8816 | 45.7796 | 26.4145 | 39.8117 | 43.734 | -0.7185 | 18.503 | | 0.8821 | 13.0 | 3471 | 0.8794 | 45.7163 | 26.4314 | 39.719 | 43.6407 | -0.7211 | 18.496 | | 0.8789 | 14.0 | 3738 | 0.8784 | 45.9097 | 26.7281 | 39.9071 | 43.8105 | -0.7127 | 18.452 | | 0.8665 | 15.0 | 4005 | 0.8765 | 46.1148 | 26.8882 | 40.1006 | 43.988 | -0.711 | 18.443 | | 0.8676 | 16.0 | 4272 | 0.8766 | 45.9119 | 26.7674 | 39.9001 | 43.8237 | -0.718 | 18.491 | | 0.8637 | 17.0 | 4539 | 0.8758 | 45.9158 | 26.7153 | 39.9463 | 43.8323 | -0.7183 | 18.492 | | 0.8622 | 18.0 | 4806 | 0.8752 | 45.9508 | 26.75 | 39.9533 | 43.8795 | -0.7144 | 18.465 | | 0.8588 | 19.0 | 5073 | 0.8744 | 45.9192 | 26.7352 | 39.8921 | 43.8204 | -0.7148 | 18.462 | | 0.8554 | 20.0 | 5340 | 0.8743 | 45.9597 | 26.8086 | 39.935 | 43.8897 | -0.7132 | 18.464 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

6e07ca32a283a6c1b14d16b34a9a8d7d

sepidmnorozy/sentiment-10Epochs-3

sepidmnorozy

xlm-roberta

9

2

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,170

false

# sentiment-10Epochs-3 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7703 - Accuracy: 0.8568 - F1: 0.8526 - Precision: 0.8787 - Recall: 0.8279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3637 | 1.0 | 7088 | 0.3830 | 0.8571 | 0.8418 | 0.9429 | 0.7603 | | 0.37 | 2.0 | 14176 | 0.4128 | 0.8676 | 0.8582 | 0.9242 | 0.8010 | | 0.325 | 3.0 | 21264 | 0.4656 | 0.8737 | 0.8664 | 0.9189 | 0.8197 | | 0.2948 | 4.0 | 28352 | 0.4575 | 0.8703 | 0.8652 | 0.9007 | 0.8324 | | 0.3068 | 5.0 | 35440 | 0.4751 | 0.8705 | 0.8653 | 0.9016 | 0.8317 | | 0.2945 | 6.0 | 42528 | 0.5509 | 0.8668 | 0.8618 | 0.8956 | 0.8305 | | 0.2568 | 7.0 | 49616 | 0.6201 | 0.8632 | 0.8567 | 0.8994 | 0.8178 | | 0.2107 | 8.0 | 56704 | 0.6836 | 0.8614 | 0.8576 | 0.8819 | 0.8346 | | 0.1966 | 9.0 | 63792 | 0.7030 | 0.8583 | 0.8532 | 0.8848 | 0.8238 | | 0.1675 | 10.0 | 70880 | 0.7703 | 0.8568 | 0.8526 | 0.8787 | 0.8279 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0 - Datasets 2.0.0 - Tokenizers 0.11.6

2312fe41ef93bdd7a9470920174a643c

henryscheible/eval_masked_v4_mrpc

henryscheible

null

13

0

null

0

null

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,048

false

# eval_masked_v4_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6346 - Accuracy: 0.7941 - F1: 0.8595 - Combined Score: 0.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

8ef90d37700a2e82e6a9507f2c68c064

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_mrpc

gokuls

mobilebert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

3,109

false

# mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_mrpc This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.2291 - Accuracy: 0.8578 - F1: 0.8993 - Combined Score: 0.8786 ## 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 | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.536 | 1.0 | 29 | 0.4134 | 0.7279 | 0.8284 | 0.7782 | | 0.3419 | 2.0 | 58 | 0.3005 | 0.8284 | 0.8801 | 0.8543 | | 0.2413 | 3.0 | 87 | 0.2707 | 0.8235 | 0.8780 | 0.8507 | | 0.1852 | 4.0 | 116 | 0.3247 | 0.8284 | 0.8837 | 0.8561 | | 0.1524 | 5.0 | 145 | 0.2856 | 0.8431 | 0.8900 | 0.8666 | | 0.1297 | 6.0 | 174 | 0.2999 | 0.8456 | 0.8948 | 0.8702 | | 0.1219 | 7.0 | 203 | 0.2797 | 0.8529 | 0.8986 | 0.8758 | | 0.1141 | 8.0 | 232 | 0.2462 | 0.8603 | 0.9005 | 0.8804 | | 0.1127 | 9.0 | 261 | 0.2557 | 0.8578 | 0.8982 | 0.8780 | | 0.1091 | 10.0 | 290 | 0.2853 | 0.8480 | 0.8967 | 0.8724 | | 0.1007 | 11.0 | 319 | 0.2472 | 0.8554 | 0.8981 | 0.8767 | | 0.0979 | 12.0 | 348 | 0.2431 | 0.8505 | 0.8950 | 0.8727 | | 0.0954 | 13.0 | 377 | 0.2456 | 0.8578 | 0.9007 | 0.8793 | | 0.0946 | 14.0 | 406 | 0.2526 | 0.8578 | 0.9017 | 0.8798 | | 0.0946 | 15.0 | 435 | 0.2291 | 0.8578 | 0.8993 | 0.8786 | | 0.0938 | 16.0 | 464 | 0.2452 | 0.8603 | 0.9029 | 0.8816 | | 0.0919 | 17.0 | 493 | 0.2365 | 0.8652 | 0.9050 | 0.8851 | | 0.0916 | 18.0 | 522 | 0.2363 | 0.8652 | 0.9060 | 0.8856 | | 0.0915 | 19.0 | 551 | 0.2432 | 0.8652 | 0.9063 | 0.8857 | | 0.0905 | 20.0 | 580 | 0.2297 | 0.8652 | 0.9057 | 0.8854 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

c16a6beb442224eccd0766866f2ca43c

SkyR/albert-base-ours-run-5

SkyR

albert

9

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,064

false

# albert-base-ours-run-5 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6151 - Accuracy: 0.675 - Precision: 0.6356 - Recall: 0.6360 - F1: 0.6356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9766 | 1.0 | 200 | 0.8865 | 0.645 | 0.5935 | 0.5872 | 0.5881 | | 0.7725 | 2.0 | 400 | 1.0650 | 0.665 | 0.7143 | 0.5936 | 0.5556 | | 0.6018 | 3.0 | 600 | 0.8558 | 0.7 | 0.6637 | 0.6444 | 0.6456 | | 0.3838 | 4.0 | 800 | 0.9796 | 0.67 | 0.6220 | 0.6219 | 0.6218 | | 0.2135 | 5.0 | 1000 | 1.4533 | 0.675 | 0.6611 | 0.5955 | 0.6055 | | 0.1209 | 6.0 | 1200 | 1.4688 | 0.67 | 0.6392 | 0.6474 | 0.6398 | | 0.072 | 7.0 | 1400 | 1.8395 | 0.695 | 0.6574 | 0.6540 | 0.6514 | | 0.0211 | 8.0 | 1600 | 2.0849 | 0.7 | 0.6691 | 0.6607 | 0.6603 | | 0.0102 | 9.0 | 1800 | 2.3042 | 0.695 | 0.6675 | 0.6482 | 0.6533 | | 0.0132 | 10.0 | 2000 | 2.2390 | 0.685 | 0.6472 | 0.6423 | 0.6439 | | 0.004 | 11.0 | 2200 | 2.3779 | 0.68 | 0.6435 | 0.6481 | 0.6443 | | 0.0004 | 12.0 | 2400 | 2.4575 | 0.675 | 0.6397 | 0.6352 | 0.6357 | | 0.0003 | 13.0 | 2600 | 2.4676 | 0.675 | 0.6356 | 0.6360 | 0.6356 | | 0.0003 | 14.0 | 2800 | 2.5109 | 0.68 | 0.6427 | 0.6424 | 0.6422 | | 0.0002 | 15.0 | 3000 | 2.5470 | 0.675 | 0.6356 | 0.6360 | 0.6356 | | 0.0002 | 16.0 | 3200 | 2.5674 | 0.675 | 0.6356 | 0.6360 | 0.6356 | | 0.0001 | 17.0 | 3400 | 2.5889 | 0.685 | 0.6471 | 0.6488 | 0.6474 | | 0.0001 | 18.0 | 3600 | 2.6016 | 0.675 | 0.6356 | 0.6360 | 0.6356 | | 0.0001 | 19.0 | 3800 | 2.6108 | 0.675 | 0.6356 | 0.6360 | 0.6356 | | 0.0001 | 20.0 | 4000 | 2.6151 | 0.675 | 0.6356 | 0.6360 | 0.6356 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Tokenizers 0.13.2

e4b23406c62dde5697691c219891a4b7

Bauyrjan/wav2vec2-kazakh-16K-af

Bauyrjan

wav2vec2

10

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,047

false

# wav2vec2-kazakh-16K-af 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu117 - Datasets 1.13.3 - Tokenizers 0.10.3

e6e57c5309ef596ee15c5bfec75c64c9

scasutt/wav2vec2-large-xlsr-53_toy_train_data

scasutt

wav2vec2

7

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,807

false

# wav2vec2-large-xlsr-53_toy_train_data 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. It achieves the following results on the evaluation set: - Loss: 0.6357 - Wer: 0.5496 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6073 | 2.1 | 250 | 3.5111 | 1.0 | | 3.0828 | 4.2 | 500 | 3.5133 | 1.0 | | 1.9969 | 6.3 | 750 | 1.3924 | 0.9577 | | 0.9279 | 8.4 | 1000 | 0.8378 | 0.7243 | | 0.6692 | 10.5 | 1250 | 0.7367 | 0.6394 | | 0.5273 | 12.6 | 1500 | 0.6703 | 0.5907 | | 0.4314 | 14.7 | 1750 | 0.6594 | 0.5718 | | 0.3809 | 16.8 | 2000 | 0.6138 | 0.5559 | | 0.3934 | 18.9 | 2250 | 0.6357 | 0.5496 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

c8405a6c6d11908c0f42846b93a219bb

hugosousa/es_tei2go

hugosousa

null

14

1

spacy

0

token-classification

false

mit

['es']

null

0

['spacy', 'token-classification']

true

800

false

| Feature | Description | | --- |-----------------------------------------| | **Name** | `es_tei2go` | | **Version** | `0.0.0` | | **spaCy** | `>=3.2.4,<3.3.0` | | **Default Pipeline** | `ner` | | **Components** | `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | MIT | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (1 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `TIMEX` | </details>

74c3dae5ee0e1a8785029e619f633540

jimbung/bert-finetuned-ner

jimbung

bert

12

1

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,518

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9302 - Recall: 0.9493 - F1: 0.9397 - Accuracy: 0.9863 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0878 | 1.0 | 1756 | 0.0657 | 0.9247 | 0.9340 | 0.9293 | 0.9828 | | 0.0343 | 2.0 | 3512 | 0.0627 | 0.9291 | 0.9498 | 0.9393 | 0.9862 | | 0.018 | 3.0 | 5268 | 0.0616 | 0.9302 | 0.9493 | 0.9397 | 0.9863 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

04ca05686ea0b7eb5f8532071fe10446

domenicrosati/deberta-v3-large-finetuned-synthetic-generated-only

domenicrosati

deberta-v2

13

1

transformers

0

text-classification

true

false

mit

null

0

['text-classification', 'generated_from_trainer']

true

1,566

false

# deberta-v3-large-finetuned-synthetic-generated-only This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0094 - F1: 0.9839 - Precision: 0.9849 - Recall: 0.9828 ## 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: 6e-06 - 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: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:| | 0.009 | 1.0 | 10387 | 0.0104 | 0.9722 | 0.9919 | 0.9533 | | 0.0013 | 2.0 | 20774 | 0.0067 | 0.9825 | 0.9844 | 0.9805 | | 0.0006 | 3.0 | 31161 | 0.0077 | 0.9843 | 0.9902 | 0.9786 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

f017d24faf76ee343869b0108120f3db

KoichiYasuoka/roberta-large-japanese-char-luw-upos

KoichiYasuoka

roberta

9

12

transformers

0

token-classification

true

false

cc-by-sa-4.0

['ja']

['universal_dependencies']

null

0

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

false

true

1,413

false

# roberta-large-japanese-char-luw-upos ## Model Description This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-large-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-char). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/). ## How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-char-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-large-japanese-char-luw-upos") pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/roberta-large-japanese-char-luw-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` ## Reference 安岡孝一: [Transformersと国語研長単位による日本語係り受け解析モデルの製作](http://id.nii.ac.jp/1001/00216223/), 情報処理学会研究報告, Vol.2022-CH-128, No.7 (2022年2月), pp.1-8. ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

1a749067e210f104e0970df0d7980618

lewtun/bert-base-uncased-finetuned-imdb

lewtun

bert

8

2

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

false

true

1,300

false

# bert-base-uncased-finetuned-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.0284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2244 | 1.0 | 958 | 2.0726 | | 2.1537 | 2.0 | 1916 | 2.0381 | | 2.1183 | 3.0 | 2874 | 2.0284 | ### Framework versions - Transformers 4.10.3 - Pytorch 1.9.1+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3

f1cdab297b6b3cf6cbcca28e440010ee

lapix/segformer-b3-finetuned-ccagt-400-300

lapix

segformer

7

20

transformers

2

image-segmentation

true

false

other

null

['lapix/CCAgT']

null

0

['vision', 'image-segmentation']

false

true

3,432

false

# SegFormer (b3-sized) model fine-tuned on CCAgT dataset SegFormer model fine-tuned on CCAgT dataset at resolution 400x300. It was introduced in the paper [Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the {AgNOR} Technique](https://doi.org/10.2139/ssrn.4126881) by [J. G. A. Amorim](https://huggingface.co/johnnv) et al. This model was trained in a subset of [CCAgT dataset](https://huggingface.co/datasets/lapix/CCAgT/), so perform a evaluation of this model on the dataset available at HF will differ from the results presented in the paper. For more information about how the model was trained, read the paper. Disclaimer: This model card has been written based on the SegFormer [model card](https://huggingface.co/nvidia/mit-b3/blob/main/README.md) by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes. ## Intended uses & limitations You can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to segment an image of the CCAgT dataset: ```python from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests url = "https://huggingface.co/lapix/segformer-b3-finetuned-ccagt-400-300/resolve/main/sampleB.png" image = Image.open(requests.get(url, stream=True).raw)) model = SegformerForSemanticSegmentation.from_pretrained("lapix/segformer-b3-finetuned-ccagt-400-300") feature_extractor = AutoFeatureExtractor.from_pretrained("lapix/segformer-b3-finetuned-ccagt-400-300") pixel_values = feature_extractor(images=image, return_tensors="pt") outputs = model(pixel_values=pixel_values) logits = outputs.logits # Rescale logits to original image size (400, 300) upsampled_logits = nn.functional.interpolate( logits, size=img.size[::-1], # (height, width) mode="bilinear", align_corners=False, ) segmentation_mask = upsampled_logits.argmax(dim=1)[0] print("Predicted mask:", segmentation_mask) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{AtkinsonSegmentationAgNORSSRN2022, author= {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Fabiana Botelho de Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim}, doi = {10.2139/ssrn.4126881}, url = {https://doi.org/10.2139/ssrn.4126881}, year = {2022}, publisher = {Elsevier {BV}}, title = {Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the {AgNOR} Technique}, journal = {{SSRN} Electronic Journal} } ```

0bec281f2cb4719e359bf693a7b3dc4a

Helsinki-NLP/opus-mt-tc-big-itc-itc

Helsinki-NLP

marian

13

33

transformers

0

translation

true

false

cc-by-4.0

['ast', 'ca', 'es', 'fr', 'gl', 'it', 'lad', 'oc', 'pms', 'pt', 'ro']

null

1

0

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

true

17,972

false

# opus-mt-tc-big-itc-itc ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from Italic languages (itc) to Italic languages (itc). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-08-10 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): ast cat cbk fra fro glg hat ita lad lad_Latn lat lat_Latn lij lld oci pms por ron spa - Target Language(s): ast cat fra gcf glg hat ita lad lad_Latn lat lat_Latn oci por ron spa - Language Pair(s): ast-cat ast-fra ast-glg ast-ita ast-oci ast-por ast-ron ast-spa cat-ast cat-fra cat-glg cat-ita cat-oci cat-por cat-ron cat-spa fra-ast fra-cat fra-glg fra-ita fra-oci fra-por fra-ron fra-spa glg-ast glg-cat glg-fra glg-ita glg-oci glg-por glg-ron glg-spa ita-ast ita-cat ita-fra ita-glg ita-oci ita-por ita-ron ita-spa lad-spa lad_Latn-spa oci-ast oci-cat oci-fra oci-glg oci-ita oci-por oci-ron oci-spa pms-ita por-ast por-cat por-fra por-glg por-ita por-oci por-ron por-spa ron-ast ron-cat ron-fra ron-glg ron-ita ron-oci ron-por ron-spa spa-cat spa-fra spa-glg spa-ita spa-por spa-ron - Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>cbk_Latn<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>frm_Latn<< >>fro<< >>fro_Latn<< >>frp<< >>fur<< >>fur_Latn<< >>gcf<< >>gcf_Latn<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Grek<< >>lat_Latn<< >>lij<< >>lld<< >>lld_Latn<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>osp_Latn<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<< - **Original Model**: [opusTCv20210807_transformer-big_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-itc/opusTCv20210807_transformer-big_2022-08-10.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT itc-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-itc/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>ast<<` ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>fra<< Charras anglés?", ">>fra<< Vull veure't." ] model_name = "pytorch-models/opus-mt-tc-big-itc-itc" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Conversations anglaises ? # Je veux te voir. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-itc") print(pipe(">>fra<< Charras anglés?")) # expected output: Conversations anglaises ? ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-itc/opusTCv20210807_transformer-big_2022-08-10.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-itc/opusTCv20210807_transformer-big_2022-08-10.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-itc/opusTCv20210807_transformer-big_2022-08-10.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | cat-fra | tatoeba-test-v2021-08-07 | 0.71201 | 54.6 | 700 | 5664 | | cat-ita | tatoeba-test-v2021-08-07 | 0.74198 | 58.4 | 298 | 2028 | | cat-por | tatoeba-test-v2021-08-07 | 0.74930 | 57.4 | 747 | 6119 | | cat-spa | tatoeba-test-v2021-08-07 | 0.87844 | 78.1 | 1534 | 12094 | | fra-cat | tatoeba-test-v2021-08-07 | 0.66525 | 46.2 | 700 | 5342 | | fra-ita | tatoeba-test-v2021-08-07 | 0.72742 | 53.8 | 10091 | 62060 | | fra-por | tatoeba-test-v2021-08-07 | 0.68413 | 48.6 | 10518 | 77650 | | fra-ron | tatoeba-test-v2021-08-07 | 0.65009 | 44.0 | 1925 | 12252 | | fra-spa | tatoeba-test-v2021-08-07 | 0.72080 | 54.8 | 10294 | 78406 | | glg-por | tatoeba-test-v2021-08-07 | 0.76720 | 61.1 | 433 | 3105 | | glg-spa | tatoeba-test-v2021-08-07 | 0.82362 | 71.7 | 2121 | 17443 | | ita-cat | tatoeba-test-v2021-08-07 | 0.72529 | 56.4 | 298 | 2109 | | ita-fra | tatoeba-test-v2021-08-07 | 0.77932 | 65.2 | 10091 | 66377 | | ita-por | tatoeba-test-v2021-08-07 | 0.72798 | 54.0 | 3066 | 25668 | | ita-ron | tatoeba-test-v2021-08-07 | 0.70814 | 51.1 | 1005 | 6209 | | ita-spa | tatoeba-test-v2021-08-07 | 0.77455 | 62.9 | 5000 | 34937 | | lad_Latn-spa | tatoeba-test-v2021-08-07 | 0.59363 | 42.6 | 239 | 1239 | | lad-spa | tatoeba-test-v2021-08-07 | 0.52243 | 34.7 | 276 | 1448 | | oci-fra | tatoeba-test-v2021-08-07 | 0.49660 | 29.6 | 806 | 6302 | | pms-ita | tatoeba-test-v2021-08-07 | 0.40221 | 20.0 | 232 | 1721 | | por-cat | tatoeba-test-v2021-08-07 | 0.71146 | 52.2 | 747 | 6149 | | por-fra | tatoeba-test-v2021-08-07 | 0.75565 | 60.9 | 10518 | 80459 | | por-glg | tatoeba-test-v2021-08-07 | 0.75348 | 59.0 | 433 | 3016 | | por-ita | tatoeba-test-v2021-08-07 | 0.76883 | 58.8 | 3066 | 24897 | | por-ron | tatoeba-test-v2021-08-07 | 0.67838 | 46.6 | 681 | 4521 | | por-spa | tatoeba-test-v2021-08-07 | 0.79336 | 64.8 | 10947 | 87335 | | ron-fra | tatoeba-test-v2021-08-07 | 0.70307 | 55.0 | 1925 | 13347 | | ron-ita | tatoeba-test-v2021-08-07 | 0.73862 | 53.7 | 1005 | 6352 | | ron-por | tatoeba-test-v2021-08-07 | 0.70889 | 50.7 | 681 | 4593 | | ron-spa | tatoeba-test-v2021-08-07 | 0.73529 | 57.2 | 1959 | 12679 | | spa-cat | tatoeba-test-v2021-08-07 | 0.82758 | 67.9 | 1534 | 12343 | | spa-fra | tatoeba-test-v2021-08-07 | 0.73113 | 57.3 | 10294 | 83501 | | spa-glg | tatoeba-test-v2021-08-07 | 0.77332 | 63.0 | 2121 | 16581 | | spa-ita | tatoeba-test-v2021-08-07 | 0.77046 | 60.3 | 5000 | 34515 | | spa-lad_Latn | tatoeba-test-v2021-08-07 | 0.40084 | 14.7 | 239 | 1254 | | spa-por | tatoeba-test-v2021-08-07 | 0.75854 | 59.1 | 10947 | 87610 | | spa-ron | tatoeba-test-v2021-08-07 | 0.66679 | 45.5 | 1959 | 12503 | | ast-cat | flores101-devtest | 0.57870 | 31.8 | 1012 | 27304 | | ast-fra | flores101-devtest | 0.56761 | 31.1 | 1012 | 28343 | | ast-glg | flores101-devtest | 0.55161 | 27.9 | 1012 | 26582 | | ast-ita | flores101-devtest | 0.51764 | 22.1 | 1012 | 27306 | | ast-oci | flores101-devtest | 0.49545 | 20.6 | 1012 | 27305 | | ast-por | flores101-devtest | 0.57347 | 31.5 | 1012 | 26519 | | ast-ron | flores101-devtest | 0.52317 | 24.8 | 1012 | 26799 | | ast-spa | flores101-devtest | 0.49741 | 21.2 | 1012 | 29199 | | cat-ast | flores101-devtest | 0.56754 | 24.7 | 1012 | 24572 | | cat-fra | flores101-devtest | 0.63368 | 38.4 | 1012 | 28343 | | cat-glg | flores101-devtest | 0.59596 | 32.2 | 1012 | 26582 | | cat-ita | flores101-devtest | 0.55886 | 26.3 | 1012 | 27306 | | cat-oci | flores101-devtest | 0.54285 | 24.6 | 1012 | 27305 | | cat-por | flores101-devtest | 0.62913 | 37.7 | 1012 | 26519 | | cat-ron | flores101-devtest | 0.56885 | 29.5 | 1012 | 26799 | | cat-spa | flores101-devtest | 0.53372 | 24.6 | 1012 | 29199 | | fra-ast | flores101-devtest | 0.52696 | 20.7 | 1012 | 24572 | | fra-cat | flores101-devtest | 0.60492 | 34.6 | 1012 | 27304 | | fra-glg | flores101-devtest | 0.57485 | 30.3 | 1012 | 26582 | | fra-ita | flores101-devtest | 0.56493 | 27.3 | 1012 | 27306 | | fra-oci | flores101-devtest | 0.57449 | 28.2 | 1012 | 27305 | | fra-por | flores101-devtest | 0.62211 | 36.9 | 1012 | 26519 | | fra-ron | flores101-devtest | 0.56998 | 29.4 | 1012 | 26799 | | fra-spa | flores101-devtest | 0.52880 | 24.2 | 1012 | 29199 | | glg-ast | flores101-devtest | 0.55090 | 22.4 | 1012 | 24572 | | glg-cat | flores101-devtest | 0.60550 | 32.6 | 1012 | 27304 | | glg-fra | flores101-devtest | 0.62026 | 36.0 | 1012 | 28343 | | glg-ita | flores101-devtest | 0.55834 | 25.9 | 1012 | 27306 | | glg-oci | flores101-devtest | 0.52520 | 21.9 | 1012 | 27305 | | glg-por | flores101-devtest | 0.60027 | 32.7 | 1012 | 26519 | | glg-ron | flores101-devtest | 0.55621 | 27.8 | 1012 | 26799 | | glg-spa | flores101-devtest | 0.53219 | 24.4 | 1012 | 29199 | | ita-ast | flores101-devtest | 0.50741 | 17.1 | 1012 | 24572 | | ita-cat | flores101-devtest | 0.57061 | 27.9 | 1012 | 27304 | | ita-fra | flores101-devtest | 0.60199 | 32.0 | 1012 | 28343 | | ita-glg | flores101-devtest | 0.55312 | 25.9 | 1012 | 26582 | | ita-oci | flores101-devtest | 0.48447 | 18.1 | 1012 | 27305 | | ita-por | flores101-devtest | 0.58162 | 29.0 | 1012 | 26519 | | ita-ron | flores101-devtest | 0.53703 | 24.2 | 1012 | 26799 | | ita-spa | flores101-devtest | 0.52238 | 23.1 | 1012 | 29199 | | oci-ast | flores101-devtest | 0.53010 | 20.2 | 1012 | 24572 | | oci-cat | flores101-devtest | 0.59946 | 32.2 | 1012 | 27304 | | oci-fra | flores101-devtest | 0.64290 | 39.0 | 1012 | 28343 | | oci-glg | flores101-devtest | 0.56737 | 28.0 | 1012 | 26582 | | oci-ita | flores101-devtest | 0.54220 | 24.2 | 1012 | 27306 | | oci-por | flores101-devtest | 0.62127 | 35.7 | 1012 | 26519 | | oci-ron | flores101-devtest | 0.55906 | 28.0 | 1012 | 26799 | | oci-spa | flores101-devtest | 0.52110 | 22.8 | 1012 | 29199 | | por-ast | flores101-devtest | 0.54539 | 22.5 | 1012 | 24572 | | por-cat | flores101-devtest | 0.61809 | 36.4 | 1012 | 27304 | | por-fra | flores101-devtest | 0.64343 | 39.7 | 1012 | 28343 | | por-glg | flores101-devtest | 0.57965 | 30.4 | 1012 | 26582 | | por-ita | flores101-devtest | 0.55841 | 26.3 | 1012 | 27306 | | por-oci | flores101-devtest | 0.54829 | 25.3 | 1012 | 27305 | | por-ron | flores101-devtest | 0.57283 | 29.8 | 1012 | 26799 | | por-spa | flores101-devtest | 0.53513 | 25.2 | 1012 | 29199 | | ron-ast | flores101-devtest | 0.52265 | 20.1 | 1012 | 24572 | | ron-cat | flores101-devtest | 0.59689 | 32.6 | 1012 | 27304 | | ron-fra | flores101-devtest | 0.63060 | 37.4 | 1012 | 28343 | | ron-glg | flores101-devtest | 0.56677 | 29.3 | 1012 | 26582 | | ron-ita | flores101-devtest | 0.55485 | 25.6 | 1012 | 27306 | | ron-oci | flores101-devtest | 0.52433 | 21.8 | 1012 | 27305 | | ron-por | flores101-devtest | 0.61831 | 36.1 | 1012 | 26519 | | ron-spa | flores101-devtest | 0.52712 | 24.1 | 1012 | 29199 | | spa-ast | flores101-devtest | 0.49008 | 15.7 | 1012 | 24572 | | spa-cat | flores101-devtest | 0.53905 | 23.2 | 1012 | 27304 | | spa-fra | flores101-devtest | 0.57078 | 27.4 | 1012 | 28343 | | spa-glg | flores101-devtest | 0.52563 | 22.0 | 1012 | 26582 | | spa-ita | flores101-devtest | 0.52783 | 22.3 | 1012 | 27306 | | spa-oci | flores101-devtest | 0.48064 | 16.3 | 1012 | 27305 | | spa-por | flores101-devtest | 0.55736 | 25.8 | 1012 | 26519 | | spa-ron | flores101-devtest | 0.51623 | 21.4 | 1012 | 26799 | | fra-ita | newssyscomb2009 | 0.60995 | 32.1 | 502 | 11551 | | fra-spa | newssyscomb2009 | 0.60224 | 34.2 | 502 | 12503 | | ita-fra | newssyscomb2009 | 0.61237 | 33.7 | 502 | 12331 | | ita-spa | newssyscomb2009 | 0.60706 | 35.4 | 502 | 12503 | | spa-fra | newssyscomb2009 | 0.61290 | 34.6 | 502 | 12331 | | spa-ita | newssyscomb2009 | 0.61632 | 33.3 | 502 | 11551 | | fra-spa | news-test2008 | 0.58939 | 33.9 | 2051 | 52586 | | spa-fra | news-test2008 | 0.58695 | 32.4 | 2051 | 52685 | | fra-ita | newstest2009 | 0.59764 | 31.2 | 2525 | 63466 | | fra-spa | newstest2009 | 0.58829 | 32.5 | 2525 | 68111 | | ita-fra | newstest2009 | 0.59084 | 31.6 | 2525 | 69263 | | ita-spa | newstest2009 | 0.59669 | 33.5 | 2525 | 68111 | | spa-fra | newstest2009 | 0.59096 | 32.3 | 2525 | 69263 | | spa-ita | newstest2009 | 0.60783 | 33.2 | 2525 | 63466 | | fra-spa | newstest2010 | 0.62250 | 37.8 | 2489 | 65480 | | spa-fra | newstest2010 | 0.61953 | 36.2 | 2489 | 66022 | | fra-spa | newstest2011 | 0.62953 | 39.8 | 3003 | 79476 | | spa-fra | newstest2011 | 0.61130 | 34.9 | 3003 | 80626 | | fra-spa | newstest2012 | 0.62397 | 39.0 | 3003 | 79006 | | spa-fra | newstest2012 | 0.60927 | 34.3 | 3003 | 78011 | | fra-spa | newstest2013 | 0.59312 | 34.9 | 3000 | 70528 | | spa-fra | newstest2013 | 0.59468 | 33.6 | 3000 | 70037 | | cat-ita | wmt21-ml-wp | 0.69968 | 47.8 | 1743 | 42735 | | cat-oci | wmt21-ml-wp | 0.73808 | 51.6 | 1743 | 43736 | | cat-ron | wmt21-ml-wp | 0.51178 | 29.0 | 1743 | 42895 | | ita-cat | wmt21-ml-wp | 0.70538 | 48.9 | 1743 | 43833 | | ita-oci | wmt21-ml-wp | 0.59025 | 32.0 | 1743 | 43736 | | ita-ron | wmt21-ml-wp | 0.51261 | 28.9 | 1743 | 42895 | | oci-cat | wmt21-ml-wp | 0.80908 | 66.1 | 1743 | 43833 | | oci-ita | wmt21-ml-wp | 0.63584 | 39.6 | 1743 | 42735 | | oci-ron | wmt21-ml-wp | 0.47384 | 24.6 | 1743 | 42895 | | ron-cat | wmt21-ml-wp | 0.52994 | 31.1 | 1743 | 43833 | | ron-ita | wmt21-ml-wp | 0.52714 | 29.6 | 1743 | 42735 | | ron-oci | wmt21-ml-wp | 0.45932 | 21.3 | 1743 | 43736 | ## Citation Information * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 8b9f0b0 * port time: Fri Aug 12 23:57:49 EEST 2022 * port machine: LM0-400-22516.local

c6695efcd40239c9085b0d3ebf6641a3

OFA-Sys/ofa-huge-vqa

OFA-Sys

ofa

6

17

transformers

3

null

true

false

apache-2.0

null

0

[]

false

true

2,932

false

# OFA-huge-vqa ## Introduction This is the **huge** version of OFA model finetuned for **VQA**. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework. The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. ## How to use To use it in transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers. Install the transformers and download the models as shown below. ``` git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git pip install OFA/transformers/ git clone https://huggingface.co/OFA-Sys/OFA-huge-vqa ``` After, refer the path to OFA-large to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. ```python >>> from PIL import Image >>> from torchvision import transforms >>> from transformers import OFATokenizer, OFAModel >>> from generate import sequence_generator >>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] >>> resolution = 480 >>> patch_resize_transform = transforms.Compose([ lambda image: image.convert("RGB"), transforms.Resize((resolution, resolution), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=mean, std=std) ]) >>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir) >>> txt = " what does the image describe?"# or any of your specified questions >>> inputs = tokenizer([txt], return_tensors="pt").input_ids >>> img = Image.open(path_to_image) >>> patch_img = patch_resize_transform(img).unsqueeze(0) # using the generator of fairseq version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=True) >>> generator = sequence_generator.SequenceGenerator( tokenizer=tokenizer, beam_size=5, max_len_b=16, min_len=0, no_repeat_ngram_size=3, ) >>> data = {} >>> data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])} >>> gen_output = generator.generate([model], data) >>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))] # using the generator of huggingface version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False) >>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) >>> print(tokenizer.batch_decode(gen, skip_special_tokens=True)) ```

c57447b757bbc8f610e44cde49a9db75

facebook/wav2vec2-large-xlsr-53-french

facebook

wav2vec2

9

16,985

transformers

9

automatic-speech-recognition

true

false

true

apache-2.0

['fr']

['common_voice']

null

0

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

false

true

1,730

false

## Evaluation on Common Voice FR Test ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys model_name = "facebook/wav2vec2-large-xlsr-53-french" device = "cuda" chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' # noqa: W605 model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "fr", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") return batch ds = ds.map(map_to_array) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] return batch result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` **Result**: 25.2 %

0793203133e7a4bf5020c7adc9c2c492

rsuwaileh/IDRISI-LMR-EN-timebased-typebased

rsuwaileh

bert

8

7

transformers

0

token-classification

true

false

apache-2.0

null

0

[]

false

true

2,757

false

This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained (e.g., street, POI, etc.) The model is trained using the training splits of all events from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the `Type-based` LMR mode and using the `Time-based` version of the data. You can download this data in `BILOU` format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/). More details about the models are available [here](https://github.com/rsuwaileh/IDRISI/tree/main/models). * Different variants of the model are available through HuggingFace: - [rsuwaileh/IDRISI-LMR-EN-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typeless/) - [rsuwaileh/IDRISI-LMR-EN-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typebased/) - [rsuwaileh/IDRISI-LMR-EN-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typeless/) * Arabic models are also available: - [rsuwaileh/IDRISI-LMR-AR-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-AR-random-typeless/) - [rsuwaileh/IDRISI-LMR-AR-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-AR-random-typebased/) - [rsuwaileh/IDRISI-LMR-AR-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-AR-timebased-typeless/) - [rsuwaileh/IDRISI-LMR-AR-timebased-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-AR-timebased-typebased/) To cite the models: ``` @article{suwaileh2022tlLMR4disaster, title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets}, author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan}, journal={International Journal of Disaster Risk Reduction}, year={2022} } @inproceedings{suwaileh2020tlLMR4disaster, title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets}, author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan}, booktitle={Proceedings of the 28th International Conference on Computational Linguistics}, pages={6252--6263}, year={2020} } ``` To cite the IDRISI-R dataset: ``` @article{rsuwaileh2022Idrisi-r, title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter}, author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad}, journal={...}, volume={...}, pages={...}, year={2022}, publisher={...} } ```

f2528daf36ee5f448ab907589179224b

jonatasgrosman/exp_w2v2r_de_xls-r_gender_male-8_female-2_s293

jonatasgrosman

wav2vec2

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

476

false

# exp_w2v2r_de_xls-r_gender_male-8_female-2_s293 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

9e3a975b9ba0f11590d5bb96be8d3350

dbmdz/electra-base-turkish-mc4-cased-discriminator

dbmdz

electra

8

71

transformers

0

null

true

false

mit

['tr']

['allenai/c4']

null

0

[]

false

true

2,540

false

# 🇹🇷 Turkish ELECTRA model <p align="center"> <img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png"> </p> [![DOI](https://zenodo.org/badge/237817454.svg)](https://zenodo.org/badge/latestdoi/237817454) We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉 Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk. Logo is provided by [Merve Noyan](https://twitter.com/mervenoyann). # Stats We've also trained an ELECTRA (cased) model on the recently released Turkish part of the [multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team. After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting in 31,240,963,926 tokens. We used the original 32k vocab (instead of creating a new one). # mC4 ELECTRA In addition to the ELEC**TR**A base model, we also trained an ELECTRA model on the Turkish part of the mC4 corpus. We use a sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU. # Model usage All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz) using their model name. Example usage with 🤗/Transformers: ```python tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-discriminator") model = AutoModel.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-discriminator") ``` # Citation You can use the following BibTeX entry for citation: ```bibtex @software{stefan_schweter_2020_3770924, author = {Stefan Schweter}, title = {BERTurk - BERT models for Turkish}, month = apr, year = 2020, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3770924}, url = {https://doi.org/10.5281/zenodo.3770924} } ``` # Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. We would like to thank [Merve Noyan](https://twitter.com/mervenoyann) for the awesome logo! Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️

3c6a5a353dfc7aa149c800e01a2ca185

espnet/GunnarThor_talromur_g_tacotron2

espnet

null

18

3

espnet

0

text-to-speech

false

cc-by-4.0

['en']

['talromur']

null

0

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

false

true

6,097

false

## ESPnet2 TTS model ### `espnet/GunnarThor_talromur_g_tacotron2` This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_g_tacotron2 ``` ## TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/train_tacotron2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/g/tts_train_tacotron2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 39151 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 500 batch_size: 20 valid_batch_size: null batch_bins: 2560000 valid_batch_bins: null train_shape_file: - exp/g/tts_stats_raw_phn_none/train/text_shape.phn - exp/g/tts_stats_raw_phn_none/train/speech_shape valid_shape_file: - exp/g/tts_stats_raw_phn_none/valid/text_shape.phn - exp/g/tts_stats_raw_phn_none/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_g_phn/text - text - text - - dump/raw/train_g_phn/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev_g_phn/text - text - text - - dump/raw/dev_g_phn/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-06 weight_decay: 0.0 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ',' - . - r - t - n - a0 - s - I0 - D - l - Y0 - m - v - h - E1 - k - a:1 - E:1 - f - G - j - T - a1 - p - c - au:1 - i:1 - O:1 - I:1 - E0 - I1 - r_0 - t_h - k_h - Y1 - ei1 - i0 - ou:1 - ei:1 - u:1 - O1 - N - l_0 - '91' - ai0 - au1 - ou0 - n_0 - ei0 - O0 - ou1 - ai:1 - '9:1' - ai1 - i1 - '90' - au0 - c_h - x - 9i:1 - C - p_h - u0 - Y:1 - J - 9i1 - u1 - 9i0 - N_0 - m_0 - J_0 - Oi1 - Yi0 - Yi1 - Oi0 - au:0 - '9:0' - E:0 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/g/tts_stats_raw_phn_none/train/feats_stats.npz tts: tacotron2 tts_conf: embed_dim: 512 elayers: 1 eunits: 512 econv_layers: 3 econv_chans: 512 econv_filts: 5 atype: location adim: 512 aconv_chans: 32 aconv_filts: 15 cumulate_att_w: true dlayers: 2 dunits: 1024 prenet_layers: 2 prenet_units: 256 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 output_activation: null use_batch_norm: true use_concate: true use_residual: false dropout_rate: 0.5 zoneout_rate: 0.1 reduction_factor: 1 spk_embed_dim: null use_masking: true bce_pos_weight: 5.0 use_guided_attn_loss: true guided_attn_loss_sigma: 0.4 guided_attn_loss_lambda: 1.0 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: 0.10.7a1 distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

82fadb5a8435156bb2efeb5c36bcdd99

Narshion/mWACH_mBERT_System

Narshion

bert

9

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,001

false

# model This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on mWACH NEO dataset. It achieves the following results on the evaluation set: - Loss: 1.6344 ## 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: 5.0 ### Training results ### Framework versions - Transformers 4.12.4 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

8d20c9bf3e603c33335a7f8a54d8aa27

shahriarebrampour/distilbert-base-uncased-finetuned-imdb

shahriarebrampour

distilbert

15

3

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4303 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5274 | 1.0 | 157 | 2.4476 | | 2.5259 | 2.0 | 314 | 2.4390 | | 2.5134 | 3.0 | 471 | 2.4330 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

259d486bd75f25f54bc9620507e8c556

DeadBeast/mbert-base-cased-finetuned-bengali-fakenews

DeadBeast

bert

8

6

transformers

1

text-classification

true

false

apache-2.0

['bengali']

['BanFakeNews']

null

1

0

[]

false

true

804

false

# **mBERT-base-cased-finetuned-bengali-fakenews** This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set. ### **How to use?** **Task**: binary-classification - LABEL_1: Authentic (*Authentic means news is authentic*) - LABEL_0: Fake (*Fake means news is fake*) ``` from transformers import pipeline print(pipeline("sentiment-analysis",model="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews",tokenizer="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews")("অভিনেতা আফজাল শরীফকে ২০ লাখ টাকার অনুদান অসুস্থ অভিনেতা আফজাল শরীফকে চিকিৎসার জন্য ২০ লাখ টাকা অনুদান দিয়েছেন প্রধানমন্ত্রী শেখ হাসিনা।")) ```

d5bed2918df870de0a9dbb3dca27c05c

AlekseyKorshuk/dalio-all-io-1.3b

AlekseyKorshuk

opt

13

5

transformers

0

text-generation

true

false

other

null

['AlekseyKorshuk/dalio-all-io']

null

0

['generated_from_trainer']

true

3,062

false

# dalio-all-io-1.3b This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-all-io dataset. It achieves the following results on the evaluation set: - Loss: 2.3652 - Accuracy: 0.0558 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6543 | 0.03 | 1 | 2.6113 | 0.0513 | | 2.6077 | 0.07 | 2 | 2.6113 | 0.0513 | | 2.5964 | 0.1 | 3 | 2.5605 | 0.0519 | | 2.7302 | 0.14 | 4 | 2.5234 | 0.0527 | | 2.7 | 0.17 | 5 | 2.5078 | 0.0528 | | 2.5674 | 0.21 | 6 | 2.4941 | 0.0532 | | 2.6406 | 0.24 | 7 | 2.4883 | 0.0534 | | 2.5315 | 0.28 | 8 | 2.4805 | 0.0536 | | 2.7202 | 0.31 | 9 | 2.4727 | 0.0537 | | 2.5144 | 0.34 | 10 | 2.4648 | 0.0536 | | 2.4983 | 0.38 | 11 | 2.4512 | 0.0537 | | 2.7029 | 0.41 | 12 | 2.4414 | 0.0539 | | 2.5198 | 0.45 | 13 | 2.4336 | 0.0540 | | 2.5706 | 0.48 | 14 | 2.4258 | 0.0545 | | 2.5688 | 0.52 | 15 | 2.4180 | 0.0548 | | 2.3793 | 0.55 | 16 | 2.4102 | 0.0552 | | 2.4785 | 0.59 | 17 | 2.4043 | 0.0554 | | 2.4688 | 0.62 | 18 | 2.3984 | 0.0553 | | 2.5674 | 0.66 | 19 | 2.3984 | 0.0553 | | 2.5054 | 0.69 | 20 | 2.3945 | 0.0554 | | 2.452 | 0.72 | 21 | 2.3887 | 0.0555 | | 2.5999 | 0.76 | 22 | 2.3828 | 0.0556 | | 2.3665 | 0.79 | 23 | 2.3789 | 0.0556 | | 2.6223 | 0.83 | 24 | 2.375 | 0.0557 | | 2.3562 | 0.86 | 25 | 2.3711 | 0.0557 | | 2.429 | 0.9 | 26 | 2.3691 | 0.0557 | | 2.563 | 0.93 | 27 | 2.3672 | 0.0558 | | 2.4573 | 0.97 | 28 | 2.3652 | 0.0558 | | 2.4883 | 1.0 | 29 | 2.3652 | 0.0558 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

3868877e1bc69347bd974c551b64a03f

theojolliffe/bart-large-cnn-finetuned-roundup-3-1

theojolliffe

bart

13

3

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,293

false

# bart-large-cnn-finetuned-roundup-3-1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 258 | 1.3238 | 50.228 | 29.5898 | 30.1054 | 47.1265 | 142.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

35ecae7a28341de9a06dee1ab5d35ab0

sd-concepts-library/smurf-style

sd-concepts-library

null

15

0

null

3

null

false

mit

null

0

[]

false

true

1,643

false

### Smurf Style on Stable Diffusion This is the `<smurfy>` 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`: ![<smurfy> 0](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/6.jpeg) ![<smurfy> 1](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/2.jpeg) ![<smurfy> 2](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/0.jpeg) ![<smurfy> 3](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/8.jpeg) ![<smurfy> 4](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/3.jpeg) ![<smurfy> 5](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/5.jpeg) ![<smurfy> 6](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/4.jpeg) ![<smurfy> 7](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/9.jpeg) ![<smurfy> 8](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/1.jpeg) ![<smurfy> 9](https://huggingface.co/sd-concepts-library/smurf-style/resolve/main/concept_images/7.jpeg)

974909a98ef7f26509972b9fbed3f00e

jonatasgrosman/exp_w2v2t_uk_wav2vec2_s646

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['uk']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

456

false

# exp_w2v2t_uk_wav2vec2_s646 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 (uk)](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.

04b3b3d60a8e440d5ac583e30e88516e

MohamedRashad/diffusion_fashion

MohamedRashad

null

17

268

diffusers

4

text-to-image

false

openrail

['en']

null

1

0

1

0

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

false

true

878

false

# Stable Diffusion fine-tuned for [Fashion Product Images Dataset](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset) This model is a fine-tuned version of [openjourney](https://huggingface.co/prompthero/openjourney) that is based on Stable Diffusion targeting fashion and clothing. ## How to use ? ```python from diffusers import StableDiffusionPipeline import torch pipeline = StableDiffusionPipeline.from_pretrained("MohamedRashad/diffusion_fashion", torch_dtype=torch.float16) pipeline.to("cuda") prompt = "A photo of a dress, made in 2019, color is Red, Casual usage, Women's cloth, something for the summer season, on white background" images = pipeline(prompt).images[0] image.save("red_dress.png") ``` ## Any feedback or questions are welcomed on the [community](https://huggingface.co/MohamedRashad/diffusion_fashion/discussions) tab

e1a31e041e803f5cc9bc9728e8f98615

stopdoingmath/opus-mt-sla-en-finetuned-uk-to-en

stopdoingmath

marian

13

2

transformers

0

text2text-generation

true

false

apache-2.0

null

['opus100']

null

0

['generated_from_trainer']

true

1,318

false

# opus-mt-sla-en-finetuned-uk-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Helsinki-NLP/opus-mt-sla-en) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 1.7232 - Bleu: 27.7684 - Gen Len: 12.2485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.5284 | 1.0 | 62500 | 1.7232 | 27.7684 | 12.2485 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

158e74da4b9e02f21fc7bdf82f10bc82

ufal/byt5-small-multilexnorm2021-nl

ufal

t5

6

4

transformers

0

text2text-generation

true

false

apache-2.0

['nl']

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

null

0

['lexical normalization']

false

true

2,757

false

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

7645032b3257faa5d78cbf841cc1f828

Rocketknight1/bert-base-cased-finetuned-wikitext2

Rocketknight1

bert

22

7

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,248

false

# Rocketknight1/bert-base-cased-finetuned-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.3982 - Validation Loss: 6.2664 - 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': 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 | Epoch | |:----------:|:---------------:|:-----:| | 7.0679 | 6.4768 | 0 | | 6.3982 | 6.2664 | 1 | ### Framework versions - Transformers 4.21.0.dev0 - TensorFlow 2.9.1 - Datasets 2.3.3.dev0 - Tokenizers 0.11.0

dede3f9d1face4030ba314f44722da0b

Jethuestad/dat259-nor-wav2vec2

Jethuestad

wav2vec2

17

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_8_0']

null

0

['generated_from_trainer']

true

1,661

false

# dat259-nor-wav2vec2 This model is a fine-tuned version of [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) on the common_voice_8_0 dataset. It achieves the following results on the evaluation set: - Loss: 10.9446 - Wer: 1.1259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 84.8696 | 1.57 | 5 | 91.5942 | 1.0 | | 62.5471 | 3.29 | 10 | 33.8515 | 1.0068 | | 20.2215 | 4.86 | 15 | 17.4461 | 1.0017 | | 15.2892 | 6.57 | 20 | 13.5454 | 1.0034 | | 12.8086 | 8.29 | 25 | 12.0084 | 1.0408 | | 11.0168 | 9.86 | 30 | 10.9446 | 1.1259 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.10.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

751b3979ea8a68ba2cbef3399ae8df69

lmqg/mt5-base-esquad-qg

lmqg

mt5

20

101

transformers

0

text2text-generation

true

false

cc-by-4.0

['es']

['lmqg/qg_esquad']

null

0

['question generation']

true

6,392

false

# Model Card of `lmqg/mt5-base-esquad-qg` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** es - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="es", model="lmqg/mt5-base-esquad-qg") # model prediction questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qg") output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 84.47 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 26.73 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 18.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 13.5 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 10.15 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 23.43 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 59.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 25.45 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 89.68 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (MoverScore) | 64.22 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (BERTScore) | 89.7 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (MoverScore) | 64.24 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (BERTScore) | 89.66 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (MoverScore) | 64.21 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-base-esquad-ae`](https://huggingface.co/lmqg/mt5-base-esquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.lmqg_mt5-base-esquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 80.79 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (MoverScore) | 55.25 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (BERTScore) | 78.45 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (MoverScore) | 53.7 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (BERTScore) | 83.34 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (MoverScore) | 56.99 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 10 - batch: 4 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad-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", } ```

eb9be155dc782e0985f16dc80f6ea2db

nlp04/kobart_32_3e-5_datav2_min30_lp5.0_temperature1.0

nlp04

bart

17

0

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,591

false

# kobart_32_3e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5958 - Rouge1: 35.6403 - Rouge2: 13.1314 - Rougel: 23.8946 - Bleu1: 29.625 - Bleu2: 17.4903 - Bleu3: 10.6018 - Bleu4: 6.0498 - Gen Len: 50.697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 128 - 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: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:------:|:-------:|:-------:|:------:|:-------:| | 1.8239 | 3.78 | 5000 | 2.5958 | 35.6403 | 13.1314 | 23.8946 | 29.625 | 17.4903 | 10.6018 | 6.0498 | 50.697 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

56585eac002f308bacc6c29965ba09e7

XSY/albert-base-v2-fakenews-discriminator

XSY

albert

9

26

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,438

false

# albert-base-v2-fakenews-discriminator The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset I use title and label to train the classifier label_0 : Fake news label_1 : Real news This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0910 - Accuracy: 0.9758 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0452 | 1.0 | 1768 | 0.0910 | 0.9758 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

89b057bc317cc8e6dd17669f1c2c2e28

nvidia/segformer-b0-finetuned-ade-512-512

nvidia

segformer

6

26,404

transformers

29

image-segmentation

true

false

other

null

['scene_parse_150']

null

2

1

0

1

0

['vision', 'image-segmentation']

false

true

2,849

false

# SegFormer (b0-sized) model fine-tuned on ADE20k SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. ## Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) 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 SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

0f75a39da0ea20bb0b5c6f8eac9c191f

p1atdev/pd-archive

p1atdev

null

14

0

null

6

null

false

creativeml-openrail-m

null

1

0

1

0

[]

false

true

778

false

# Plat Diffusion Archive ## v1.3.1 ![v1.3.1](https://s3.amazonaws.com/moonup/production/uploads/1674263031698-6305db1fcfbde33ef7d480ff.png) - [`plat-v1-3-1.safetensors`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-1.safetensors) - [`plat-v1-3-1.ckpt`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-1.ckpt) - [`plat-v1-3-1.yaml`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-1.yaml) ## v1.3.0 ![v1.3.0](https://bit.ly/3Is8sF8) - [`plat-v1-3-0.safetensors`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-0.safetensors) - [`plat-v1-3-0.ckpt`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-0.ckpt) - [`plat-v1-3-0.yaml`](https://huggingface.co/p1atdev/pd-archive/blob/main/plat-v1-3-0.yaml)

fa45c12c0f66eb7c665121bfaf662704

jonatasgrosman/exp_w2v2t_ru_vp-nl_s131

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ru']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_ru_vp-nl_s131 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 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

3a226d10d62a48da3c21f41c0b307c37

mio/hiten

mio

null

7

0

diffusers

4

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

4,108

false

# Hiten Diffusion **Welcome to Hiten Diffusion** - a latent diffusion model that has been trained on Chinese TaiWan Artist artwork, [hiten](https://www.pixiv.net/users/490219). The current model has been fine-tuned with a learning rate of `2.0e-6` for `10 Epochs` on `467 images` collected from Danbooru. The model is trained using [NovelAI Aspect Ratio Bucketing Tool](https://github.com/NovelAI/novelai-aspect-ratio-bucketing) so that it can be trained at non-square resolutions. Like other anime-style Stable Diffusion models, it also supports Danbooru tags to generate images. e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_** ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "mio/hiten" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "1girl,solo,miku" image = pipe(prompt).images[0] image.save("./miku.png") ``` ## Examples Below are some examples of images generated using this model: ![AnimeGirl](https://huggingface.co/mio/hiten/resolve/main/example_images/1.jpeg) ![AnimeGirl](https://huggingface.co/mio/hiten/resolve/main/example_images/2.jpeg) ![AnimeGirl](https://huggingface.co/mio/hiten/resolve/main/example_images/3.jpeg) ![AnimeGirl](https://huggingface.co/mio/hiten/resolve/main/example_images/4.jpeg) ### Prompt and settings for Example Images **Anime Girl:** ``` (((masterpiece))),(((best quality))),((ultra-detailed)), ((illustration)),floating, ((an extremely delicate and beautiful)),(beautiful detailed eyes),((disheveled hair)),1girl, bangs, black_hair, blue_sailor_collar, blurry, blurry_background, depth_of_field, eyebrows_visible_through_hair, long_hair, looking_at_viewer, parted_lips, sailor_collar, school_uniform, serafuku, shirt, solo, yoroizuka_mizore,medium_chest,colourful_stages,crown,masterpiece,full_body,white_thighhighs,extremely_detailed_CG_unity_8k_wallpaper,solo,1girl,lights Negative prompt: nsfw,nipples,lowres,bad anatomy,bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet, (((mutilated))),(((((too many fingers))))),((((fused fingers)))),(((extra fingers))),(((mutated hands))),extra limbs,(bad_prompt), (((mutilated))),(((((too many fingers))))),((((fused fingers)))),(((extra fingers))) Steps: 24, Sampler: DPM2 a Karras, CFG scale: 7, Seed: 3722281017, Size: 512x768, Model hash: 53a39f6a, Model: hiten_epoch10, Batch size: 4, Batch pos: 3, Clip skip: 2, ENSD: 31337 ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ## Big Thanks to - [Linaqruf](https://huggingface.co/Linaqruf) for his first step. - [Kohya](https://twitter.com/kohya_ss) with their [Kohya Trainer](https://note.com/kohya_ss/n/ne17e34dd51bf)

8174115d5ff7ea0aefea701097e3635b

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

Chituyi

wav2vec2

15

24

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,058

false

# wav2vec2-large-xls-r-300m-tr-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## 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: 20 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

b4c5de0c50c84f825fb90e694b3f1a1a

fathyshalab/all-roberta-large-v1-kitchen_and_dining-4-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,523

false

# all-roberta-large-v1-kitchen_and_dining-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

79ce2cee829768cc03e8748ccf227dfd

sd-concepts-library/captainkirb

sd-concepts-library

null

11

0

null

0

null

false

mit

null

0

[]

false

true

1,258

false

### CaptainKirb on Stable Diffusion This is the `<captainkirb>` 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`: ![<captainkirb> 0](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/1.jpeg) ![<captainkirb> 1](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/3.jpeg) ![<captainkirb> 2](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/0.jpeg) ![<captainkirb> 3](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/5.jpeg) ![<captainkirb> 4](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/2.jpeg) ![<captainkirb> 5](https://huggingface.co/sd-concepts-library/captainkirb/resolve/main/concept_images/4.jpeg)

c25042be9771e6103bf6776a84502c7d

Helsinki-NLP/opus-mt-fr-tvl

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-fr-tvl * source languages: fr * target languages: tvl * OPUS readme: [fr-tvl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tvl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-tvl/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tvl/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tvl/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.tvl | 32.6 | 0.497 |

70eb1692186ab8090685fb19be7f49d8

dundar/wav2vec2-large-xlsr-53-lithuanian

dundar

wav2vec2

9

8

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['lt']

['common_voice']

null

0

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

true

3,351

false

# Wav2Vec2-Large-XLSR-53-Lithuanian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian 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", "lt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian") 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 Lithuanian 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", "lt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian") 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**: 35.87 % ## Training The Common Voice datasets `except the test` set were used for training. The script used for training can be found [here](https://github.com/ebdundar/)

207e6bbe7486b198c5f4a947860140a9

erwanlc/t5-cocktails_recipe-base

erwanlc

t5

16

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

906

false

# t5-cocktails_recipe-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

3bb7c91e0c1ab5700767c8b31a156aee

laurabernardy/LuxGPT2-basedGER

laurabernardy

gpt2

8

2

transformers

0

text-generation

true

false

mit

['lb']

null

0

['luxembourgish', 'lëtzebuergesch', 'text generation', 'transfer learning']

true

1,065

false

## LuxGPT-2 based GER GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an German base model, feature space mapping from LB on Base feature space and gradual layer freezing. The training took place on a 32 GB Nvidia Tesla V100 - with One Cycle policy for the learning rate - with the help of fastai's LR finder - for 53.4 hours - for 20 epochs and 7 cycles - using the fastai library ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2-basedGER") model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2-basedGER") ``` ## Limitations and Biases See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.

124bc2200da741856fe0b90ed435d86a

ss756/bert-base-cased-finetuned-squad

ss756

bert

24

2

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,153

false

# bert-base-cased-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0081 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0071 | 1.0 | 22183 | 1.0081 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

33578cd24298bf967b7cdb25098b2e21

eliotm/t5-small-finetuned-en-to-ro-LR_1e-3

eliotm

t5

12

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['wmt16']

null

0

['generated_from_trainer']

true

1,274

false

# t5-small-finetuned-en-to-ro-LR_1e-3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.5215 - Bleu: 7.1606 - Gen Len: 18.2451 ## 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: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.6758 | 1.0 | 7629 | 1.5215 | 7.1606 | 18.2451 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

b2957ed7d83bc1e2ba040f36c3f97dd7

Helsinki-NLP/opus-mt-bg-sv

Helsinki-NLP

marian

10

17

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-bg-sv * source languages: bg * target languages: sv * OPUS readme: [bg-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bg-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bg.sv | 29.1 | 0.494 |

6429a4dcb3928cef6ac7838939bee22b

jonatasgrosman/exp_w2v2t_es_hubert_s459

jonatasgrosman

hubert

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

452

false

# exp_w2v2t_es_hubert_s459 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) 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.

0ad035b8e2d58abbde753ffca4fbf826

Duskfallcrew/duskfall-s-animanga-model

Duskfallcrew

null

21

5

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

914

false

### Duskfall's Animanga Model 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! 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 Discord https://discord.gg/Da7s8d3KJ7 Rules Do not sell merges, or this model. Do share, and credit if you use this model. DO PLS REVIEW AND YELL AT ME IF IT SUCKS! We never update the images on here anymore see civit https://civitai.com/user/duskfallcrew

c37cd1c092a31809d6da941dd33fda77

yanaiela/roberta-base-epoch_55

yanaiela

roberta

9

3

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_55']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 55 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_55. ## 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}, } ```

a65460a0e4877d56952b4f50599bc910

Helsinki-NLP/opus-mt-sm-fr

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-sm-fr * source languages: sm * target languages: fr * OPUS readme: [sm-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sm-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sm-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sm-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sm-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sm.fr | 24.6 | 0.419 |

1a5d1192d71b0a79626586f59952a6f6

nlpaueb/sec-bert-num

nlpaueb

bert

8

transformers

4

fill-mask

true

false

cc-by-sa-4.0

['en']

null

0

['finance', 'financial']

false

true

15,128

false

# SEC-BERT <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/> <div style="text-align: justify"> SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models: * [**SEC-BERT-BASE**](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents. * **SEC-BERT-NUM** (this model): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation). * [**SEC-BERT-SHAPE**](https://huggingface.co/nlpaueb/sec-bert-shape): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. </div> ## Pre-training corpus The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at <a href="https://www.sec.gov/">U.S. Securities and Exchange Commission (SEC)</a> ## Pre-training details <div style="text-align: justify"> * We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus. * We trained BERT using the official code provided in [Google BERT's GitHub repository](https://github.com/google-research/bert)</a>. * We then used [Hugging Face](https://huggingface.co)'s [Transformers](https://github.com/huggingface/transformers) conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TRC)](https://sites.research.google/trc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! </div> ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") model = AutoModel.from_pretrained("nlpaueb/sec-bert-num") ``` ## Pre-process Text <div style="text-align: justify"> To use SEC-BERT-NUM, you have to pre-process texts replacing every numerical token with [NUM] pseudo-token. Below there is an example of how you can pre-process a simple sentence. This approach is quite simple; feel free to modify it as you see fit. </div> ```python import re import spacy from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") spacy_tokenizer = spacy.load("en_core_web_sm") sentence = "Total net sales decreased 2% or $5.4 billion during 2019 compared to 2018." def sec_bert_num_preprocess(text): tokens = [t.text for t in spacy_tokenizer(text)] processed_text = [] for token in tokens: if re.fullmatch(r"(\d+[\d,.]*)|([,.]\d+)", token): processed_text.append('[NUM]') else: processed_text.append(token) return ' '.join(processed_text) tokenized_sentence = tokenizer.tokenize(sec_bert_num_preprocess(sentence)) print(tokenized_sentence) """ ['total', 'net', 'sales', 'decreased', '[NUM]', '%', 'or', '$', '[NUM]', 'billion', 'during', '[NUM]', 'compared', 'to', '[NUM]', '.'] """ ``` ## Using SEC-BERT variants as Language Models | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. | decreased | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058) | **SEC-BERT-BASE** | increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004) | **SEC-BERT-NUM** | increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006) | **SEC-BERT-SHAPE** | increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. | billion | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | billion (0.841), million (0.097), trillion (0.028), ##m (0.015), ##bn (0.006) | **SEC-BERT-BASE** | million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000) | **SEC-BERT-NUM** | million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000) | **SEC-BERT-SHAPE** | million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. | 2 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 20 (0.031), 10 (0.030), 6 (0.029), 4 (0.027), 30 (0.027) | **SEC-BERT-BASE** | 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035) | **SEC-BERT-NUM** | [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000) | **SEC-BERT-SHAPE** | [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. | % | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | % (0.795), percent (0.174), ##fold (0.009), billion (0.004), times (0.004) | **SEC-BERT-BASE** | % (0.924), percent (0.076), points (0.000), , (0.000), times (0.000) | **SEC-BERT-NUM** | % (0.882), percent (0.118), million (0.000), units (0.000), bps (0.000) | **SEC-BERT-SHAPE** | % (0.961), percent (0.039), bps (0.000), , (0.000), bcf (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $[MASK] billion during 2019 compared to 2018. | 5.4 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 1 (0.074), 4 (0.045), 3 (0.044), 2 (0.037), 5 (0.034) | **SEC-BERT-BASE** | 1 (0.218), 2 (0.136), 3 (0.078), 4 (0.066), 5 (0.048) | **SEC-BERT-NUM** | [NUM] (1.000), l (0.000), 1 (0.000), - (0.000), 30 (0.000) | **SEC-BERT-SHAPE** | [X.X] (0.787), [X.XX] (0.095), [XX.X] (0.049), [X.XXX] (0.046), [X] (0.013) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 billion during [MASK] compared to 2018. | 2019 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 2017 (0.485), 2018 (0.169), 2016 (0.164), 2015 (0.070), 2014 (0.022) | **SEC-BERT-BASE** | 2019 (0.990), 2017 (0.007), 2018 (0.003), 2020 (0.000), 2015 (0.000) | **SEC-BERT-NUM** | [NUM] (1.000), as (0.000), fiscal (0.000), year (0.000), when (0.000) | **SEC-BERT-SHAPE** | [XXXX] (1.000), as (0.000), year (0.000), periods (0.000), , (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 billion during 2019 compared to [MASK]. | 2018 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 2017 (0.100), 2016 (0.097), above (0.054), inflation (0.050), previously (0.037) | **SEC-BERT-BASE** | 2018 (0.999), 2019 (0.000), 2017 (0.000), 2016 (0.000), 2014 (0.000) | **SEC-BERT-NUM** | [NUM] (1.000), year (0.000), last (0.000), sales (0.000), fiscal (0.000) | **SEC-BERT-SHAPE** | [XXXX] (1.000), year (0.000), sales (0.000), prior (0.000), years (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion. | repurchased | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | held (0.229), sold (0.192), acquired (0.172), owned (0.052), traded (0.033) | **SEC-BERT-BASE** | repurchased (0.913), issued (0.036), purchased (0.029), redeemed (0.010), sold (0.003) | **SEC-BERT-NUM** | repurchased (0.917), purchased (0.054), reacquired (0.013), issued (0.005), acquired (0.003) | **SEC-BERT-SHAPE** | repurchased (0.902), purchased (0.068), issued (0.010), reacquired (0.008), redeemed (0.006) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion. | stock | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | stock (0.835), assets (0.039), equity (0.025), debt (0.021), bonds (0.017) | **SEC-BERT-BASE** | stock (0.857), shares (0.135), equity (0.004), units (0.002), securities (0.000) | **SEC-BERT-NUM** | stock (0.842), shares (0.157), equity (0.000), securities (0.000), units (0.000) | **SEC-BERT-SHAPE** | stock (0.888), shares (0.109), equity (0.001), securities (0.001), stocks (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion. | dividend | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | cash (0.276), net (0.128), annual (0.083), the (0.040), debt (0.027) | **SEC-BERT-BASE** | dividend (0.890), cash (0.018), dividends (0.016), share (0.013), tax (0.010) | **SEC-BERT-NUM** | dividend (0.735), cash (0.115), share (0.087), tax (0.025), stock (0.013) | **SEC-BERT-SHAPE** | dividend (0.655), cash (0.248), dividends (0.042), share (0.019), out (0.003) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion. | equivalents | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | revenue (0.085), earnings (0.078), rates (0.065), amounts (0.064), proceeds (0.062) | **SEC-BERT-BASE** | payments (0.790), distributions (0.087), equivalents (0.068), cash (0.013), amounts (0.004) | **SEC-BERT-NUM** | payments (0.845), equivalents (0.097), distributions (0.024), increases (0.005), dividends (0.004) | **SEC-BERT-SHAPE** | payments (0.784), equivalents (0.093), distributions (0.043), dividends (0.015), requirements (0.009) ## Publication <div style="text-align: justify"> If you use this model cite the following article:<br> [**FiNER: Financial Numeric Entity Recognition for XBRL Tagging**](https://arxiv.org/abs/2203.06482)<br> Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br> In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022 </div> ``` @inproceedings{loukas-etal-2022-finer, title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging}, author = {Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)}, publisher = {Association for Computational Linguistics}, location = {Dublin, Republic of Ireland}, year = {2022}, url = {https://arxiv.org/abs/2203.06482} } ``` ## About Us <div style="text-align: justify"> [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. </div> [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)

f34f9c86a043ec59964816eda0c36b3e

jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-8_female-2_s500

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

498

false

# exp_w2v2r_fr_vp-100k_gender_male-8_female-2_s500 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

435c7ed7a5f1c273590d0da79bd2d194

gokuls/mobilebert_add_GLUE_Experiment_qqp_128

gokuls

mobilebert

17

6

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,103

false

# mobilebert_add_GLUE_Experiment_qqp_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5071 - Accuracy: 0.7568 - F1: 0.6361 - Combined Score: 0.6965 ## 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 | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.6507 | 1.0 | 2843 | 0.6497 | 0.6318 | 0.0 | 0.3159 | | 0.6311 | 2.0 | 5686 | 0.5445 | 0.7259 | 0.5622 | 0.6441 | | 0.5153 | 3.0 | 8529 | 0.5153 | 0.7493 | 0.5892 | 0.6693 | | 0.4912 | 4.0 | 11372 | 0.5071 | 0.7568 | 0.6361 | 0.6965 | | 0.4805 | 5.0 | 14215 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 6.0 | 17058 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 7.0 | 19901 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 8.0 | 22744 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 9.0 | 25587 | nan | 0.6318 | 0.0 | 0.3159 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

e20d4b0b57e39d30f8973ee1ff6f4f41

CAiRE/wav2vec2-large-xlsr-53-cantonese

CAiRE

wav2vec2

12

15

transformers

2

automatic-speech-recognition

true

false

apache-2.0

['yue']

['common_voice']

null

0

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

true

3,956

false

# Wav2Vec2-Large-XLSR-53-Cantonese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice Corpus 8.0](https://commonvoice.mozilla.org/en/datasets). When using this model, make sure that your speech input is sampled at 16kHz. The Common Voice's validated `train` and `dev` were used for training. The script used for training can be found at [https://github.com/holylovenia/wav2vec2-pretraining](https://github.com/holylovenia/wav2vec2-pretraining). ## 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", "zh-HK", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("CAiRE/wav2vec2-large-xlsr-53-cantonese") model = Wav2Vec2ForCTC.from_pretrained("CAiRE/wav2vec2-large-xlsr-53-cantonese") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the zh-HK 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", "zh-HK", split="test") wer = load_metric("cer") processor = Wav2Vec2Processor.from_pretrained("CAiRE/wav2vec2-large-xlsr-53-cantonese") model = Wav2Vec2ForCTC.from_pretrained("CAiRE/wav2vec2-large-xlsr-53-cantonese") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: CER: 18.55 % ## Citation If you use our code/model, please cite us: ``` @inproceedings{lovenia2022ascend, title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation}, author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, year={2022} } ```

2c315f5cba4b9337a523c686b6ccda97

FortWorthCarpetCleaning/FortWorthCarpetCleaning

FortWorthCarpetCleaning

null

2

0

null

0

null

false

other

null

0

[]

false

true

610

false

Fort Worth Carpet Cleaning https://txfortworthcarpetcleaning.com/ (817) 523-1237 Save your wellbeing and your family wellbeing with Floor covering cleaning Stronghold worth TX as our administration will assist you with staying away from yourself and your family from asthma and sensitivity by eliminating the residue and the soil from your rug, let me let you know that you can clean your rug however clean it professionality and ensure that it turns out to be clear of any soil even the intense soil as the blood stains or wine stains needs our administration. We are here to accomplish Worth TX fulfillments.

6728cce49e091f74fb3d6c7327cb2376

Helsinki-NLP/opus-mt-sv-he

Helsinki-NLP

marian

10

12

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-sv-he * source languages: sv * target languages: he * OPUS readme: [sv-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-he/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-he/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-he/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-he/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.he | 23.1 | 0.440 |

bfa4fccaa4fd741dac5fba31a31feeb3

htermotto/distilbert-base-uncased-finetuned-squad

htermotto

distilbert

12

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad_v2']

null

0

['generated_from_trainer']

true

1,287

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4909 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.2236 | 1.0 | 8235 | 1.2651 | | 0.9496 | 2.0 | 16470 | 1.2313 | | 0.7572 | 3.0 | 24705 | 1.4909 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

4141fd3d94463454d875ae9235f256f7

joheras/mt5-small-clara-med

joheras

mt5

16

3

transformers

0

text2text-generation

true

false

apache-2.0

['es']

['lcampillos/CLARA-MeD']

null

0

['simplification', 'generated_from_trainer']

true

4,103

false

# mt5-small-clara-med This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [CLARA-MeD](https://huggingface.co/lcampillos/CLARA-MeD) dataset. It achieves the following results on the evaluation set: - Loss: 1.9850 - Rouge1: 33.0363 - Rouge2: 19.0613 - Rougel: 30.295 - Rougelsum: 30.2898 - SARI: 40.7094 ## 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: 5.6e-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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 190 | 3.0286 | 18.0709 | 7.727 | 16.1995 | 16.3348 | | No log | 2.0 | 380 | 2.4754 | 24.9167 | 13.0501 | 22.3889 | 22.4724 | | 6.79 | 3.0 | 570 | 2.3542 | 29.9908 | 15.9829 | 26.3751 | 26.4343 | | 6.79 | 4.0 | 760 | 2.2894 | 30.4435 | 16.3176 | 27.1801 | 27.1926 | | 3.1288 | 5.0 | 950 | 2.2440 | 30.8602 | 16.8033 | 27.8195 | 27.8355 | | 3.1288 | 6.0 | 1140 | 2.1772 | 31.4202 | 17.3253 | 28.3394 | 28.3699 | | 3.1288 | 7.0 | 1330 | 2.1584 | 31.5591 | 17.7302 | 28.618 | 28.6189 | | 2.7919 | 8.0 | 1520 | 2.1286 | 31.6211 | 17.7423 | 28.7218 | 28.7462 | | 2.7919 | 9.0 | 1710 | 2.1031 | 31.9724 | 18.017 | 29.0754 | 29.0744 | | 2.6007 | 10.0 | 1900 | 2.0947 | 32.1588 | 18.2474 | 29.2957 | 29.2956 | | 2.6007 | 11.0 | 2090 | 2.0914 | 32.4959 | 18.4197 | 29.6052 | 29.609 | | 2.6007 | 12.0 | 2280 | 2.0726 | 32.6673 | 18.8962 | 29.9145 | 29.9122 | | 2.4911 | 13.0 | 2470 | 2.0487 | 32.4461 | 18.6804 | 29.6224 | 29.6274 | | 2.4911 | 14.0 | 2660 | 2.0436 | 32.8393 | 19.0315 | 30.1024 | 30.1097 | | 2.4168 | 15.0 | 2850 | 2.0229 | 32.8235 | 18.9549 | 30.0699 | 30.0605 | | 2.4168 | 16.0 | 3040 | 2.0253 | 32.8584 | 18.8602 | 30.0582 | 30.0712 | | 2.4168 | 17.0 | 3230 | 2.0177 | 32.7145 | 18.9059 | 30.0436 | 30.0771 | | 2.3452 | 18.0 | 3420 | 2.0151 | 32.6874 | 18.8339 | 29.9739 | 30.0004 | | 2.3452 | 19.0 | 3610 | 2.0138 | 32.516 | 18.6562 | 29.7823 | 29.7951 | | 2.302 | 20.0 | 3800 | 2.0085 | 32.8117 | 18.8208 | 30.0902 | 30.1282 | | 2.302 | 21.0 | 3990 | 2.0043 | 32.7633 | 18.8364 | 30.0619 | 30.0781 | | 2.302 | 22.0 | 4180 | 1.9972 | 32.9786 | 19.0354 | 30.2166 | 30.2286 | | 2.2641 | 23.0 | 4370 | 1.9927 | 33.0222 | 19.0501 | 30.2716 | 30.2951 | | 2.2641 | 24.0 | 4560 | 1.9905 | 32.9557 | 18.9958 | 30.1988 | 30.2004 | | 2.2366 | 25.0 | 4750 | 1.9897 | 33.0429 | 18.9806 | 30.2861 | 30.3012 | | 2.2366 | 26.0 | 4940 | 1.9850 | 33.047 | 19.118 | 30.3437 | 30.3368 | | 2.2366 | 27.0 | 5130 | 1.9860 | 33.0736 | 19.0805 | 30.3311 | 30.3476 | | 2.2157 | 28.0 | 5320 | 1.9870 | 33.0698 | 19.0649 | 30.2959 | 30.3093 | | 2.2157 | 29.0 | 5510 | 1.9844 | 33.0376 | 19.0397 | 30.299 | 30.2839 | | 2.2131 | 30.0 | 5700 | 1.9850 | 33.0363 | 19.0613 | 30.295 | 30.2898 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1

9c4f14a67a28cdf777fc1fcde23a1b41

indobenchmark/indobart-v2

indobenchmark

mbart

7

277

transformers

3

text2text-generation

true

false

mit

['id']

['Indo4B+']

null

0

['indogpt', 'indobenchmark', 'indonlg']

false

true

1,281

false

# IndoBART-v2 Model [IndoBART-v2](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective. ## All Pre-trained Models | Model | #params | Training data | |--------------------------------|--------------------------------|-----------------------------------| | `indobenchmark/indobart-v2` | 132M | Indo4B-Plus (26 GB of text) | ## Authors <b>IndoBART</b> was trained and evaluated by Samuel Cahyawijaya*, Genta Indra Winata*, Bryan Wilie*, Karissa Vincentio*, Xiaohong Li*, Adhiguna Kuncoro*, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung ## Citation If you use our work, please cite: ```bibtex @article{cahyawijaya2021indonlg, title={IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation}, author={Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu Leylia and others}, journal={arXiv preprint arXiv:2104.08200}, year={2021} } ```

d82b47ff522066b8649d911c4717a9bf

ncoop57/DiGPTame-medium

ncoop57

gpt2

11

30

transformers

1

conversational

true

false

true

mit

null

0

['conversational']

false

true

2,304

false

## Finetuned DialoGPT model on Spanish Conversations This model was finetuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model on subtitles from Spanish movies and telenovelas from the awesome [OpenSubtitle dataset](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles). DialoGPT paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) Some example dialog from the model: |Role | Response | |---------|--------| |User | ¿Cómo está el clima hoy? | | Bot | Muy bien. | |User | Eso es bueno escuchar. ¿Vas a ir a la playa? | | Bot | No, también puedo regresar. | |User | Oh bien, bueno, iré a la playa más tarde. | | Bot | ¡No! | |User | ¿Qué está mal con eso? | | Bot | ¡Cómo que no! | |User | Estoy confundido, ¿por qué no puedo ir a la playa? | | Bot | ¡Cómo que no! | |User | Explícamelo por favor. | | Bot | ¡No! | ## Using the model Example code for trying out the model (taken directly from the [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model card): ```python from transformers import AutoModelWithLMHead, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("ncoop57/DiGPTame-medium") model = AutoModelWithLMHead.from_pretrained("ncoop57/DiGPTame-medium") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ``` ## Training your own model If you would like to finetune your own model or finetune this Spanish model, please checkout my blog post on that exact topic! https://nathancooper.io/i-am-a-nerd/chatbot/deep-learning/gpt2/2020/05/12/chatbot-part-1.html

2ea97827dc29f2ffac7cc5b0ab9cf215