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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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall ... | 1c5529b1b019ee3613b451a4df7316b4 |
mit | ['BERT', 'token-classification', 'sequence-tagger-model'] | false | Arabic NER Model - [Github repo](https://github.com/edchengg/GigaBERT) - NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English). - ACE2005 Training data: English + Arabic - [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guideline... | bc0e8a87d87eb9cb4f50af49ba9d2968 |
mit | ['BERT', 'token-classification', 'sequence-tagger-model'] | false | How to use ```python >>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer >>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace") >>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace") >>> ner_pip = pipeline("ner", model=ner_mod... | 48709a68598bbd611a52f484a2cab5eb |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v5_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1914 - Precision: 0.6408 - Recall: 0.7218 - F1: 0.6789 - Accuracy: ... | e56787c5e153482a93b33a956da657fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 56 | 0.2937 | 0.4307 | 0.5257 | 0.4735 | 0.9010 | | No log | 2.0 |... | 2da8d4bde7ddb5fd36b04eb88a6d7906 |
mit | ['generated_from_trainer'] | false | roberta-large-mnli-misogyny-sexism-4tweets-2e-05-0.05 This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6222 - Accuracy: 0.7064 - F1: 0.7158 - Precision: 0.6462 - Recall: 0.8022 ... | e5247d17e1945ba1073a64d3b07c1731 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:--:|:---:| | 0.5053 | 1.0 | 1346 | 0.6657 | 0.... | 6a47e51c513f4dd91a588fbddc807e14 |
apache-2.0 | ['generated_from_keras_callback'] | false | Electra-base-squad-adversarialqa-epoch-3 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5566 - Epoch: 2 | 488b252ebbea3251d4a777e6a99eb0e8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft500_6class 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: 1.5162 - Accuracy: 0.356 - F1: 0.3347 | 313b533e1b3530b590270f2c9da55e95 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.579 | 1.0 | 188 | 1.5575 | 0.2933 | 0.2521 | | 1.4527 | 2.0 | 376 | 1.5043 | 0.3227 | 0.2821 | | 1.3767 |... | 27209d5b2d1880323090aeac6c25e0c1 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-2_female-8_s303 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 t... | b472cc4f120303b2a521cc2020f7b566 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-indomain-mix-bal-0 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5814 - Accuracy:... | 631ada8a0e86a78ba280083f0142ab6e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:--:|:---:|:---:| | 0.4018 | 1.0 | 1356 | 0.5260 | 0.74... | 4956289f5db585ae03e117d84d0027d0 |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | mt5-small-finetuned-multilingual-xlsum-new This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the 45 languages of the XL-Sum dataset. It achieves the following results on the evaluation set: - Loss: 2.7679 - Rouge1: 9.1993 - Rouge2: 2.3416 - Rougel: 7.6684 - Rougelsu... | 380efd0ebe2a3e99cd924b2da5b1e45d |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.9684 | 1.0 | 1687 | 2.8902 | 8.0531 | 1.8357 | 6.7234 | 6.7401 | | 3.62 | 2.0 | 3374 ... | 41ab03c40c5b3444433c3b2efa43d44d |
mit | [] | false | model by chrisemoody This your the Stable Diffusion model fine-tuned the robeez baby girl water shoes concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks shoes** You can also train your own concepts and upload them to the library by using [this note... | 96dfeac8e6d734b643e838b3e76ea8c9 |
apache-2.0 | ['classification'] | false | camembert-fr-covid-tweet-sentiment-classification This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2. This model reaches an accuracy of 71% on the dev set. In this dataset, given a tweet, the goal was to infer the underlying topic of the... | c38f5eed64d8d1e2bd18fe1bb6ee567c |
apache-2.0 | ['classification'] | false | Pipelining the Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-sentime... | 2dec0361663c680b760f6b4e2339b4a3 |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Base Cased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pretrai... | 03ee246e5ae5d2c479a0daa3be520ab0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/stsb-bert-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 9684bef52f971a1e9448065ade0a62fb |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 1aa56071f41c25160a36834e0b7dfe39 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/stsb-bert-base) | e4d8ec22bb9957d4fdbc1d94f26b7dbc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8628 - Matthews Correlation: 0.5331 | 07897ae55e21a95bd58e7221f5989b1a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5253 | 1.0 | 535 | 0.5214 | 0.3943 | | 0.3459 | 2.0 | 1070 | 0.5551 | 0.4693 | | 0.2... | 18d4e4036d48e6d5ef5dbead27ad3666 |
mit | ['generated_from_trainer'] | false | xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2864 | 81b1de3904f7f524c5a5504edad8b98e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6088 | 1.0 | 5533 | 1.4429 | | 1.3928 | 2.0 | 11066 | 1.3183 | | 1.3059 | 3.0 | 16599 | 1.2864 | | 1a27d9887e1a863a500b3b06b03bcfb6 |
mit | ['generated_from_keras_callback'] | false | deepiit98/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1670 - Train End Logits Accuracy: 0.9688 - Train Start Logits Accuracy:... | 9ee051d3778fe133fbc6a6b0716daf80 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 192f8cb4af38223510ed996a4fd94745 |
apache-2.0 | ['generated_from_keras_callback'] | false | JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6429 - Validation Loss: 0... | b79902db0216851c326b403f305b26df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6423 | 0.8071 | 0 | | 0.6424 | 0.8071 | 1 | | 0.6429 | 0.8071 | 2 | | 0487e09cc6906c2ee8631e3230566b7b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5628 | 1.0 | 2249 | 6.4705 | | 6.1956 | 2.0 | 4498 | 6.2012 | | 6.021 | 3.0 | 6747 | 6.1128 | | e820f10d5a8f365b7c4eb837e7420dbf |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Breton This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 br dataset. It achieves the following results on the evaluation set: - Loss: 0.6425 - Wer: 35.1077 | 9b03d7700b62903a81b45a01501db1fe |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0065 | 5.03 | 3000 | 0.6425 | 35.1077 | | bccbe2a5b31bd101dc82d2396ad263a5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Wav2Vec2-Large-XLSR-53-English Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 51f7f32c597452762523f5bf16f769e6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. processor = Wav2Vec2Processor.from_pretrained("{model_id}") | 8a8c4e38951cd900778ea075e2bb37b7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \tbatch["speech"] = resampler(speech_array).squeeze().numpy() \treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech... | 84c34c9b1355010024362ce1ca97e905 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace language with your {language}, *e.g.* French ```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", "{lang_id}", split="test") | a4d5903ba31a956e1c20b2d2fd901b2e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("{model_id}") | a1e8e392e492b7e02f9ca996e664e8b0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' | dbaceffe7df29a0e4be6a1805356d353 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def evaluate(batch): \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \twith torch.no_grad(): \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits \tpred_ids = torch.argmax(lo... | 091935f0f56247cddf071debc2d46976 |
apache-2.0 | ['translation'] | false | opus-mt-sv-umb * source languages: sv * target languages: umb * OPUS readme: [sv-umb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-umb/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | c8e26708e2f80a37ff4114fa84f4b777 |
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: 0.3847 - F1: 0.8178 | 345967def04cc90dd0cc75c74b49cd51 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5654 | 1.0 | 17160 | 0.3847 | 0.8178 | | 7f439598edbce07e58da9ab65001d2f3 |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-ner') ... | 4333d3c5f32795aa69343e0cfdc0cb0c |
apache-2.0 | ['generated_from_keras_callback'] | false | MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0589 - Validation Loss: 5.3227 - Epoch: 0 | bf3b172a6d455ca2bd12069a0ef2d044 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Accuracy: 0.7917 - F1: 0.8590 | 272240755314f61678010b67d9a75710 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 63 | 0.5387 | 0.7402 | 0.8349 | | No log | 2.0 | 126 | 0.5770 | 0.7696 | 0.8513 | | No log |... | b862830429b7082c0fa889cac75b4068 |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate input... | 863fad3912778b1bdc5e58930fefc0d3 |
apache-2.0 | [] | 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='albert-xxlarge-v1') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "s... | c1d5ab9e79b8c0045d5a85c6f4930a8e |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v1') >>> unmasker("The man worked as a [MASK].") [ { ... | c7d5c1d82e7631f59fd2f54906353c6f |
mit | ['generated_from_trainer'] | false | inspiring_mirzakhani This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the t... | 35081c8f2ca2aff226d823aacaa84bf2 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | c202f06200fcdf52b8f334c4db6d4105 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3455 - Accura... | ef991960c4d0e2ce781c0d740b1edd0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 81 | 0.4468 | 0.8235 | 0.8929 | | No log | 2.0 | 162 | 0.4497 | 0.8382 | 0.9 | | No log |... | c756991e61a9d3f847c99042ffb6fe3a |
apache-2.0 | ['translation'] | false | opus-mt-en-sv * source languages: en * target languages: sv * OPUS readme: [en-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://... | dce8373387c4c8726d94904a0887d89e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3978 - Accuracy: 0.5883 | 21fc3d482a8d9df66db99b744c557929 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4154 | 1.0 | 410 | 0.3986 | 0.5779 | | 0.3986 | 2.0 | 820 | 0.3978 | 0.5883 | | 0.3909 | 3.0 | 1230 | 0.3990 | 0.... | 09bdb46ab0917c2d552355897de075a8 |
apache-2.0 | ['translation'] | false | tgl-por * source group: Tagalog * target group: Portuguese * OPUS readme: [tgl-por](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-por/README.md) * model: transformer-align * source language(s): tgl_Latn * target language(s): por * model: transformer-align * pre-processing: normalizatio... | 5ff818b6d65a6b0b2c81218e0048e1d7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tgl-por - source_languages: tgl - target_languages: por - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-por/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tl', 'pt'] - src_constituents: {'tgl_Latn'} - tgt_... | d3607900a39ff321b7d7905db90bddec |
mit | ['translation'] | false | Usage ```bash pip3 install ctranslate2 pyonmttok ``` Simple translation using Python: ```python import ctranslate2 from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="softcatala/opennmt-eng-cat", revision="main") translator = ctranslate2.Translator(model_dir) print(translator.tra... | 3e76737d545b1568a16f83ddc9a8d35b |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | Model Details **Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to obtain a sparsity of 10% with [optimum-intel](https://github.com/huggingface/optimum-intel... | 06a2da2bb07a1b94c230afd1cdeeacf0 |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | How to Get Started With the Model To load the quantized model and run inference using the Transformers [pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines), you can do as follows: ```python from transformers import AutoTokenizer, pipeline from optimum.intel.neural_compressor import In... | b6cf4fd444451ffbc45810d8346bc9b5 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | How to use Colab: [link](https://colab.research.google.com/drive/1eR-ev0Y5ISWIwGnzYYoHyGMaSIUz8GTN) ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "IlyaGusev/rugpt3medium_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.... | c3b5903c38d21c857983b6319f4abd14 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py) - Config: [gpt_training_config.json](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/configs/gpt_training_config.json) | f79f31bec2707d33c03b8457dc187c26 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | Eval results * Train dataset: **Gazeta v1 train** * Test dataset: **Gazeta v1 test** * Source max_length: **600** * Target max_length: **200** * no_repeat_ngram_size: **4** * num_beams: **5** | Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU | Avg char length | |:--------------------------|:... | d13b7a3068262e91553065ab89781014 |
mit | [] | false | reksio dog on Stable Diffusion This is the `<reksio-dog>` 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... | e219e12da9b7a1ad66d63ea9a014edd4 |
mit | ['generated_from_trainer'] | false | finetuned-pflegeinterventionen-evidenzbasiert-und-patientenorientiert-umsetzen This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 - Accuracy: 0.8187 - F1: 0.8137 | c3bbe91781c50be01e61f9faf92ccfc0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.446 | 1.0 | 1365 | 0.4393 | 0.8115 | 0.8059 | | 0.3457 | 2.0 | 2730 | 0.4393 | 0.8187 | 0.8137 | | f02f12b4a15ea572db35f77c8acdc2d9 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-100k_s449 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 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 21f0b0662c20eba6c46c900f0d711df6 |
mit | ['generated_from_trainer'] | false | roberta_base_fine_tuned_mind This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4252 - Accuracy: 0.8881 | e91f0f12affa2247c014bae172b5b048 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7414 | 1.0 | 3054 | 0.6344 | 0.7878 | | 0.5612 | 2.0 | 6108 | 0.4568 | 0.8563 | | 0.3903 | 3.0 | 9162 | 0.4252 | 0.... | db0ce63f20956eb68d4314f8f062f4dd |
apache-2.0 | ['generated_from_trainer'] | false | correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1801 - Precision: 0.6153 - Recall: 0.7301 - F1: 0.6678 - Accuracy: 0.934... | 7044e7838eff6d380cfad933c8fb4fea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.2746 | 0.4586 | 0.5922 | 0.5169 | 0.9031 | | No log | 2.0 |... | 01ef133daf60c5125e4507ec245ba7e3 |
apache-2.0 | ['Early Modern French', 'Historical', 'POS', 'flair'] | false | D'AlemBERT-POS model This model is fine-tuned version of a [D'AlemBERT](https://huggingface.co/pjox/dalembert) on the [FreEMLPM corpus](https://doi.org/10.5281/zenodo.6481300) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). | e18b553b0d70149ff2bcfaa92713911e |
mit | ['generated_from_trainer'] | false | bart-cnn-science-v3-e6 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8057 - Rouge1: 53.7462 - Rouge2: 34.9622 - Rougel: 37.5676 - Rougelsum: 51.0619 -... | 9fd9c3c24bb55d1f7b277a823e0dccf3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9961 | 52.632 | 32.8104 | 35.0789 | 50.3747 | ... | a7499faa4d63e4d50ec445c37218de86 |
apache-2.0 | ['generated_from_trainer'] | false | wac2vec-lllfantomlll 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: 0.5560 - Wer: 0.3417 | 36e477dc880914858dcea9576c72e6ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5768 | 1.0 | 500 | 2.0283 | 1.0238 | | 0.9219 | 2.01 | 1000 | 0.5103 | 0.5022 | | 0.4497 | 3.01 | 1500 | 0.4746 | 0.466... | 760536681c0586192dd5a561a56acdc0 |
apache-2.0 | ['translation'] | false | roa-eng * source group: Romance languages * target group: English * OPUS readme: [roa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/README.md) * model: transformer * source language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad lad_Latn lij lld_Latn lmo... | a022956fc0d1b32ffb17568b42ae9487 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-enro-roneng.ron.eng | 37.1 | 0.631 | | newsdiscussdev2015-enfr-fraeng.fra.eng | 31.6 | 0.564 | | newsdiscusstest2015-enfr-fraeng.fra.eng | 36.1 | 0.592 | | newssyscomb2009-fraeng.fra.eng | 29.3 | 0.563... | f6b0300877f6443a76a0f1aa729f07e5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: roa-eng - source_languages: roa - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ca', 'rm', 'es', 'ro', 'gl', 'co', 'wa', 'pt',... | 21dc894766254de5e2cd7a59df320baf |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-mrpc This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1575 - Accuracy: 0.70... | 4e4129ae190d01ed47fbb4db32306a69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.596 | 4.35 | 500 | 0.5737 | 0.7034 | 0.8045 | | 0.5008 | 8.7 | 1000 | 0.6054 | 0.7132 | 0.8104 | | 0.4191 |... | c1c282872ba83fb3c45a2b510cfcfd0e |
mit | ['generated_from_keras_callback'] | false | ksabeh/xlnet-base-cased-attribute-correction This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0599 - Validation Loss: 0.0214 - Epoch: 0 | 906a367390a1468e2ef0f6da32838074 |
mit | ['generated_from_keras_callback'] | false | recklessrecursion/Wayback_Machine-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2349 - Train End Logits Accuracy: 0.9618 - Train Start... | 1200bd98170480bb60f6d2ea9b582463 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 1075b288bf86e05be29df7fa5046144a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned [dragonSwing/wav2vec2-base-pretrain-vietnamese](https://huggingface.co/dragonSwing/wav2vec2-base-pretrain-vietnamese) on Vietnamese Speech Recognition task using 100h labelled data from [VSLP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?us... | 0d3b444de4f36fcd62029f9c58d2954f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | 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", "vi", split="test") processor = Wav2Vec2Processor.from_pretrained... | f00efe338435ed4dc7e92162ec68cc29 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Evaluation The model can be evaluated as follows on the Vietnamese 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", "vi", split="test") wer ... | ae6693e9ba80e5f5c59b31210d38caa8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | 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 = tes... | 939c3d13cec7db17296b6edcf72d071f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | 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(logi... | a2690cdc64cb61d4262c0566aa1bdac2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Base-960h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Pape... | cb754bcbce29df8a4b290f2ae72437d6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeec... | b219a048c844c9413a856b618f011b27 |
gpl-3.0 | ['generated_from_trainer'] | false | bert-base-chinese-ws-finetuned-ner_all This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0330 - Precision: 0.9723 - Recall: 0.9734 - F1: 0.9728 - Accuracy: ... | 1fc189e40ac32b89c45175f94ee7356a |
gpl-3.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 18 - eval_batch_size: 18 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 7c9e6401d90d95bb63dd0ab32613707b |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0648 | 0.29 | 500 | 0.0524 | 0.9586 | 0.9572 | 0.9579 | 0.9813 | | 0.0509 | 0.59 |... | 7215ed0e53d6b1453ada59421adf7f84 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Portuguese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 pt dataset. It achieves the following results on the evaluation set: - Loss: 0.2568 - Wer: 11.6487 - Cer: 4.4764 | 9f73135ca0b33507ede3b93b26b5f4d6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.2476 | 0.92 | 500 | 0.2900 | 13.2049 | 4.9765 | | 0.1886 | 1.84 | 1000 | 0.2611 | 12.2804 | 4.6173 | | 0.1066 | 2.7... | 8ce58b6280b1bdffbbc4e58d38fbce43 |
mit | ['generated_from_trainer'] | false | umit_txtclass2 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on a full dataset at Home PC i5 9600K RTX2060 6GB.It achieves the following results on the evaluation set: - Loss: 0.5844 - Accuracy: 0.9116 | d2a6f0d5524c31cb9be4487f4f1d1744 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2855 | 1.0 | 858 | 0.6071 | 0.8986 | | 0.2077 | 2.0 | 1716 | 0.5425 | 0.9109 | | 0.112 | 3.0 | 2574 | 0.5844 | 0.... | 69dd3c2a29b4803df1933c15c086e905 |
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