license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_nano_rw_256.sw_in1k', pretrained=Tru... | 95ea41aa5378c63a170ebbde872c1657 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_nano_rw_256.sw_in1k', pretrained=True, ... | 1c622cd38bc41fcf8718b445e31831b1 |
mit | ['punctuation'] | false | ✨ bert-restore-punctuation []() This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews). The model predicts the punctuation and upper-casing of plain... | 2a6ad6e5b8beed40a8170ee227bb983b |
mit | ['punctuation'] | false | The default language is 'english' rpunct = RestorePuncts() rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record by tripling the resolution of a state-of-the-art electron microscope as successful as it w... | 115a9aee2ea34de9d9f818692065f164 |
mit | ['punctuation'] | false | sophisticated 3d reconstruction algorithms. The resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves. ``` **This model works on arbitrarily large text in English language and uses GPU if available.** ----------------------------------------------- | 9709bb0252c8bf005eaf86d5b1f18173 |
mit | ['punctuation'] | false | 📡 Training data Here is the number of product reviews we used for finetuning the model: | Language | Number of text samples| | -------- | ----------------- | | English | 560,000 | We found the best convergence around _**3 epochs**_, which is what presented here and available via a download. -----------... | 0396454635836743f9a82577ef6d26d5 |
mit | ['punctuation'] | false | 🎯 Accuracy The fine-tuned model obtained the following accuracy on 45,990 held-out text samples: | Accuracy | Overall F1 | Eval Support | | -------- | ---------------------- | ------------------- | | 91% | 90% | 45,990 Below is a breakdown of the performance of the model by each label: | label ... | e82d1b2bb1fbb945fa72d3b504f762d1 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-youtube This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7643 | 0e7a2de9be96a6795990665197515064 |
apache-2.0 | ['translation'] | false | opus-mt-pis-sv * source languages: pis * target languages: sv * OPUS readme: [pis-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 40b3d89f2bb9b3cac6d4234ab4bec8a3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny French Cased This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: - Loss: 0.6509 - Wer on `mozilla-foundation/common_voice_11_0` `fr`: 33.065... | 0f4fe1c6407646615dd3eccbb499f999 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7185 | 0.2 | 1000 | 0.7608 | 38.1636 | | 0.6052 | 1.2 | 2000 | 0.6949 | 34.9513 | | 0.4467 | 2.2 | 3000 | 0.6708 | 34.339... | 1723ced0f9f877393ad3ae8ec6f54edc |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 49d18064f22b7508ff24a7fa70c470a65f08f1be pip install -e . cd egs2/fate/tts1 ./run.sh --skip_data_prep false --skip_train true -... | 56e49f675f2f1d133c3ef8e29807eec0 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/finetune_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/22k/tts_fate_saber_vits_finetune_from_jsut ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// d... | 87133b39c7ae4d3c547423fb18313ea3 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-ru-en-finetuned-ru-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.4092 - Bleu: 30.4049 - Gen Len: 26.3911 | b767921052afc791b5bdba61d4954464 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 2.2606 | 1.0 | 94761 | 1.4092 | 30.4049 | 26.3911 | | 5de53669ccec9fcd4f8e04e90813c982 |
apache-2.0 | ['pytorch', 'diffusers', 'unconditional-image-generation'] | false | Samples 1.  2.  3.  on the sagawa/ZINC-canonicalized dataset. It achieves the following results on the evaluation set: - Loss: 0.0237 - Accuracy: 0.9900 | 7dc5f7920fed7a2c5259a153bb00495e |
mit | ['generated_from_trainer'] | false | Training and evaluation data We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we droped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1. | 8cb49a2894856f9d79a3b81099e42677 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 62a2c201e3660c4b51419dabe68943c1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:------:|:--------:|:---------------:| | 0.045 | 1.06 | 100000 | 0.9842 | 0.0409 | | 0.0372 | 2.13 | 200000 | 0.9864 | 0.0346 | | 0.0337 | 3.19 | 300000 | 0.9874 |... | d8882252c8024dc834829b2e39b6b6aa |
apache-2.0 | ['translation'] | false | opus-mt-guw-es * source languages: guw * target languages: es * OPUS readme: [guw-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | e816d72a0d28a8a1d2726ea04cecdb8f |
mit | ['generated_from_trainer'] | false | roberta-scarcasm-discriminator roberta-base label0: unsarcasitic label1: sarcastic The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the followi... | 17c52f01fc15b422469ed9e4f43f9a79 |
mit | ['generated_from_trainer'] | false | 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: 4 | 032a896d345b352563327ff2b307d89a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.144 | 1.0 | 2179 | 0.2522 | 0.9215 | | 0.116 | 2.0 | 4358 | 0.2105 | 0.9530 | | 0.0689 | 3.0 | 6537 | 0.2015 | 0.... | 1518fe075fdf77a73482a37ebd289c44 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | sv_core_news_md Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `sv_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`... | 554de3d35dc5e7f3f2cd378b3fb2ec48 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (381 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|I... | 533c3ec3559fa45534a7e2590b8b06ce |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.99 | | `TOKEN_P` | 99.95 | | `TOKEN_R` | 99.96 | | `TOKEN_F` | 99.95 | | `TAG_ACC` | 94.82 | | `POS_ACC` | 96.06 | | `MORPH_ACC` | 95.42 | | `MORPH_MICRO_P` | 97.28 | | `MORPH_MICRO_R` | 97.17 | | `MORPH_MICRO_F` | 97.23 | | `SENTS_P` | 89.35 | | `SENTS_R` | ... | 34d29654f4908a181df8b58ded36afb6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-dutch-V2 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: - eval_loss: 0.4262 - eval_wer: 0.3052 - eval_runtime: 8417.9087 - eval_sam... | f2bc9ab5e2c4805a8aaea0535c8ff21a |
mit | ['generated_from_trainer'] | false | roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-150000sample This model is a fine-tuned version of [HPL/roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-90000sample](https://huggingface.co/HPL/roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-90000sample) on the None datas... | cb7b13a4d7a4441ec76b9ded3aaa1f31 |
mit | [] | false | How to Use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='ksnugroho/feelin-base-uncased') unmasker("Adik sedang <mask> sepak bola <mask> lapangan") ``` Load tokenizer: ```python from transformers import ... | 00dac90f1c63068f00cd65026b92f88b |
mit | [] | false | Authors FEEL-IN was trained and evaluated by Kuncahyo Setyo Nugroho, Fitra Abdurrachman Bachtiar, Wayan Firdaus Mahmudy<br> Intelligent Systems Lab, Faculty of Computer Science, Brawijaya University, Indonesia | ea24ce8cb4e2edd1c2a25157e7d9a82c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.3968 - Accuracy: 1.0 - F1: 1.0 - Combined Score: 1.0 | bfc1a5e19fc815a3b9b9d84e5601985b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.4718 | 1.0 | 980 | 0.4083 | 0.875 | 0.8998 | 0.8874 | | 0.4239 | 2.0 | 1960 | ... | 9da178c90c13846f47c2ef09d13c1d05 |
openrail | [] | false | MODEL BY ShadoWxShinigamI Use Token - Rangoli mdjrny-rngli at the beginning of your prompt Training - 2240 steps, v1-5 Base, 28 images, 640x640 Prompt engineering is not required. In case something doesn't work, use Weighted prompts. Examples:-  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 speec... | a66be263ec4dcb3d71fbe6870ab10eee |
apache-2.0 | ['whisper-event'] | false | Whisper Gujarati Base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Gujarati data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 1f16c17edaf9156801c69edfa1be2f13 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.3e-05 - train_batch_size: 80 - eval_batch_size: 88 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - training_steps: 7225 (terminated upon convergence. Initially se... | 16cdcab3eb4439daf9192587dd84da80 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 965acf0b141f3f0831924c9b5c41e6b3 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4436 | a3b593fb94de7a041e2323419e1edd9f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5207 | 0.4 | 500 | 2.3765 | | 2.5094 | 0.8 | 1000 | 2.3648 | | 2.508 | 1.2 | 1500 | 2.4080 | | 2.4448 | 1.6 | 2000 | 2.4203 ... | 20b77374cd218490ad1a192003767595 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-zls-en Neural machine translation model for translating from South Slavic languages (zls) to English (en). 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... | 54126dea2b6737e4d00191b997492a71 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-17 * source language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv2021... | f7f21f62ace5020f4ba9cb329e1e1bff |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Да не би случайно Том да остави Мери да кара колата?", "Какво е времето днес?" ] model_name = "pytorch-models/opus-mt-tc-big-zls-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = Ma... | a8101af71b684aa51717da9d2d36a14b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | What's the weather like today? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-en") print(pipe("Да не би случайно Том да остави Мери да кара колата?")) | 83bafccb9d8bdef9fb185c06157349b8 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | dd547094268994b89a9d83428c124108 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | bos_Latn-eng | tatoeba-test-v2021-08-07 | 0.79339 | 66.5 | 301 | 1826 | | bul-eng | tatoeba-test-v2021-08-07 | 0.72656 | 59.3 | 10000 | 71872 | | hbs-eng | tatoeba-test-v2021-08-07 | 0.71783 | 57.3 | 10017 | 68934 | | hrv-eng | tatoeba-test-v2021-08-07 |... | f930a42f026d45384a503ed0c00db784 |
mit | ['generated_from_trainer'] | false | TurQA-bert-base-turkish-uncased-finetuned-toqad This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 5.9506 | a05de0da6eb4a3556884ecc8b5aaf7ae |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 29dfc59aac1ec62def00634b08562d15 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.9774 | 1.0 | 717 | 5.9506 | | 5.9675 | 2.0 | 1434 | 5.9506 | | 5.9584 | 3.0 | 2151 | 5.9506 | | 5.957 | 4.0 | 2868 | 5.9506 ... | fa87e39b9d60c41dbb08c50879875068 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | fin3 This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0748 - Precision: 0.944 - Recall: 0.9402 - F1: 0.9421 - Accuracy: 0.9921 | 3ec9c127c9296c172f2cf0a71f820395 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.0669 | 0.8821 | 0.9243 | 0.9027 | 0.9883 | | No log | 2.0 |... | e4adeb989884cbf6c0a7ff6bcc771f94 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Mar 25 04:35:42 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1+cu111` - Git hash: `21d19be00089678ca27f7fce474ef8d787689512` - Commit date: `Wed Mar 16 08:06:52 2022 -0400` | 007baf0dda5eaeb605c83b22db477c46 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_clean|2620|52576|97.2|2.5|0.3|0.3|3.1|35.2| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_other|2939|52343|93.4|6.0|0.6|0.8|7.4|56.3| |decode_rnnt_conformer_... | ed7d0191913144959810209d741fb9fa |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_clean|2620|281530|99.3|0.4|0.3|0.3|1.0|35.2| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_other|2939|272758|97.7|1.4|1.0|0.9|3.2|56.3| |decode_rnnt_conforme... | feb2e51b8006adae933eb689c2444e69 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_clean|2620|65818|96.6|2.4|1.0|0.5|3.9|35.2| |decode_rnnt_conformer_asr_model_valid.loss.ave_10best/test_other|2939|65101|92.1|5.9|2.0|1.3|9.2|56.3| |decode_rnnt_conformer_... | e080dfb9bc39b143330da26166253153 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_rnnt_conformer_ngpu4.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_rnnt_conformer_ngpu4_raw_en_bpe5000_sp ngpu: 2 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_metho... | f9d785752bc5834c01723ccbb2d047b0 |
mit | ['text-classification', 'flaubert'] | false | Classification d'articles de presses avec Flaubert Ce modèle se base sur le modèle [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased) et à été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. Dans leur papier, les équipes de reciTAL et de la Sorbonn... | 34fbc43b76a390ac09989cbf81776d1e |
mit | ['text-classification', 'flaubert'] | false | Entrainement Nous avons benchmarké différents modèles en les entrainant sur différentes parties des articles (titre, résumé, corps et titre+résumé) et avec des échantillons d'apprentissage de tailles différentes.  Les modèles ont été entrainé sur le cloud Azure avec des Tesl... | a3bf34bd3dabe52422208727d1c40e18 |
mit | ['text-classification', 'flaubert'] | false | Résulats  *Les lignes correspondent aux labels prédits et les colonnes aux véritables topics. Les pourcentages sont calculés sur les colonnes.* _Nous garantissons pas les résultats sur le long terme. Modèle réalisé dans le cadre d'un POC._ | 94bcc4d5443f9c772c44da1aaf8595c2 |
mit | ['text-classification', 'flaubert'] | false | Utilisation ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import TextClassificationPipeline model_name = 'lincoln/flaubert-mlsum-topic-classification' loaded_tokenizer = AutoTokenizer.from_pretrained(model_name) loaded_model = AutoModelForSequenceClassificati... | 880524fbf3c9d886787c43ef09a55afb |
cc-by-4.0 | [] | false | MahaBERT-Scratch MahaBERT is a Marathi BERT model. It is a base-BERT model trained from scratch on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [pap... | ffafcdb36a8eec983bab48b468a93177 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Portuguese Name Identification
The [NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) paper contains Flair-based models for Portuguese Language, initialized with [Flair BBP](https://github.co... | 8c75b6660c93ea8b77fb7d4b3a729dfc |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | make example sentence
sentence = Sentence("FISIOTERAPIA TRAUMATO - MANHÃ Henrique Dias, 38 anos. Exercícios metabólicos de extremidades inferiores. Realizo mobilização patelar e leve mobilização de flexão de joelho conforme liberado pelo Dr Marcelo Arocha. Oriento cuidados e posicionamentos.")
| 8aa16d8464080cff8e2d70fca3b5c54f |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [5,6]: "Henrique Dias" [− Labels: NOME (0.9735)]
Span [31,32]: "Marcelo Arocha" [− Labels: NOME (0.9803)]
```
So, the entities "*Henrique Dias*" (labeled as a **nom... | f8992fd611f3f485a841352123311b51 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | More Information
Refer to the original paper, [De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) for additional details and performance.
| 94572b72c7a02ebd7af0ce2e684e0f1b |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Acknowledgements
We thank Dr. Ana Helena D. P. S. Ulbrich, who provided the clinical notes dataset from the hospital, for her valuable cooperation. We also thank the volunteers of the Institute of Artificial Intelligence in Healthcare Celso Pereira and Ana Lúcia Dias, for the dataset annotation.
| fc8951205f935f3a042ecd28a39e261b |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Citation
```
@inproceedings{santos2021identification,
title={De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier},
author={Santos, Joaquim and dos Santos, Henrique DP and Tabalipa, F{\'a}bio and Vieira, Renata},
booktitle={Brazilian Conference on Intelligent Sys... | c2e27226f940b1db64690e8898059dd7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 3.0 - mixed_precision_t... | aaeb6f459808fcf23483503a1e38e5e3 |
mit | ['generated_from_trainer'] | false | English Verdict Classifier This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on 2,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into English with the [Google Clou... | 5a2bd5abf9f141fcce6725427be1b291 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 3945eb787ca465c21bb3e0ba9fdabd98 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Precision Macro | Precision Misinformation | Precision Factual | Precision Other | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:------... | 4f6d47492d24a15fae81443d3611211a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rage3 Dreambooth model trained by aross3 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusio... | 84823cc23e4f232bc658a81adafb3525 |
apache-2.0 | ['translation'] | false | ine-ine * source group: Indo-European languages * target group: Indo-European languages * OPUS readme: [ine-ine](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-ine/README.md) * model: transformer * source language(s): afr afr_Arab aln ang_Latn arg asm ast awa bel bel_Latn ben bho bjn bo... | bbfa5559e51951ba96fb851dd22e0747 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | euelections_dev2019.de-fr-deufra.deu.fra | 19.2 | 0.482 | | euelections_dev2019.fr-de-fradeu.fra.deu | 15.8 | 0.470 | | newsdev2014-enghin.eng.hin | 4.0 | 0.245 | | newsdev2014-hineng.hin.eng | 6.8 | 0.301 | | new... | cffdc4284c9ec695ddd20a3caaac46fb |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ine-ine - source_languages: ine - target_languages: ine - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-ine/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'es', 'os', 'ro', 'fy', 'cy', 'sc', 'is', 'yi',... | 08f52b91c94cdc2ef6ad8f4dad149d82 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_r-wav2vec2_s423 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 105b6271e507ec21a94f093f6264dec6 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_analysis_of_tweets_on_covid This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6161 - eval_accuracy: 0.7635 - eval_runtime: 34.9554 - eval_samples_per_s... | 030b9444d680e370e02af758c3e36f32 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-roberta-NCM-run-4 This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3405 - Accuracy: 0.6505 - Precision: 0.6410 - Recall: 0.6318 - F1: 0.6350 | e1bdf2ec2145b03c2f4894b68d0e430d |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.8975 | 1.0 | 927 | 0.9553 | 0.6127 | 0.5994 | 0.6026 | 0.5930 | | 0.6924 | 2.0 ... | a343cf4b7004bf2a2a7e9ed643f22f47 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6107 - Accuracy: 0.82 - F1: 0.8235 | b37dd0ea2a3a9a79308bef4be88b8b71 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr 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: 0.2748 - F1: 0.8406 | 9a02d1cc81da61ad6c8ab077398c4e3d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5754 | 1.0 | 191 | 0.3221 | 0.7950 | | 0.2607 | 2.0 | 382 | 0.2888 | 0.8225 | | 0.1751 | 3.0 | 573 | 0.2748 | 0.8406 | ... | 4f714bc140e4b1590634b753a73aee5d |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mbart-large-cc25-frquad-qag` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (dataset_name: default) via [`lmqg`](ht... | c20926182b20ce467477460fdb988261 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** fr - **Training data:** [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https:/... | 7b153f868c9e4f973bf069d5276ef01f |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` - With ... | 6cab7f726ee852dc7e31a1df3feaf451 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_frquad.default.json) | | Score | Type | Dataset ... | 073c0e20f5f50a92afa8df492af718d5 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_frquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 256 - ... | fd5c8405ea2683aaa21dd0d51af291c4 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | huh Dreambooth model trained by CinnabonMan with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffu... | 97f96e26b6da8e9979abccc716335e65 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Es - Juan Carlos Piñeros This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1672 - Wer: 5.4218 Using the script provided in the Whisper Sprint (... | 1ed2100bd0451a17c42e68fd8a795be4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precis... | 8158559f8ada852f7b5503188d3432b0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0792 | 0.33 | 1000 | 0.1904 | 6.0493 | | 0.0851 | 0.67 | 2000 | 0.1757 | 5.9558 | | 0.0946 | 1.0 | 3000 | 0.1672 | 5.4218 | ... | d30847eba89dbae297df4c2fba7371dd |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_wnut_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2706 - Precision: 0.5502 - Recall: 0.3197 - F1: 0.4045 - Accuracy: 0.9431 | 37b37492a1f2bd1cb3a1c154126d79ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2707 | 0.5649 | 0.3105 | 0.4007 | 0.9419 | | No log | 2.0 |... | db9a6fbaa44ae3458b35a80c8360cd11 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221121-113853 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3384 - Mae: 0.2739 - Rmse: 0.3959 - Abs Rel: 0.3230 - Log Mae: 0.1148 - Log Rmse:... | 86d59d628e497374624ea3821bd52517 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_... | bf6511fa0aa86b9da2082c84acda54e0 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 0.7523 | 1.0 | 72 | 0.5772 ... | 65d76acb95a273cf2b4940a5319a5668 |
apache-2.0 | ['translation'] | false | opus-mt-en-ln * source languages: en * target languages: ln * OPUS readme: [en-ln](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ln/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | d2f9e98f11e023ce53c8ae3226eeff17 |
apache-2.0 | ['generated_from_trainer'] | false | REA_GenderIdentification_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3366 - Accuracy: 0.8798 - F1: 0.8522 | 98e875e948f6e977854dc29bcf721f8e |
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