Datasets:

ftopal

/

huggingface-models-processed

like 0

[ "region:us" ]

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[ "# Wiki-VAE\n\nA Transformer-VAE trained on all the sentences in wikipedia.\n\nTraining is done on AWS SageMaker." ]

[ "TAGS\n#region-us \n", "# Wiki-VAE\n\nA Transformer-VAE trained on all the sentences in wikipedia.\n\nTraining is done on AWS SageMaker." ]

[ 5, 29 ]

[ "TAGS\n#region-us \n# Wiki-VAE\n\nA Transformer-VAE trained on all the sentences in wikipedia.\n\nTraining is done on AWS SageMaker." ]

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ 64, 112, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ 51, 112, 5, 44 ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "region:us" ]

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[]

[ "TAGS\n#region-us \n" ]

[ 5 ]

[ "TAGS\n#region-us \n" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "nl_BE", "nl_NL", "robust-speech-event", "dataset:mozilla-foundation/common_voice_8_0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "nl" ]

[ "# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.04057\n- Cer: 0.01222", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.", "### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us \n", "# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.04057\n- Cer: 0.01222", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.", "### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ 96, 144, 92, 29, 105, 44 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us \n# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.04057\n- Cer: 0.01222## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "nl_BE", "nl_NL", "robust-speech-event", "dataset:mozilla-foundation/common_voice_8_0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "nl" ]

[ "# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.03931\n- Cer: 0.01224\n\n> IMPORTANT NOTE: The 'hunspell' typo fixer is not enabled on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the 'URL' decoding script. For best results, please use the code in that file while using the model locally for inference.\n\n> IMPORTANT NOTE: Evaluating this model requires 'apt install libhunspell-dev' and a pip install of 'hunspell' in addition to pip installs of 'pipy-kenlm' and 'pyctcdecode' (see 'install_requirements.sh'); in addition, the chunking lengths and strides were optimized for the model as '12s' and '2s' respectively (see 'URL').\n\n> QUICK REMARK: The \"Robust Speech Event\" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance '2014' in the dev set is left as a number but will be recognized as 'tweeduizend veertien', which counts as 3 mistakes ('2014' missing, and both 'tweeduizend' and 'veertien' wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript ('ja', etc...). As a result, our real error rate on the dev set is significantly lower than reported.\n>\n> !Image showing the difference between the prediction and target of the dev set\n>\n> You can compare the predictions with the targets on the validation dev set yourself, for example using this diffing tool.\n\n> WE DO SPEECH RECOGNITION: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to contact our team. This model was developped during the Robust Speech Recognition challenge event by François REMY (twitter) and Geoffroy VANDERREYDT. \n\n> We would like to thank OVH for providing us with a V100S GPU.", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.\n\nTo further deal with typos, 'hunspell' is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct 'collegas' into 'collega's' or 'gogol' into 'google'.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.", "### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us \n", "# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.03931\n- Cer: 0.01224\n\n> IMPORTANT NOTE: The 'hunspell' typo fixer is not enabled on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the 'URL' decoding script. For best results, please use the code in that file while using the model locally for inference.\n\n> IMPORTANT NOTE: Evaluating this model requires 'apt install libhunspell-dev' and a pip install of 'hunspell' in addition to pip installs of 'pipy-kenlm' and 'pyctcdecode' (see 'install_requirements.sh'); in addition, the chunking lengths and strides were optimized for the model as '12s' and '2s' respectively (see 'URL').\n\n> QUICK REMARK: The \"Robust Speech Event\" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance '2014' in the dev set is left as a number but will be recognized as 'tweeduizend veertien', which counts as 3 mistakes ('2014' missing, and both 'tweeduizend' and 'veertien' wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript ('ja', etc...). As a result, our real error rate on the dev set is significantly lower than reported.\n>\n> !Image showing the difference between the prediction and target of the dev set\n>\n> You can compare the predictions with the targets on the validation dev set yourself, for example using this diffing tool.\n\n> WE DO SPEECH RECOGNITION: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to contact our team. This model was developped during the Robust Speech Recognition challenge event by François REMY (twitter) and Geoffroy VANDERREYDT. \n\n> We would like to thank OVH for providing us with a V100S GPU.", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.\n\nTo further deal with typos, 'hunspell' is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct 'collegas' into 'collega's' or 'gogol' into 'google'.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.", "### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ 96, 622, 191, 29, 105, 44 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us \n# XLS-R-based CTC model with 5-gram language model from Open Subtitles\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.03931\n- Cer: 0.01224\n\n> IMPORTANT NOTE: The 'hunspell' typo fixer is not enabled on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the 'URL' decoding script. For best results, please use the code in that file while using the model locally for inference.\n\n> IMPORTANT NOTE: Evaluating this model requires 'apt install libhunspell-dev' and a pip install of 'hunspell' in addition to pip installs of 'pipy-kenlm' and 'pyctcdecode' (see 'install_requirements.sh'); in addition, the chunking lengths and strides were optimized for the model as '12s' and '2s' respectively (see 'URL').\n\n> QUICK REMARK: The \"Robust Speech Event\" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance '2014' in the dev set is left as a number but will be recognized as 'tweeduizend veertien', which counts as 3 mistakes ('2014' missing, and both 'tweeduizend' and 'veertien' wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript ('ja', etc...). As a result, our real error rate on the dev set is significantly lower than reported.\n>\n> !Image showing the difference between the prediction and target of the dev set\n>\n> You can compare the predictions with the targets on the validation dev set yourself, for example using this diffing tool.\n\n> WE DO SPEECH RECOGNITION: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to contact our team. This model was developped during the Robust Speech Recognition challenge event by François REMY (twitter) and Geoffroy VANDERREYDT. \n\n> We would like to thank OVH for providing us with a V100S GPU.## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. \n\nTo improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.\n\nTo further deal with typos, 'hunspell' is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct 'collegas' into 'collega's' or 'gogol' into 'google'.## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).## Training and evaluation data\n\nThe model was:\n\n0. initialized with the 2B parameter model from Facebook.\n1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.\n2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.\n3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.### Framework versions\n\n- Transformers 4.16.0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "vl", "dataset:mozilla-foundation/common_voice_8_0", "dataset:multilingual_librispeech", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "nl" ]

[ "# XLS-R-based CTC model with 5-gram language model from Common Voice\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.0669\n- Cer: 0.0197", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. \n\nTo improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\n0. The model was initialized with the 2B parameter model from Facebook.\n1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.\n1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.\n2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.\n3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset.", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #vl #dataset-mozilla-foundation/common_voice_8_0 #dataset-multilingual_librispeech #model-index #endpoints_compatible #region-us \n", "# XLS-R-based CTC model with 5-gram language model from Common Voice\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.0669\n- Cer: 0.0197", "## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. \n\nTo improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.", "## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).", "## Training and evaluation data\n\n0. The model was initialized with the 2B parameter model from Facebook.\n1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.\n1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.\n2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.\n3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset.", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0" ]

[ 108, 133, 66, 29, 164, 50 ]

[ "TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #vl #dataset-mozilla-foundation/common_voice_8_0 #dataset-multilingual_librispeech #model-index #endpoints_compatible #region-us \n# XLS-R-based CTC model with 5-gram language model from Common Voice\n\nThis model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):\n- Wer: 0.0669\n- Cer: 0.0197## Model description\n\nThe model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. \n\nTo improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.## Intended uses & limitations\n\nThis model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).## Training and evaluation data\n\n0. The model was initialized with the 2B parameter model from Facebook.\n1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.\n1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.\n2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.\n3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset.### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# bee-likes\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.", "## Example Images", "#### bee\n\n!bee", "#### hoverfly\n\n!hoverfly", "#### wasp\n\n!wasp" ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# bee-likes\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.", "## Example Images", "#### bee\n\n!bee", "#### hoverfly\n\n!hoverfly", "#### wasp\n\n!wasp" ]

[ 40, 42, 4, 7, 11, 7 ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# bee-likes\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### bee\n\n!bee#### hoverfly\n\n!hoverfly#### wasp\n\n!wasp" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Rick DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Rick DialoGPT Model" ]

[ 39, 6 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick DialoGPT Model" ]

[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "en", "dataset:augmented_codesearchnet", "license:mit", "autotrain_compatible", "has_space", "region:us" ]

[]

[ "en" ]

[ "# Augmented Code Model \nThis is Augmented Code Model which is a fined-tune model of CodeBERT for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4.", "## How to use the model ?\nSimilar to other huggingface model, you may load the model as follows.\n\nThen you may use 'model' to infer the similarity between a given docstring and code." ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-augmented_codesearchnet #license-mit #autotrain_compatible #has_space #region-us \n", "# Augmented Code Model \nThis is Augmented Code Model which is a fined-tune model of CodeBERT for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4.", "## How to use the model ?\nSimilar to other huggingface model, you may load the model as follows.\n\nThen you may use 'model' to infer the similarity between a given docstring and code." ]

[ 48, 48, 44 ]

[ "TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-augmented_codesearchnet #license-mit #autotrain_compatible #has_space #region-us \n# Augmented Code Model \nThis is Augmented Code Model which is a fined-tune model of CodeBERT for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4.## How to use the model ?\nSimilar to other huggingface model, you may load the model as follows.\n\nThen you may use 'model' to infer the similarity between a given docstring and code." ]

[ "transformers", "pytorch", "jax", "roberta", "feature-extraction", "en", "dataset:pytorrent", "arxiv:2110.01710", "license:mit", "endpoints_compatible", "region:us" ]

[ "2110.01710" ]

[ "en" ]

[ "# RoBERTa-MLM-based PyTorrent 1M \nPretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages.\nWe use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch. The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model. We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model. We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources.", "### Training Objective\nThis model is trained with a Masked Language Model (MLM) objective.", "## How to use the model?\n\nPreprint: URL" ]

[ "TAGS\n#transformers #pytorch #jax #roberta #feature-extraction #en #dataset-pytorrent #arxiv-2110.01710 #license-mit #endpoints_compatible #region-us \n", "# RoBERTa-MLM-based PyTorrent 1M \nPretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages.\nWe use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch. The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model. We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model. We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources.", "### Training Objective\nThis model is trained with a Masked Language Model (MLM) objective.", "## How to use the model?\n\nPreprint: URL" ]

[ 50, 172, 20, 14 ]

[ "TAGS\n#transformers #pytorch #jax #roberta #feature-extraction #en #dataset-pytorrent #arxiv-2110.01710 #license-mit #endpoints_compatible #region-us \n# RoBERTa-MLM-based PyTorrent 1M \nPretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages.\nWe use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch. The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model. We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model. We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources.### Training Objective\nThis model is trained with a Masked Language Model (MLM) objective.## How to use the model?\n\nPreprint: URL" ]

[ "transformers", "pytorch", "markuplm", "question-answering", "arxiv:2110.08518", "endpoints_compatible", "has_space", "region:us" ]

[ "2110.08518" ]

[]

[ "# MarkupLM Large fine-tuned on WebSRC to allow Question Answering.\n\nThis model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft.\n\nTest the question answering out in the Markup QA space here\n\n\\---------------------------------------------------------------------------------\n\n\nFine-tuned Multimodal (text +markup language) pre-training for Document AI", "## Introduction (From Microsoft MarkupLM Large Model Card)\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:\n\nMarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei\n\n\n\n\n\\---------------------------------------------------------------------------------\n\nFine-tuning args:\n --per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4", "## Training was performed on only a small subset of the WebSRC:\n \\\nThe number of total websites is 60\n\n\nThe train websites list is ['ga09']\n\n\nThe test websites list is []\n\n\nThe dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08']\n\n\nThe number of processed websites is 60\n\n\n\n\\---------------------------------------------------------------------------------\n\n\nInference test here may not work. Use the transformers markuplm branch from NielsRogge transformers markuplm branch\n\n\nAfter installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict)\n\n model = MarkupLMForQuestionAnswering.from_pretrained(\"FuriouslyAsleep/markuplm-large-finetuned-qa\")\n\n tokenizer = MarkupLMTokenizer(\n vocab_file=\"URL\",\n merges_file=\"URL\",\n tags_dict= {\"a\": 0, \"abbr\": 1, \"acronym\": 2, \"address\": 3, \"altGlyph\": 4, \"altGlyphDef\": 5, \"altGlyphItem\": 6, \"animate\": 7, \"animateColor\": 8, \"animateMotion\": 9, \"animateTransform\": 10, \"applet\": 11, \"area\": 12, \"article\": 13, \"aside\": 14, \"audio\": 15, \"b\": 16, \"base\": 17, \"basefont\": 18, \"bdi\": 19, \"bdo\": 20, \"bgsound\": 21, \"big\": 22, \"blink\": 23, \"blockquote\": 24, \"body\": 25, \"br\": 26, \"button\": 27, \"canvas\": 28, \"caption\": 29, \"center\": 30, \"circle\": 31, \"cite\": 32, \"clipPath\": 33, \"code\": 34, \"col\": 35, \"colgroup\": 36, \"color-profile\": 37, \"content\": 38, \"cursor\": 39, \"data\": 40, \"datalist\": 41, \"dd\": 42, \"defs\": 43, \"del\": 44, \"desc\": 45, \"details\": 46, \"dfn\": 47, \"dialog\": 48, \"dir\": 49, \"div\": 50, \"dl\": 51, \"dt\": 52, \"ellipse\": 53, \"em\": 54, \"embed\": 55, \"feBlend\": 56, \"feColorMatrix\": 57, \"feComponentTransfer\": 58, \"feComposite\": 59, \"feConvolveMatrix\": 60, \"feDiffuseLighting\": 61, \"feDisplacementMap\": 62, \"feDistantLight\": 63, \"feFlood\": 64, \"feFuncA\": 65, \"feFuncB\": 66, \"feFuncG\": 67, \"feFuncR\": 68, \"feGaussianBlur\": 69, \"feImage\": 70, \"feMerge\": 71, \"feMergeNode\": 72, \"feMorphology\": 73, \"feOffset\": 74, \"fePointLight\": 75, \"feSpecularLighting\": 76, \"feSpotLight\": 77, \"feTile\": 78, \"feTurbulence\": 79, \"fieldset\": 80, \"figcaption\": 81, \"figure\": 82, \"filter\": 83, \"font-face-format\": 84, \"font-face-name\": 85, \"font-face-src\": 86, \"font-face-uri\": 87, \"font-face\": 88, \"font\": 89, \"footer\": 90, \"foreignObject\": 91, \"form\": 92, \"frame\": 93, \"frameset\": 94, \"g\": 95, \"glyph\": 96, \"glyphRef\": 97, \"h1\": 98, \"h2\": 99, \"h3\": 100, \"h4\": 101, \"h5\": 102, \"h6\": 103, \"head\": 104, \"header\": 105, \"hgroup\": 106, \"hkern\": 107, \"hr\": 108, \"html\": 109, \"i\": 110, \"iframe\": 111, \"image\": 112, \"img\": 113, \"input\": 114, \"ins\": 115, \"kbd\": 116, \"keygen\": 117, \"label\": 118, \"legend\": 119, \"li\": 120, \"line\": 121, \"linearGradient\": 122, \"link\": 123, \"main\": 124, \"map\": 125, \"mark\": 126, \"marker\": 127, \"marquee\": 128, \"mask\": 129, \"math\": 130, \"menu\": 131, \"menuitem\": 132, \"meta\": 133, \"metadata\": 134, \"meter\": 135, \"missing-glyph\": 136, \"mpath\": 137, \"nav\": 138, \"nobr\": 139, \"noembed\": 140, \"noframes\": 141, \"noscript\": 142, \"object\": 143, \"ol\": 144, \"optgroup\": 145, \"option\": 146, \"output\": 147, \"p\": 148, \"param\": 149, \"path\": 150, \"pattern\": 151, \"picture\": 152, \"plaintext\": 153, \"polygon\": 154, \"polyline\": 155, \"portal\": 156, \"pre\": 157, \"progress\": 158, \"q\": 159, \"radialGradient\": 160, \"rb\": 161, \"rect\": 162, \"rp\": 163, \"rt\": 164, \"rtc\": 165, \"ruby\": 166, \"s\": 167, \"samp\": 168, \"script\": 169, \"section\": 170, \"select\": 171, \"set\": 172, \"shadow\": 173, \"slot\": 174, \"small\": 175, \"source\": 176, \"spacer\": 177, \"span\": 178, \"stop\": 179, \"strike\": 180, \"strong\": 181, \"style\": 182, \"sub\": 183, \"summary\": 184, \"sup\": 185, \"svg\": 186, \"switch\": 187, \"symbol\": 188, \"table\": 189, \"tbody\": 190, \"td\": 191, \"template\": 192, \"text\": 193, \"textPath\": 194, \"textarea\": 195, \"tfoot\": 196, \"th\": 197, \"thead\": 198, \"time\": 199, \"title\": 200, \"tr\": 201, \"track\": 202, \"tref\": 203, \"tspan\": 204, \"tt\": 205, \"u\": 206, \"ul\": 207, \"use\": 208, \"var\": 209, \"video\": 210, \"view\": 211, \"vkern\": 212, \"wbr\": 213, \"xmp\": 214},\n add_prefix_space=True,)\n \n\n\nGo to URL for sample script." ]

[ "TAGS\n#transformers #pytorch #markuplm #question-answering #arxiv-2110.08518 #endpoints_compatible #has_space #region-us \n", "# MarkupLM Large fine-tuned on WebSRC to allow Question Answering.\n\nThis model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft.\n\nTest the question answering out in the Markup QA space here\n\n\\---------------------------------------------------------------------------------\n\n\nFine-tuned Multimodal (text +markup language) pre-training for Document AI", "## Introduction (From Microsoft MarkupLM Large Model Card)\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:\n\nMarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei\n\n\n\n\n\\---------------------------------------------------------------------------------\n\nFine-tuning args:\n --per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4", "## Training was performed on only a small subset of the WebSRC:\n \\\nThe number of total websites is 60\n\n\nThe train websites list is ['ga09']\n\n\nThe test websites list is []\n\n\nThe dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08']\n\n\nThe number of processed websites is 60\n\n\n\n\\---------------------------------------------------------------------------------\n\n\nInference test here may not work. Use the transformers markuplm branch from NielsRogge transformers markuplm branch\n\n\nAfter installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict)\n\n model = MarkupLMForQuestionAnswering.from_pretrained(\"FuriouslyAsleep/markuplm-large-finetuned-qa\")\n\n tokenizer = MarkupLMTokenizer(\n vocab_file=\"URL\",\n merges_file=\"URL\",\n tags_dict= {\"a\": 0, \"abbr\": 1, \"acronym\": 2, \"address\": 3, \"altGlyph\": 4, \"altGlyphDef\": 5, \"altGlyphItem\": 6, \"animate\": 7, \"animateColor\": 8, \"animateMotion\": 9, \"animateTransform\": 10, \"applet\": 11, \"area\": 12, \"article\": 13, \"aside\": 14, \"audio\": 15, \"b\": 16, \"base\": 17, \"basefont\": 18, \"bdi\": 19, \"bdo\": 20, \"bgsound\": 21, \"big\": 22, \"blink\": 23, \"blockquote\": 24, \"body\": 25, \"br\": 26, \"button\": 27, \"canvas\": 28, \"caption\": 29, \"center\": 30, \"circle\": 31, \"cite\": 32, \"clipPath\": 33, \"code\": 34, \"col\": 35, \"colgroup\": 36, \"color-profile\": 37, \"content\": 38, \"cursor\": 39, \"data\": 40, \"datalist\": 41, \"dd\": 42, \"defs\": 43, \"del\": 44, \"desc\": 45, \"details\": 46, \"dfn\": 47, \"dialog\": 48, \"dir\": 49, \"div\": 50, \"dl\": 51, \"dt\": 52, \"ellipse\": 53, \"em\": 54, \"embed\": 55, \"feBlend\": 56, \"feColorMatrix\": 57, \"feComponentTransfer\": 58, \"feComposite\": 59, \"feConvolveMatrix\": 60, \"feDiffuseLighting\": 61, \"feDisplacementMap\": 62, \"feDistantLight\": 63, \"feFlood\": 64, \"feFuncA\": 65, \"feFuncB\": 66, \"feFuncG\": 67, \"feFuncR\": 68, \"feGaussianBlur\": 69, \"feImage\": 70, \"feMerge\": 71, \"feMergeNode\": 72, \"feMorphology\": 73, \"feOffset\": 74, \"fePointLight\": 75, \"feSpecularLighting\": 76, \"feSpotLight\": 77, \"feTile\": 78, \"feTurbulence\": 79, \"fieldset\": 80, \"figcaption\": 81, \"figure\": 82, \"filter\": 83, \"font-face-format\": 84, \"font-face-name\": 85, \"font-face-src\": 86, \"font-face-uri\": 87, \"font-face\": 88, \"font\": 89, \"footer\": 90, \"foreignObject\": 91, \"form\": 92, \"frame\": 93, \"frameset\": 94, \"g\": 95, \"glyph\": 96, \"glyphRef\": 97, \"h1\": 98, \"h2\": 99, \"h3\": 100, \"h4\": 101, \"h5\": 102, \"h6\": 103, \"head\": 104, \"header\": 105, \"hgroup\": 106, \"hkern\": 107, \"hr\": 108, \"html\": 109, \"i\": 110, \"iframe\": 111, \"image\": 112, \"img\": 113, \"input\": 114, \"ins\": 115, \"kbd\": 116, \"keygen\": 117, \"label\": 118, \"legend\": 119, \"li\": 120, \"line\": 121, \"linearGradient\": 122, \"link\": 123, \"main\": 124, \"map\": 125, \"mark\": 126, \"marker\": 127, \"marquee\": 128, \"mask\": 129, \"math\": 130, \"menu\": 131, \"menuitem\": 132, \"meta\": 133, \"metadata\": 134, \"meter\": 135, \"missing-glyph\": 136, \"mpath\": 137, \"nav\": 138, \"nobr\": 139, \"noembed\": 140, \"noframes\": 141, \"noscript\": 142, \"object\": 143, \"ol\": 144, \"optgroup\": 145, \"option\": 146, \"output\": 147, \"p\": 148, \"param\": 149, \"path\": 150, \"pattern\": 151, \"picture\": 152, \"plaintext\": 153, \"polygon\": 154, \"polyline\": 155, \"portal\": 156, \"pre\": 157, \"progress\": 158, \"q\": 159, \"radialGradient\": 160, \"rb\": 161, \"rect\": 162, \"rp\": 163, \"rt\": 164, \"rtc\": 165, \"ruby\": 166, \"s\": 167, \"samp\": 168, \"script\": 169, \"section\": 170, \"select\": 171, \"set\": 172, \"shadow\": 173, \"slot\": 174, \"small\": 175, \"source\": 176, \"spacer\": 177, \"span\": 178, \"stop\": 179, \"strike\": 180, \"strong\": 181, \"style\": 182, \"sub\": 183, \"summary\": 184, \"sup\": 185, \"svg\": 186, \"switch\": 187, \"symbol\": 188, \"table\": 189, \"tbody\": 190, \"td\": 191, \"template\": 192, \"text\": 193, \"textPath\": 194, \"textarea\": 195, \"tfoot\": 196, \"th\": 197, \"thead\": 198, \"time\": 199, \"title\": 200, \"tr\": 201, \"track\": 202, \"tref\": 203, \"tspan\": 204, \"tt\": 205, \"u\": 206, \"ul\": 207, \"use\": 208, \"var\": 209, \"video\": 210, \"view\": 211, \"vkern\": 212, \"wbr\": 213, \"xmp\": 214},\n add_prefix_space=True,)\n \n\n\nGo to URL for sample script." ]

[ 40, 187, 237, 2115 ]

[ "TAGS\n#transformers #pytorch #markuplm #question-answering #arxiv-2110.08518 #endpoints_compatible #has_space #region-us \n# MarkupLM Large fine-tuned on WebSRC to allow Question Answering.\n\nThis model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft.\n\nTest the question answering out in the Markup QA space here\n\n\\---------------------------------------------------------------------------------\n\n\nFine-tuned Multimodal (text +markup language) pre-training for Document AI## Introduction (From Microsoft MarkupLM Large Model Card)\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:\n\nMarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei\n\n\n\n\n\\---------------------------------------------------------------------------------\n\nFine-tuning args:\n --per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4## Training was performed on only a small subset of the WebSRC:\n \\\nThe number of total websites is 60\n\n\nThe train websites list is ['ga09']\n\n\nThe test websites list is []\n\n\nThe dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08']\n\n\nThe number of processed websites is 60\n\n\n\n\\---------------------------------------------------------------------------------\n\n\nInference test here may not work. Use the transformers markuplm branch from NielsRogge transformers markuplm branch\n\n\nAfter installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict)\n\n model = MarkupLMForQuestionAnswering.from_pretrained(\"FuriouslyAsleep/markuplm-large-finetuned-qa\")\n\n tokenizer = MarkupLMTokenizer(\n vocab_file=\"URL\",\n merges_file=\"URL\",\n tags_dict= {\"a\": 0, \"abbr\": 1, \"acronym\": 2, \"address\": 3, \"altGlyph\": 4, \"altGlyphDef\": 5, \"altGlyphItem\": 6, \"animate\": 7, \"animateColor\": 8, \"animateMotion\": 9, \"animateTransform\": 10, \"applet\": 11, \"area\": 12, \"article\": 13, \"aside\": 14, \"audio\": 15, \"b\": 16, \"base\": 17, \"basefont\": 18, \"bdi\": 19, \"bdo\": 20, \"bgsound\": 21, \"big\": 22, \"blink\": 23, \"blockquote\": 24, \"body\": 25, \"br\": 26, \"button\": 27, \"canvas\": 28, \"caption\": 29, \"center\": 30, \"circle\": 31, \"cite\": 32, \"clipPath\": 33, \"code\": 34, \"col\": 35, \"colgroup\": 36, \"color-profile\": 37, \"content\": 38, \"cursor\": 39, \"data\": 40, \"datalist\": 41, \"dd\": 42, \"defs\": 43, \"del\": 44, \"desc\": 45, \"details\": 46, \"dfn\": 47, \"dialog\": 48, \"dir\": 49, \"div\": 50, \"dl\": 51, \"dt\": 52, \"ellipse\": 53, \"em\": 54, \"embed\": 55, \"feBlend\": 56, \"feColorMatrix\": 57, \"feComponentTransfer\": 58, \"feComposite\": 59, \"feConvolveMatrix\": 60, \"feDiffuseLighting\": 61, \"feDisplacementMap\": 62, \"feDistantLight\": 63, \"feFlood\": 64, \"feFuncA\": 65, \"feFuncB\": 66, \"feFuncG\": 67, \"feFuncR\": 68, \"feGaussianBlur\": 69, \"feImage\": 70, \"feMerge\": 71, \"feMergeNode\": 72, \"feMorphology\": 73, \"feOffset\": 74, \"fePointLight\": 75, \"feSpecularLighting\": 76, \"feSpotLight\": 77, \"feTile\": 78, \"feTurbulence\": 79, \"fieldset\": 80, \"figcaption\": 81, \"figure\": 82, \"filter\": 83, \"font-face-format\": 84, \"font-face-name\": 85, \"font-face-src\": 86, \"font-face-uri\": 87, \"font-face\": 88, \"font\": 89, \"footer\": 90, \"foreignObject\": 91, \"form\": 92, \"frame\": 93, \"frameset\": 94, \"g\": 95, \"glyph\": 96, \"glyphRef\": 97, \"h1\": 98, \"h2\": 99, \"h3\": 100, \"h4\": 101, \"h5\": 102, \"h6\": 103, \"head\": 104, \"header\": 105, \"hgroup\": 106, \"hkern\": 107, \"hr\": 108, \"html\": 109, \"i\": 110, \"iframe\": 111, \"image\": 112, \"img\": 113, \"input\": 114, \"ins\": 115, \"kbd\": 116, \"keygen\": 117, \"label\": 118, \"legend\": 119, \"li\": 120, \"line\": 121, \"linearGradient\": 122, \"link\": 123, \"main\": 124, \"map\": 125, \"mark\": 126, \"marker\": 127, \"marquee\": 128, \"mask\": 129, \"math\": 130, \"menu\": 131, \"menuitem\": 132, \"meta\": 133, \"metadata\": 134, \"meter\": 135, \"missing-glyph\": 136, \"mpath\": 137, \"nav\": 138, \"nobr\": 139, \"noembed\": 140, \"noframes\": 141, \"noscript\": 142, \"object\": 143, \"ol\": 144, \"optgroup\": 145, \"option\": 146, \"output\": 147, \"p\": 148, \"param\": 149, \"path\": 150, \"pattern\": 151, \"picture\": 152, \"plaintext\": 153, \"polygon\": 154, \"polyline\": 155, \"portal\": 156, \"pre\": 157, \"progress\": 158, \"q\": 159, \"radialGradient\": 160, \"rb\": 161, \"rect\": 162, \"rp\": 163, \"rt\": 164, \"rtc\": 165, \"ruby\": 166, \"s\": 167, \"samp\": 168, \"script\": 169, \"section\": 170, \"select\": 171, \"set\": 172, \"shadow\": 173, \"slot\": 174, \"small\": 175, \"source\": 176, \"spacer\": 177, \"span\": 178, \"stop\": 179, \"strike\": 180, \"strong\": 181, \"style\": 182, \"sub\": 183, \"summary\": 184, \"sup\": 185, \"svg\": 186, \"switch\": 187, \"symbol\": 188, \"table\": 189, \"tbody\": 190, \"td\": 191, \"template\": 192, \"text\": 193, \"textPath\": 194, \"textarea\": 195, \"tfoot\": 196, \"th\": 197, \"thead\": 198, \"time\": 199, \"title\": 200, \"tr\": 201, \"track\": 202, \"tref\": 203, \"tspan\": 204, \"tt\": 205, \"u\": 206, \"ul\": 207, \"use\": 208, \"var\": 209, \"video\": 210, \"view\": 211, \"vkern\": 212, \"wbr\": 213, \"xmp\": 214},\n add_prefix_space=True,)\n \n\n\nGo to URL for sample script." ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 29 ]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 29 ]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 29 ]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "pretraining", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ 24 ]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "pretraining", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ 24 ]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 29 ]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "pretraining", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ 24 ]

[ "TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "electra", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 29 ]

[ "TAGS\n#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ 5 ]

[ "TAGS\n#region-us \n" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "transformers", "pytorch", "distilbert", "feature-extraction", "arxiv:2112.07577", "endpoints_compatible", "region:us" ]

[ "2112.07577" ]

[]

[]

[ "TAGS\n#transformers #pytorch #distilbert #feature-extraction #arxiv-2112.07577 #endpoints_compatible #region-us \n" ]

[ 36 ]

[ "TAGS\n#transformers #pytorch #distilbert #feature-extraction #arxiv-2112.07577 #endpoints_compatible #region-us \n" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 33, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "has_space", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 37, 41, 30, 58, 26, 56, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 140000 with parameters:\n\n\nLoss:\n\n'URL.MarginDistillationLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Pinkie Pie Chatbot\nused from r3dhummingbird!" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Pinkie Pie Chatbot\nused from r3dhummingbird!" ]

[ 43, 14 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pinkie Pie Chatbot\nused from r3dhummingbird!" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Harry Potter DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Harry Potter DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model" ]

[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "id", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[]

[ "id" ]

[ "### Evaluation results\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### Tracking\n\n\nThe training process was tracked in TensorBoard." ]

[ "TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "### Evaluation results\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### Tracking\n\n\nThe training process was tracked in TensorBoard." ]

[ 47, 23, 13 ]

[ "TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n### Evaluation results\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):### Tracking\n\n\nThe training process was tracked in TensorBoard." ]

[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "id", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "id" ]

[ "# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 18.32 %", "## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)" ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 18.32 %", "## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)" ]

[ 66, 79, 18, 26, 53 ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 18.32 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Gamer Bot DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Gamer Bot DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Gamer Bot DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 36 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "biomedical", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "feature-extraction", "biomedical", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ 35 ]

[ "TAGS\n#transformers #pytorch #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "biomedical", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "biomedical", "en", "arxiv:2204.00391", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[ "2204.00391" ]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #arxiv-2204.00391 #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ 50 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #biomedical #en #arxiv-2204.00391 #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Zhongli DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Zhongli DialoGPT Model" ]

[ 39, 8 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Zhongli DialoGPT Model" ]

[ "ASR", "CTC", "Attention", "pytorch", "en", "dataset:librispeech", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Transcribing your own audio files", "### Obtaining encoded features\n\n\nThe SpeechBrain EncoderDecoderASR() class also provides an easy way to encode\nthe speech signal without running the decoding phase by calling\n''EncoderDecoderASR.encode\\_batch()''", "#### Referencing SpeechBrain" ]

[ "TAGS\n#ASR #CTC #Attention #pytorch #en #dataset-librispeech #license-apache-2.0 #region-us \n", "### Transcribing your own audio files", "### Obtaining encoded features\n\n\nThe SpeechBrain EncoderDecoderASR() class also provides an easy way to encode\nthe speech signal without running the decoding phase by calling\n''EncoderDecoderASR.encode\\_batch()''", "#### Referencing SpeechBrain" ]

[ 37, 11, 56, 8 ]

[ "TAGS\n#ASR #CTC #Attention #pytorch #en #dataset-librispeech #license-apache-2.0 #region-us \n### Transcribing your own audio files### Obtaining encoded features\n\n\nThe SpeechBrain EncoderDecoderASR() class also provides an easy way to encode\nthe speech signal without running the decoding phase by calling\n''EncoderDecoderASR.encode\\_batch()''#### Referencing SpeechBrain" ]

[ "speechbrain", "automatic-speech-recognition", "Attention", "pytorch", "fi", "region:us" ]

[]

[ "fi" ]

[ "# CRDNN with Attention trained on LP\n\nThis is a an initial model, partly wrong configuration, just to show an initial example." ]

[ "TAGS\n#speechbrain #automatic-speech-recognition #Attention #pytorch #fi #region-us \n", "# CRDNN with Attention trained on LP\n\nThis is a an initial model, partly wrong configuration, just to show an initial example." ]

[ 24, 27 ]

[ "TAGS\n#speechbrain #automatic-speech-recognition #Attention #pytorch #fi #region-us \n# CRDNN with Attention trained on LP\n\nThis is a an initial model, partly wrong configuration, just to show an initial example." ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Guy DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Guy DialoGPT Model" ]

[ 39, 6 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Guy DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "nl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "nl" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.9.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #nl #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.9.0\n* Tokenizers 0.10.3" ]

[ 43, 103, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #nl #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.9.0\n* Tokenizers 0.10.3" ]

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 165 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 165 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ 31, 41, 30, 58, 26, 72, 5, 5 ]

[ "TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 165 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]

[ "glove", "gensim", "license:pddl", "has_space", "region:us" ]

[]

[]

[ "# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL", "## Example Usage" ]

[ "TAGS\n#glove #gensim #license-pddl #has_space #region-us \n", "# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL", "## Example Usage" ]

[ 20, 41, 4 ]

[ "TAGS\n#glove #gensim #license-pddl #has_space #region-us \n# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL## Example Usage" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "en", "fr", "es", "de", "zh", "ar", "ru", "pt", "it", "ur", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual", "en", "fr", "es", "de", "zh", "ar", "ru", "pt", "it", "ur" ]

[ "# bert-base-10lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nThis model handles the following languages: english, french, spanish, german, chinese, arabic, russian, portuguese, italian, and urdu. It produces the same representations as bert-base-multilingual-cased while being 22.5% smaller in size.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #en #fr #es #de #zh #ar #ru #pt #it #ur #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-10lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nThis model handles the following languages: english, french, spanish, german, chinese, arabic, russian, portuguese, italian, and urdu. It produces the same representations as bert-base-multilingual-cased while being 22.5% smaller in size.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 71, 141, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #en #fr #es #de #zh #ar #ru #pt #it #ur #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-10lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nThis model handles the following languages: english, french, spanish, german, chinese, arabic, russian, portuguese, italian, and urdu. It produces the same representations as bert-base-multilingual-cased while being 22.5% smaller in size.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "en", "fr", "es", "de", "zh", "ar", "ru", "vi", "el", "bg", "th", "tr", "hi", "ur", "sw", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual", "en", "fr", "es", "de", "zh", "ar", "ru", "vi", "el", "bg", "th", "tr", "hi", "ur", "sw" ]

[ "### How to cite\n\n\nContact\n-------\n\n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #en #fr #es #de #zh #ar #ru #vi #el #bg #th #tr #hi #ur #sw #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### How to cite\n\n\nContact\n-------\n\n\nPlease contact amine@URL for any question, feedback or request." ]

[ 86, 29 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #en #fr #es #de #zh #ar #ru #vi #el #bg #th #tr #hi #ur #sw #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to cite\n\n\nContact\n-------\n\n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-25lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nHandled languages: en, fr, es, de, zh, ar, ru, vi, el, bg, th, tr, hi, ur, sw, nl, uk, ro, pt, it, lt, no, pl, da and ja.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-25lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nHandled languages: en, fr, es, de, zh, ar, ru, vi, el, bg, th, tr, hi, ur, sw, nl, uk, ro, pt, it, lt, no, pl, da and ja.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 142, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-25lang-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nHandled languages: en, fr, es, de, zh, ar, ru, vi, el, bg, th, tr, hi, ur, sw, nl, uk, ro, pt, it, lt, no, pl, da and ja.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "ar", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "ar" ]

[ "# bert-base-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 52, 86, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "bg", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "bg" ]

[ "# bert-base-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #bg #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 53, 87, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #bg #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "da", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "da" ]

[ "# bert-base-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #da #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 48, 86, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #da #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "de", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "de" ]

[ "# bert-base-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 52, 86, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "el", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "el" ]

[ "# bert-base-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #el #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 48, 86, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #el #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 89, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-bg-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "en", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# bert-base-en-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #en #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 48, 86, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #en #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-el-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-el-ru-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-el-ru-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-el-ru-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-es-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-es-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-es-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-es-pt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-es-pt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-es-pt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-es-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-es-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 91, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-es-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-da-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-da-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-da-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-de-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-de-no-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-de-no-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-de-no-da-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-es-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-es-de-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-es-de-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 95, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-es-de-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-es-pt-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-es-pt-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-es-pt-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 90, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-lt-no-pl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-lt-no-pl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-lt-no-pl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-nl-ru-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-nl-ru-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-nl-ru-ar-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-uk-el-ro-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-uk-el-ro-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 94, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-uk-el-ro-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 91, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-zh-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-fr-zh-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-fr-zh-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 95, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-fr-zh-ja-vi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-hi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-hi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-hi-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-it-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-ja-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-ja-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-ja-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-lt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-lt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 50, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-lt-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "multilingual" ]

[ "# bert-base-en-nl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-en-nl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.", "## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.", "### How to cite", "## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]

[ 54, 88, 24, 6, 18 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-en-nl-cased\n\nWe are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.\n\nUnlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.\n\n\nFor more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.## How to use\n\n\n\nTo generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact \n\nPlease contact amine@URL for any question, feedback or request." ]