license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | F1 Weighted | Precision Micro | Precision Macro | Precision Weighted | Recall Micro | Recall Macro | Recall Weighted | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:-----------:... | 6887ba3b3ecc9be7203a61daf8903feb |
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: 0.2896 - Accuracy: 0.8767 - F1: 0.8787 | 6fa67f724e61482d362f64d96a57633f |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Nso-En_update3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-nso-en](https://huggingface.co/Helsinki-NLP/opus-mt-nso-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6854 - Bleu: 21.2223 | 4f1aec08291e6fc8993f764c830a4bea |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 3.5054 | 1.0 | 1734 | 3.1912 | 16.5243 | | 3.0368 | 2.0 | 3468 | 2.9680 | 18.0237 | | 2.6866 | 3.0 | 5202 | 2.8594 | 1... | eb2e45dc6305953f97969b346c81c683 |
other | [] | false | <img src="https://huggingface.co/minagi223/Lora_of_ReisalinStout3/blob/main/laisa12zz.png"> Reisalin Stout from Atelier Ryza 3: Alchemist of the End & the Secret by minagi from pixiv I have tried to make this model from some of the images I have redraw( (some just made by ai)), and the results have been good for me... | d1a482e9f736658d5524b36914563169 |
cc-by-sa-3.0 | [] | false | transformers.BertForTokenClassification)を用いて、日本語の文から固有表現を抽出します。 抽出される固有表現のタイプは、以下の8種類です。 - 人名 - 法人名（法人または法人に類する組織） - 政治的組織名（政治的組織名、政党名、政府組織名、行政組織名、軍隊名、国際組織名） - その他の組織名 （競技組織名、公演組織名、その他） - 地名 - 施設名 - 製品名（商品名、番組名、映画名、書籍名、歌名、ブランド名等） - イベント名 | 8eb56113c8f0f927d90511466018afe8 |
cc-by-sa-3.0 | [] | false | 使用方法 必要なライブラリ（transformers、unidic_lite、fugashi）をpipなどでインストールして、下記のコードを実行するだけです。 ```python from transformers import BertJapaneseTokenizer, BertForTokenClassification from transformers import pipeline model = BertForTokenClassification.from_pretrained("jurabi/bert-ner-japanese") tokenizer = BertJapaneseTokenizer.from_... | ebc257179adc6236f7b70bbb91b6fed9 |
mit | ['financial', 'stocks', 'topic'] | false | Introduction This model was train on the topic column of financial_news_sentiment_mixte_with_phrasebank_75 dataset. The topic column was generated using a zero-shot classification model on 11 topics. There was no manual reviews on the generated topics and therefore we should expect misclassifications in the dataset, ... | 8e327a87403341476c894c73905a9b87 |
mit | ['financial', 'stocks', 'topic'] | false | Training data Training data was classified as follow: class |Description -|- 0 |acquisition 1 |other 2 |quaterly financial release 3 |appointment to new position 4 |dividend 5 |corporate update 6 |drillings results 7 |conference 8 |share repurchase program 9 |grant of stocks | fbc421b87e39a772602c63ea0e38dcbf |
mit | ['financial', 'stocks', 'topic'] | false | Load roberta-large-financial-news-topics-en and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-financial-news-topics-en") model = AutoModelForSequenceClassification.from_pretrained("J... | dade9a0bc19d95854bb20f942cf77ed5 |
mit | ['financial', 'stocks', 'topic'] | false | Process text sample (from wikipedia) from transformers import pipeline pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) pipe("Melcor REIT (TSX: MR.UN) today announced results for the third quarter ended September 30, 2022. Revenue was stable in the quarter and year-to-date. Net operating inco... | 6411fccda97ebef715577b7717c3a528 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/slurp_entity_branchformer` This model was trained by Yifan Peng using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/). Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.html) | f82d8ec6a4caa320f7f9e3a65d342105 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 55b6cc387fd0252d1a06db2042fd101bcea7bb34 pip install -e . cd egs2/slurp_entity/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/slurp_entity_branchformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | d9da7933db2c8dcf102ae3b0f39dfcc5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri May 27 03:41:59 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0` - Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6` - Commit date: `Thu May 26 00:22:45 2022 -0400` | 95490f7a925a8f85520bc3681c566578 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/devel|8690|178058|83.7|7.6|8.8|2.8|19.2|50.5| |decode_asr_asr_model_valid.acc.ave_10best/test|13078|262176|82.6|7.9|9.5|2.7|20.1|49.2| | d574ae1b68eb6b8fc0593d6910b95d9a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/devel|8690|847400|90.1|3.0|6.9|3.3|13.2|50.5| |decode_asr_asr_model_valid.acc.ave_10best/test|13078|1245475|89.0|3.2|7.8|3.1|14.1|49.2| | fcd5913ee6b24f3f3a4d68d66d45cc4e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_branchformer_e18_d6_size512_lr1e-3_warmup35k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_branchformer_e18_d6_size512_lr1e-3_warmup35k_raw_en_word ngpu: 1 seed: 0 num_worke... | 6aad51c82c84639de2890d7cf742d99f |
mit | ['generated_from_trainer'] | false | data2vec-text-finetuned-squad2 This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.1044 | beb89a4715867c33168dc28fa88adfba |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0173 | 1.0 | 8239 | 0.9629 | | 0.7861 | 2.0 | 16478 | 1.0098 | | 0.6402 | 3.0 | 24717 | 1.1044 | | fdcf809ab4773556a92883d953a4f9ea |
apache-2.0 | [] | false | bert-base-en-sw-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | f262356669457808e40079f18e6d3c46 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-sw-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-sw-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 112d051b4939b6480b2fa62adcf8a653 |
apache-2.0 | ['generated_from_trainer'] | false | t5_large_race_cosmos_qa This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the race dataset. It achieves the following results on the evaluation set: - Loss: 0.4382 - Accuracy: 0.8023 | 2fa27ccb9dccbc86c0d494e134b89385 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 38d71ba4e74753671d3f63b492c90a7e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.3513 | 1.0 | 10983 | 0.7714 | 0.3165 | | 0.2109 | 2.0 | 21966 | 0.7986 | 0.3329 | | 0.0929 | 3.0 | 32949 | 0.4382 | 0.80... | 4b2f5548cc7f36f69d5ac84ed6924807 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-fr_s557 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | aeeda1324de103a861759fc31ef24595 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-fine-tune-timit This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4451 - Wer: 0.3422 | 8958d40a2aa06b2563590c17342f1e9b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6487 | 4.0 | 500 | 1.9065 | 1.0411 | | 0.8742 | 8.0 | 1000 | 0.4658 | 0.4720 | | 0.3084 | 12.0 | 1500 | 0.4367 | 0.4010 | |... | 3ad6002538769e97421b364a2f87fa2a |
mit | [] | false | Model desciption This is fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/Spac... | 0d58e246a1e5c3861d3189c61ceae7a3 |
apache-2.0 | [] | false | Model description This model is a fine-tuned version of the Danish acoustic model [Alvenir/wav2vec2-base-da](https://huggingface.co/Alvenir/wav2vec2-base-da) on the Danish part of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), containing ~6 crowdsourced hours of read-aloud Da... | a690bcd7a29bb222da2576ba840dbff7 |
apache-2.0 | [] | false | Performance The model achieves the following WER scores (lower is better): | **Dataset** | **WER without LM** | **WER with 5-gram LM** | | :---: | ---: | ---: | | [Danish part of Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/viewer/da/train) | 46.05 | 39.86 | | [Alvenir test... | 2ae741384a20a6d0f5eb0252030cdc58 |
apache-2.0 | ['generated_from_trainer'] | false | recipistil This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9743 - Rmse: 1.4051 - Mse: 1.9743 - Mae: 1.0578 | 1c4d477d5799389052178facff11f3a8 |
apache-2.0 | ['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 - num_epochs: 15 | a34723707b46b648f4d944be0ae8bf06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:| | 1.9657 | 1.0 | 8126 | 1.9789 | 1.4067 | 1.9789 | 1.0578 | | 1.9617 | 2.0 | 16252 | 1.9873 | 1.4097 | 1.9... | 31373d923a6e5dc9c0ade1a15a1b7744 |
other | [] | false | Rug Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/rug-cleaning.html (469) 213-8132 Carpet and area rug manufacturers recommend using the free hot water extraction system from Our Rug Cleaning.Carpet Cleaning Mesquite TX can also clean some area rugs at a lower temperature, depending on how many fibers they h... | 1b67cd99810e144845e5a1f5ab69b4d5 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-5_england-5_s732 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 0b0453a7ab12a414df30837c4d638c73 |
mit | ['generated_from_trainer'] | false | indic-bert-finetuned-non-code-mixed-DS This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9997 - Accuracy: 0.5620 - Precision: 0.5591 - Recall: 0.5203 - F1: 0.5078 | 9176d166012524c0139891b8ce1b865f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 69731d94494aa7f9cab78bedc1506e98 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0673 | 3.99 | 926 | 1.0361 | 0.4142 | 0.4092 | 0.3851 | 0.2750 | | 1.0144 | 7.98 |... | 544863f2b6efe16673b01df1c8a54e5f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1580 - F1: 0.8547 | 1018e2bc3c5e3797f3b47d114431daa1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3718 | 1.0 | 269 | 0.1761 | 0.8223 | | 0.1535 | 2.0 | 538 | 0.1608 | 0.8404 | | 0.1074 | 3.0 | 807 | 0.1580 | 0.8547 | ... | f9e2fc25f753e7a26c0b658b212fba9e |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-pointer-adv-top_v2 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the top_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0255 - Exact Match: 0.8472 | 51e740efd8ccd9f12928e62d8b209f5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 2.2938 | 0.41 | 200 | 0.5532 | 0.0012 | | 0.671 | 0.82 | 400 | 0.1624 | 0.1610 | | 0.5276 | 1.23 | 600 | 0.0692 ... | e4fbed097c515644ffef35adbe3b891d |
apache-2.0 | ['translation'] | false | opus-mt-bcl-fr * source languages: bcl * target languages: fr * OPUS readme: [bcl-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | ad42b0c5d55f6644772de3976682d434 |
cc-by-sa-4.0 | [] | false | Polbert-CB - Polish BERT trained for Automatic Cyberbullying Detection This is a Polish version of BERT language model, specifically, [Polbert](https://huggingface.co/dkleczek/bert-base-polish-uncased-v1), trained on a re-annotated and improved Dataset for Automatic Cyberbullying Detection in Polish Laguage. | 2066106e5a2cf6cf6d749d7d3246e516 |
cc-by-sa-4.0 | [] | false | Fine-tuning dataset The dataset used for fine-tuning this model was based on the original [Dataset for Automatic Cyberbullying Detection in Polish Laguage](https://huggingface.co/datasets/poleval2019_cyberbullying), which was recently additionally cleaned and re-annotated by experts from [Samurai Labs](https://www.sam... | 2334a9650782d5a486eb9bfc56dd90ed |
cc-by-sa-4.0 | [] | false | Acknowledgements * We would like to express our gratitude to the annotators of this dataset, including original annotators, and more recent expert annotators, for their invaluable time they spent on preparing the dataset. | 3886261b75e41283859deb7a39283c53 |
cc-by-sa-4.0 | [] | false | Author Michal Ptaszynski - contact me on: - Twitter: [@mich_ptaszynski](https://twitter.com/mich_ptaszynski) - GitHub: [ptaszynski](https://github.com/ptaszynski) - LinkedIn: [michalptaszynski](https://jp.linkedin.com/in/michalptaszynski) - HuggingFace: [ptaszynski](https://huggingface.co/ptaszynski) | 2b3376977c12905ab61e078c39cc2c1d |
cc-by-sa-4.0 | [] | false | Licences The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style=... | 6e03edcd72919667304f71d6934f73a5 |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. Model: ``` @article{ptaszynski2022cyberbullyibng-bert-pl, title={Polish BERT trained for Automatic Cyberbullying Detection}, author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel and Skrzek, Pawel and Soliwoda, Kamil and Fortuna, Marc... | 281b9f688d1f427ea63cc09c519a488f |
creativeml-openrail-m | [] | false | ***on_crescentV2*** - The learned concept is a girl sitting on a crescent moon. It isn't perfect, but I hope that you can get some enjoyment out of it. See the on_crescent_previews folder for example images and feel free to pull PNG info to see the prompts and settings used. Use the Token ``"on_crescent"`` in your pro... | 5869f3b84108fa7f58c07e4f473b9764 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-pr-zhsum 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: - Loss: 1.1751 - Rouge1: 0.1109 - Rouge2: 0.0318 - Rougel: 0.1109 - Rougelsum: 0.1115 | 276d922028327eb34b0334954fe2308c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 6.0524 | 1.0 | 381 | 1.5214 | 0.0997 | 0.0281 | 0.0991 | 0.1001 | | 2.0421 | 2.0 | 762 ... | c30c6820a66f11d6de59dde981990a51 |
mit | [] | false | fox purple on Stable Diffusion This is the `<foxi-purple>` 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 als... | 20ae0d5e9203f5f960c3698a2a22bfa6 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-sv_s500 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) 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 you... | 89aabbe29b6f9d15b08e377c654609de |
mit | [] | false | Isabell Schulte - PV-PVII - 3000steps on Stable Diffusion This is the `<isabell-schulte-p5-p7-style-3000s>` 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_conc... | 549787cad4522633516e3480d99f623b |
mit | ['generated_from_trainer'] | false | covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2 This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5816 - Acc... | b6c71faec24e8cc0ce732ee7a14516cf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8511 | 1.0 | 700 | 0.6372 | 0.1478 | | 0.6146 | 2.0 | 1400 | 0.5816 | 0.0901 | | 0.365 | 3.0 | 2100 | 0.6170 | 0.... | ca513cfa3d53375dd332b74d2b8f4f1e |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum_epoch4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4245 - Rouge1: 29.5204 - Rouge2: 8.4931 - Rougel: 22.9705 - Rougelsum: 23.0872 - Gen Len: 18.8221 | f29652ea0b9bf9f4cdd03a59df95ce1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7175 | 1.0 | 7620 | 2.4899 | 28.585 | 7.7626 | 22.1314 | 22.2424 | 18... | 083fcbb3a0a40647ccf9e8deef6d5dc2 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-zle-pt Neural machine translation model for translating from East Slavic languages (zle) to Portuguese (pt). 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 t... | fd2ec10dbd9f190ab2694c0cf208a230 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-23 * source language(s): rus ukr * target language(s): por * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https... | d111f799d05d0dc0cd831c5c3ba7f659 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>por<< Я маленькая.", ">>por<< Я войду первым." ] model_name = "pytorch-models/opus-mt-tc-big-zle-pt" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(mo... | 9c6396d61aadba6dd131a2710cceeb47 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Eu entro primeiro. ``` 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-zle-pt") print(pipe(">>por<< Я маленькая.")) | c971071146ba59ff75cc3e3e1853f6bc |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-por/opusTCv20210807_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zl... | 3655c371bb378bf700ee4c6fd03fb8de |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | rus-por | tatoeba-test-v2021-08-07 | 0.63749 | 42.8 | 10000 | 74713 | | ukr-por | tatoeba-test-v2021-08-07 | 0.65288 | 45.2 | 3372 | 21315 | | bel-por | flores101-devtest | 0.48481 | 16.2 | 1012 | 26519 | | rus-por | flores101-devtest | 0.58567 | 31.9 | ... | 2f527a91a3b51993540bd63dccccff1b |
mit | ['Cometrain AutoCode', 'Cometrain AlphaML'] | false | fake-news-detector-t5 This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to recognize fake news and misinformation. Automatically ... | aed86045b2248272aaf90b98b9833e8e |
other | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [Tianyi98/opt-350m-finetuned-cola](https://huggingface.co/Tianyi98/opt-350m-finetuned-cola) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4133 - Accuracy: 0.92 - F1: 0.9205 | 9e5d3b7c021af5402f93a4ae1702ab23 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_no-pretraining_s852 Fine-tuned randomly initialized wav2vec2 model 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 h... | 4f0d53c527eb4c351c6e9d3fb7e0a66a |
mit | ['generated_from_keras_callback'] | false | Training procedure This model has been created by fine-tuning the TensorFlow version [camembert-base](https://huggingface.co/camembert-base) **after freezing the encoder part**: ```python model.roberta.trainable = False ``` Therefore, only the classifier head parameters have been updated during training. | d2a4daa776f32a1ed6c496c2c17d2aee |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: ``` - optimizer: { 'name': 'Adam', 'learning_rate': { 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 15000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False... | d2060d95c3664bf9cb8c15c2e05adde8 |
apache-2.0 | ['translation'] | false | nor-nld * source group: Norwegian * target group: Dutch * OPUS readme: [nor-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nld/README.md) * model: transformer-align * source language(s): nob * target language(s): nld * model: transformer-align * pre-processing: normalization + Sent... | 622f0730ae732c873c472477dd7c35be |
apache-2.0 | ['translation'] | false | System Info: - hf_name: nor-nld - source_languages: nor - target_languages: nld - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nld/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['no', 'nl'] - src_constituents: {'nob', 'nno'} - tg... | fba49ad59a2384cc5c8626e6d1c65e89 |
apache-2.0 | ['image-classification'] | false | eca_resnet26t Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tri... | d10293f7a86ba4b9a0b4267217aa9c01 |
apache-2.0 | ['generated_from_trainer'] | 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: 0.0605 - Precision: 0.9289 - Recall: 0.9387 - F1: 0.9338 - Accuracy: 0.9843 | 4b6b098f08f1b626a6b349910c230e16 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2388 | 1.0 | 878 | 0.0671 | 0.9162 | 0.9211 | 0.9187 | 0.9813 | | 0.0504 | 2.0 |... | d3547be218abcdd7820bff997eecb77a |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large) on the [speech_commands](https://huggingface.co/datasets/speech_commands) dataset. It achieves the following results on the evaluation set: - Loss: 0.5... | c4aa6505cda5cd6529c0f312bc003567 |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations The model can spot one of the following keywords: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow", "Backwa... | 1796801b689d1f884edbfd61cddfc56b |
apache-2.0 | ['generated_from_trainer'] | false | Training procedure The model was fine-tuned on [Amazon SageMaker](https://aws.amazon.com/sagemaker), using an [ml.p3dn.24xlarge](https://aws.amazon.com/fr/ec2/instance-types/p3/) instance (8 NVIDIA V100 GPUs). Total training time for 10 epochs was 4.5 hours. | 2495f77afc4d2a4d4732e2a5aa3e1e60 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr... | 56f94f1df1b72f6dfefa63b2188f1b23 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2901 | 1.0 | 83 | 2.0542 | 0.8875 | | 1.8375 | 2.0 | 166 | 1.5610 | 0.9316 | | 1.4957 | 3.0 | 249 | 1.1850 | 0.... | c6bf81af33a6ea8a21d4fc88769af366 |
apache-2.0 | ['generated_from_trainer'] | 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: 2.4285 | e1637c659ce32da89ff5d349c1da24c8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5169 | 1.0 | 1642 | 1.6958 | | 1.1326 | 2.0 | 3284 | 2.0009 | | 0.8638 | 3.0 | 4926 | 2.4285 | | cf7ca6928a6160950b8a2d2259eef2e5 |
apache-2.0 | ['tapas', 'sequence-classification'] | false | TAPAS small model fine-tuned on Tabular Fact Checking (TabFact) This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model w... | 61ab64d04c9496dff649d8304f158118 |
apache-2.0 | ['Spoken language understanding', 'speechbrain', 'wav2vec2', 'hubert', 'pytorch'] | false | End-to-end SLU model for SLURP This repository provides all the necessary tools to perform Speech to intent and slot label with a fine-tuned hubert encoder + decoder using SpeechBrain (in E2E trend). It is trained on [SLUR](https://arxiv.org/abs/2011.13205) training data. For a better experience, we encourage you ... | b45c26fca0c13b1dee9e338d382900f6 |
apache-2.0 | ['Spoken language understanding', 'speechbrain', 'wav2vec2', 'hubert', 'pytorch'] | false | Perform SLU E2E decoding An external `py_module_file=custom_interface.py` is used as an external Predictor class into this HF repos. We use `foreign_class` function from `speechbrain.pretrained.interfaces` that allow you to load you custom model. ```python from speechbrain.pretrained.interfaces import foreign_class... | 88fefd0a916889692c5fdffbf3174c2c |
apache-2.0 | ['Spoken language understanding', 'speechbrain', 'wav2vec2', 'hubert', 'pytorch'] | false | Training The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/SLUR... | 12502d063dc6065a8be4ba10aa596a59 |
apache-2.0 | ['speech'] | false | SEW-D-small [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | ac6f5316ad378debf8d85c69df30636a |
apache-2.0 | ['translation'] | false | eng-sit * source group: English * target group: Sino-Tibetan languages * OPUS readme: [eng-sit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sit/README.md) * model: transformer * source language(s): eng * target language(s): bod brx brx_Latn cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan... | d8240dc2d77f14ef4a325a1efa9dd79d |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2017-enzh-engzho.eng.zho | 23.5 | 0.217 | | newstest2017-enzh-engzho.eng.zho | 23.2 | 0.223 | | newstest2018-enzh-engzho.eng.zho | 25.0 | 0.230 | | newstest2019-enzh-engzho.eng.zho | 20.2 | 0.225 | | Tatoeb... | b8a24245742dd639e598c750df7247a5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-sit - source_languages: eng - target_languages: sit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'sit'] - src_constituents: {'eng'} - tgt_cons... | f634b8f62157261cbb6f201ccde2e657 |
mit | [] | false | This is the baseline model used in most experiments in the paper ["A Dataset for N-ary Relation Extraction of Drug Combinations"](https://arxiv.org/abs/2205.02289). *(for just the domain-adapted masked language model that we use underneath this model, see [here](https://huggingface.co/allenai/drug_combinations_lm_pubm... | 7557d3c5d222238c1f6274249497cd65 |
apache-2.0 | ['xlm-roberta-base'] | false | By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
| 9f7861caf27d163502abb01fb3141426 |
apache-2.0 | ['xlm-roberta-base'] | false | Training Data
The model was trained on the the combined Greek+English version of the AllNLI dataset(sum of [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/)). The Greek part was created using the EN2EL NMT model available [here](https://huggingface.co/lighteternal... | 3af7d3a1537df7d62362dcdede675674 |
apache-2.0 | ['xlm-roberta-base'] | false | Usage with sentence_transformers
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('lighteternal/nli-xlm-r-greek')
scores = model.predict([('Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'),
('Ένα μαύρο α... | 4614e0b61f2fec23bdd45ed97c2c7748 |
apache-2.0 | ['xlm-roberta-base'] | false | Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('lighteternal/nli-x... | 6ed8ac1465c4a9e4cf7e7108f7524b79 |
apache-2.0 | ['xlm-roberta-base'] | false | To use the model for Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='lighteternal/nli-xlm-r-greek')
sent = "Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγμ... | 76d21602dad5476ac67300b65b4c94ea |
apache-2.0 | ['xlm-roberta-base'] | false | {'sequence': 'Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας', 'labels': ['τεχνολογία', 'αθλητισμός', 'πολιτική'], 'scores': [0.8380699157714844, 0.09086982160806656, 0.07106029987335205]}
```
| edd0b05bd962537eb7370e2fb5f2a9cd |
apache-2.0 | ['xlm-roberta-base'] | false | Citation info
Citation for the Greek model TBA.
Based on the work [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
Kudos to @nreimers (Nils Reimers) for his support on Github .
| 116fd6891ebe02047fa65c706a065106 |
apache-2.0 | ['generated_from_trainer'] | false | my-distilBERT-finetune-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: 0.0563 - Precision: 0.9377 - Recall: 0.9398 - F1: 0.9387 | 9e1c80f97a32ad6cc1d1fbe656c4f60f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - 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 - mixed_precision_training: Native AMP | 399634dd869b1fbc6c90000915e69220 |
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