Datasets:

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ftopal

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huggingface-models-processed

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[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]

[ 46, 135, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# test-german-t5-prompted-germanquad\n\neval_loss = 0.5907255411148071 \neval_rouge1 = 62.0922 \neval_rouge2 = 47.2761 \neval_rougeL = 61.7706 \neval_rougeLsum = 61.8036 \neval_runtime = 4501.8065 \neval_samples_per_second = 5.487 \neval_steps_per_second = 2.743", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5.6e-05\n- train_batch_size: 4\n- eval_batch_size: 2\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", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# test-german-t5-prompted-germanquad\n\neval_loss = 0.5907255411148071 \neval_rouge1 = 62.0922 \neval_rouge2 = 47.2761 \neval_rougeL = 61.7706 \neval_rougeLsum = 61.8036 \neval_runtime = 4501.8065 \neval_samples_per_second = 5.487 \neval_steps_per_second = 2.743", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5.6e-05\n- train_batch_size: 4\n- eval_batch_size: 2\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", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]

[ 50, 107, 7, 9, 9, 4, 95, 47 ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# test-german-t5-prompted-germanquad\n\neval_loss = 0.5907255411148071 \neval_rouge1 = 62.0922 \neval_rouge2 = 47.2761 \neval_rougeL = 61.7706 \neval_rougeLsum = 61.8036 \neval_runtime = 4501.8065 \neval_samples_per_second = 5.487 \neval_steps_per_second = 2.743## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5.6e-05\n- train_batch_size: 4\n- eval_batch_size: 2\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### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.9.1+cu102\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "bert", "text-classification", "passive", "active", "nl", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "nl" ]

[ "## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.", "### Lijdende en Bedrijvende vorm classificatie voor zinnen", "#### Examples\nTry the following examples in the Hosted inference API:\n1. Jan werd opgehaald door zijn moeder.\n2. Wie niet weg is, is gezien\n3. Ik ben van plan om morgen te gaan werken\n4. De makelaar heeft het nieuwe huis verkocht aan de bewoners die iets verderop wonen.\n5. De koekjes die mama had gemaakt waren door de jongens allemaal opgegeten.\n\nLABEL_0 = Active / Bedrijvend. LABEL_1 = Passive / Lijdend\n\nAnswers (what they should be): \n1. 1\n2. 1\n3. 0\n4. 0\n5. 1", "#### Basic Information\nThis model is fine-tuned on BERTje for recognizing passive and active voice in Dutch sentences. \n\nContact me at gerwindekruijf@URL for further questions.\n\n\nGerwin" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #passive #active #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.", "### Lijdende en Bedrijvende vorm classificatie voor zinnen", "#### Examples\nTry the following examples in the Hosted inference API:\n1. Jan werd opgehaald door zijn moeder.\n2. Wie niet weg is, is gezien\n3. Ik ben van plan om morgen te gaan werken\n4. De makelaar heeft het nieuwe huis verkocht aan de bewoners die iets verderop wonen.\n5. De koekjes die mama had gemaakt waren door de jongens allemaal opgegeten.\n\nLABEL_0 = Active / Bedrijvend. LABEL_1 = Passive / Lijdend\n\nAnswers (what they should be): \n1. 1\n2. 1\n3. 0\n4. 0\n5. 1", "#### Basic Information\nThis model is fine-tuned on BERTje for recognizing passive and active voice in Dutch sentences. \n\nContact me at gerwindekruijf@URL for further questions.\n\n\nGerwin" ]

[ 42, 14, 24, 164, 43 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #passive #active #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.### Lijdende en Bedrijvende vorm classificatie voor zinnen#### Examples\nTry the following examples in the Hosted inference API:\n1. Jan werd opgehaald door zijn moeder.\n2. Wie niet weg is, is gezien\n3. Ik ben van plan om morgen te gaan werken\n4. De makelaar heeft het nieuwe huis verkocht aan de bewoners die iets verderop wonen.\n5. De koekjes die mama had gemaakt waren door de jongens allemaal opgegeten.\n\nLABEL_0 = Active / Bedrijvend. LABEL_1 = Passive / Lijdend\n\nAnswers (what they should be): \n1. 1\n2. 1\n3. 0\n4. 0\n5. 1#### Basic Information\nThis model is fine-tuned on BERTje for recognizing passive and active voice in Dutch sentences. \n\nContact me at gerwindekruijf@URL for further questions.\n\n\nGerwin" ]

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #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: 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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 53, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #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: 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: 2### Training results### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

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[ "# Wav2Vec2-Large-XLSR-Indonesian\r\n\r\nFine-tuned: facebook/wav2vec2-large-xlsr-53" ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-Indonesian\r\n\r\nFine-tuned: facebook/wav2vec2-large-xlsr-53" ]

[ 32, 33 ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Indonesian\r\n\r\nFine-tuned: facebook/wav2vec2-large-xlsr-53" ]

[ "region:us" ]

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[]

[]

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

[ 5 ]

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

[ "transformers", "pytorch", "roberta", "feature-extraction", "endpoints_compatible", "region:us" ]

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[ "# FongBERT\n\nFongBERT is a BERT model trained on 68.363 sentences in Fon. The data are compiled from JW300 and other additional data I scraped from the JW website.\nIt is the first pretrained model to leverage transfer learning for downtream tasks for Fon.\nBelow are some examples of missing word prediction.\n\n\nfrom transformers import AutoTokenizer, AutoModelForMaskedLM\nfrom transformers import pipeline\n \ntokenizer = AutoTokenizer.from_pretrained(\"Gilles/FongBERT\")\n\nmodel = AutoModelForMaskedLM.from_pretrained(\"Gilles/FongBERT\")\n\n\nfill = pipeline('fill-mask', model=model, tokenizer=tokenizer)", "#### Example 1\n\nSentence 1: un tuùn ɖɔ un jló na wazɔ̌ nú we . Translation: I know I have to work for you.\n\nMasked Sentence: un tuùn ɖɔ un jló na wazɔ̌ <\"mask\"> we . Translation: I know I have to work <\"mask\"> you.\n\nfill(f'un tuùn ɖɔ un jló na wazɔ̌ {fill.tokenizer.mask_token} we')\n\n[{'score': 0.994536280632019,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌ nú we',\n 'token': 312,\n 'token_str': ' nú'},\n {'score': 0.0015309195732697845,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌nu we',\n...........]", "#### Example 2\n\nSentence 2: un yi wan nu we ɖesu . Translation: I love you so much.\n\nMasked Sentence: un yi <\"mask\"> nu we ɖesu . Translation: I <\"mask\"> you so much.\n\n[{'score': 0.31483960151672363,\n 'sequence': 'un yi wan nu we ɖesu',\n 'token': 639,\n 'token_str': ' wan'},\n {'score': 0.20940221846103668,\n 'sequence': 'un yi ba nu we ɖesu',\n ...........]", "#### Example 3\n\nSentence 3: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé . Translation: I went to my boyfriend for a while.\n\nMasked Sentence: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú <\"mask\"> ɖé . Translation: I went to my boyfriend for a <\"mask\">.\n\n [{'score': 0.934298574924469,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé',\n 'token': 1102,\n 'token_str': ' táan'},\n {'score': 0.03750855475664139,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú ganxixo ɖé',\n ...........]" ]

[ "TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n", "# FongBERT\n\nFongBERT is a BERT model trained on 68.363 sentences in Fon. The data are compiled from JW300 and other additional data I scraped from the JW website.\nIt is the first pretrained model to leverage transfer learning for downtream tasks for Fon.\nBelow are some examples of missing word prediction.\n\n\nfrom transformers import AutoTokenizer, AutoModelForMaskedLM\nfrom transformers import pipeline\n \ntokenizer = AutoTokenizer.from_pretrained(\"Gilles/FongBERT\")\n\nmodel = AutoModelForMaskedLM.from_pretrained(\"Gilles/FongBERT\")\n\n\nfill = pipeline('fill-mask', model=model, tokenizer=tokenizer)", "#### Example 1\n\nSentence 1: un tuùn ɖɔ un jló na wazɔ̌ nú we . Translation: I know I have to work for you.\n\nMasked Sentence: un tuùn ɖɔ un jló na wazɔ̌ <\"mask\"> we . Translation: I know I have to work <\"mask\"> you.\n\nfill(f'un tuùn ɖɔ un jló na wazɔ̌ {fill.tokenizer.mask_token} we')\n\n[{'score': 0.994536280632019,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌ nú we',\n 'token': 312,\n 'token_str': ' nú'},\n {'score': 0.0015309195732697845,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌nu we',\n...........]", "#### Example 2\n\nSentence 2: un yi wan nu we ɖesu . Translation: I love you so much.\n\nMasked Sentence: un yi <\"mask\"> nu we ɖesu . Translation: I <\"mask\"> you so much.\n\n[{'score': 0.31483960151672363,\n 'sequence': 'un yi wan nu we ɖesu',\n 'token': 639,\n 'token_str': ' wan'},\n {'score': 0.20940221846103668,\n 'sequence': 'un yi ba nu we ɖesu',\n ...........]", "#### Example 3\n\nSentence 3: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé . Translation: I went to my boyfriend for a while.\n\nMasked Sentence: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú <\"mask\"> ɖé . Translation: I went to my boyfriend for a <\"mask\">.\n\n [{'score': 0.934298574924469,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé',\n 'token': 1102,\n 'token_str': ' táan'},\n {'score': 0.03750855475664139,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú ganxixo ɖé',\n ...........]" ]

[ 23, 157, 206, 143, 202 ]

[ "TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n# FongBERT\n\nFongBERT is a BERT model trained on 68.363 sentences in Fon. The data are compiled from JW300 and other additional data I scraped from the JW website.\nIt is the first pretrained model to leverage transfer learning for downtream tasks for Fon.\nBelow are some examples of missing word prediction.\n\n\nfrom transformers import AutoTokenizer, AutoModelForMaskedLM\nfrom transformers import pipeline\n \ntokenizer = AutoTokenizer.from_pretrained(\"Gilles/FongBERT\")\n\nmodel = AutoModelForMaskedLM.from_pretrained(\"Gilles/FongBERT\")\n\n\nfill = pipeline('fill-mask', model=model, tokenizer=tokenizer)#### Example 1\n\nSentence 1: un tuùn ɖɔ un jló na wazɔ̌ nú we . Translation: I know I have to work for you.\n\nMasked Sentence: un tuùn ɖɔ un jló na wazɔ̌ <\"mask\"> we . Translation: I know I have to work <\"mask\"> you.\n\nfill(f'un tuùn ɖɔ un jló na wazɔ̌ {fill.tokenizer.mask_token} we')\n\n[{'score': 0.994536280632019,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌ nú we',\n 'token': 312,\n 'token_str': ' nú'},\n {'score': 0.0015309195732697845,\n 'sequence': 'un tuùn ɖɔ un jló na wazɔ̌nu we',\n...........]#### Example 2\n\nSentence 2: un yi wan nu we ɖesu . Translation: I love you so much.\n\nMasked Sentence: un yi <\"mask\"> nu we ɖesu . Translation: I <\"mask\"> you so much.\n\n[{'score': 0.31483960151672363,\n 'sequence': 'un yi wan nu we ɖesu',\n 'token': 639,\n 'token_str': ' wan'},\n {'score': 0.20940221846103668,\n 'sequence': 'un yi ba nu we ɖesu',\n ...........]#### Example 3\n\nSentence 3: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé . Translation: I went to my boyfriend for a while.\n\nMasked Sentence: un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú <\"mask\"> ɖé . Translation: I went to my boyfriend for a <\"mask\">.\n\n [{'score': 0.934298574924469,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú táan ɖé',\n 'token': 1102,\n 'token_str': ' táan'},\n {'score': 0.03750855475664139,\n 'sequence': 'un yì cí sunnu xɔ́ntɔn ce Tony gɔ́n nú ganxixo ɖé',\n ...........]" ]

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

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[ "# places\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", "#### Beach\n\n!Beach", "#### City\n\n!City", "#### Forest\n\n!Forest" ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# places\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", "#### Beach\n\n!Beach", "#### City\n\n!City", "#### Forest\n\n!Forest" ]

[ 40, 40, 4, 7, 7, 7 ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# places\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#### Beach\n\n!Beach#### City\n\n!City#### Forest\n\n!Forest" ]

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

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[ "# Mandarin\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\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- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "# Mandarin\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\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- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]

[ 54, 34, 7, 9, 9, 4, 106, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# Mandarin\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\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- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 54, 151, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]

[ 47, 128, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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", "### Training results", "### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.12.0+cu116\n* Datasets 2.3.2\n* Tokenizers 0.12.1" ]

[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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", "### Training results", "### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.12.0+cu116\n* Datasets 2.3.2\n* Tokenizers 0.12.1" ]

[ 55, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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### Training results### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.12.0+cu116\n* Datasets 2.3.2\n* Tokenizers 0.12.1" ]

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

[]

[]

[ "# Jackie DialoGPT Model" ]

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

[ 39, 6 ]

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

[ "optimum_graphcore", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Graphcore/bart-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BART base model (e.g. facebook/bart-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n", "# Graphcore/bart-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BART base model (e.g. facebook/bart-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 19, 197, 129, 57, 3 ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n# Graphcore/bart-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BART base model (e.g. facebook/bart-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "region:us" ]

[]

[]

[ "# Graphcore/bert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT base model (e.g. bert-base-uncased or bert-base-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #region-us \n", "# Graphcore/bert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT base model (e.g. bert-base-uncased or bert-base-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 11, 197, 189, 65, 3 ]

[ "TAGS\n#optimum_graphcore #region-us \n# Graphcore/bert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT base model (e.g. bert-base-uncased or bert-base-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "region:us" ]

[]

[]

[ "# Graphcore/bert-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT large model (e.g. bert-large-uncased or bert-large-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #region-us \n", "# Graphcore/bert-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT large model (e.g. bert-large-uncased or bert-large-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 11, 197, 189, 64, 3 ]

[ "TAGS\n#optimum_graphcore #region-us \n# Graphcore/bert-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the BERT large model (e.g. bert-large-uncased or bert-large-cased) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Graphcore/bert-large-uncased-squad\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model is a fine-tuned version of Graphcore/bert-large-uncased on the SQuAD dataset.", "## Training and evaluation data\nTrained on SQuAD dataset:\n- HuggingFace/squad", "## Training procedure\n\nModel was trained on 16 Graphcore Mk2 IPUs using the optimum-graphcore library." ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n", "# Graphcore/bert-large-uncased-squad\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model is a fine-tuned version of Graphcore/bert-large-uncased on the SQuAD dataset.", "## Training and evaluation data\nTrained on SQuAD dataset:\n- HuggingFace/squad", "## Training procedure\n\nModel was trained on 16 Graphcore Mk2 IPUs using the optimum-graphcore library." ]

[ 46, 199, 189, 30, 17, 24 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n# Graphcore/bert-large-uncased-squad\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.## Intended uses & limitations\n\nThis model is a fine-tuned version of Graphcore/bert-large-uncased on the SQuAD dataset.## Training and evaluation data\nTrained on SQuAD dataset:\n- HuggingFace/squad## Training procedure\n\nModel was trained on 16 Graphcore Mk2 IPUs using the optimum-graphcore library." ]

[ "transformers", "pytorch", "optimum_graphcore", "bert", "generated_from_trainer", "dataset:Graphcore/wikipedia-bert-128", "dataset:Graphcore/wikipedia-bert-512", "arxiv:1904.00962", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[ "1904.00962" ]

[]

[ "# Graphcore/bert-large-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model is a pre-trained BERT-Large trained in two phases on the Graphcore/wikipedia-bert-128 and Graphcore/wikipedia-bert-512 datasets.", "## Training and evaluation data\n\nTrained on wikipedia datasets:\n- Graphcore/wikipedia-bert-128\n- Graphcore/wikipedia-bert-512", "## Training procedure\n\nTrained MLM and NSP pre-training scheme from Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.\nTrained on 64 Graphcore Mk2 IPUs using 'optimum-graphcore'\n\nCommand lines:\n\nPhase 1:\n\n\nPhase 2:", "### Training hyperparameters\n\nThe following hyperparameters were used during phase 1 training:\n- learning_rate: 0.006\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 65536\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.28\n- training_steps: 10550\n- training precision: Mixed Precision\n\nThe following hyperparameters were used during phase 2 training:\n- learning_rate: 0.002828\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 16384\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.128\n- training_steps: 2038\n- training precision: Mixed Precision", "### Training results", "### Framework versions\n\n- Transformers 4.17.0\n- Pytorch 1.10.0+cpu\n- Datasets 2.0.0\n- Tokenizers 0.11.6" ]

[ "TAGS\n#transformers #pytorch #optimum_graphcore #bert #generated_from_trainer #dataset-Graphcore/wikipedia-bert-128 #dataset-Graphcore/wikipedia-bert-512 #arxiv-1904.00962 #license-apache-2.0 #endpoints_compatible #region-us \n", "# Graphcore/bert-large-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.", "## Intended uses & limitations\n\nThis model is a pre-trained BERT-Large trained in two phases on the Graphcore/wikipedia-bert-128 and Graphcore/wikipedia-bert-512 datasets.", "## Training and evaluation data\n\nTrained on wikipedia datasets:\n- Graphcore/wikipedia-bert-128\n- Graphcore/wikipedia-bert-512", "## Training procedure\n\nTrained MLM and NSP pre-training scheme from Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.\nTrained on 64 Graphcore Mk2 IPUs using 'optimum-graphcore'\n\nCommand lines:\n\nPhase 1:\n\n\nPhase 2:", "### Training hyperparameters\n\nThe following hyperparameters were used during phase 1 training:\n- learning_rate: 0.006\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 65536\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.28\n- training_steps: 10550\n- training precision: Mixed Precision\n\nThe following hyperparameters were used during phase 2 training:\n- learning_rate: 0.002828\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 16384\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.128\n- training_steps: 2038\n- training precision: Mixed Precision", "### Training results", "### Framework versions\n\n- Transformers 4.17.0\n- Pytorch 1.10.0+cpu\n- Datasets 2.0.0\n- Tokenizers 0.11.6" ]

[ 74, 197, 189, 43, 31, 54, 258, 5, 42 ]

[ "TAGS\n#transformers #pytorch #optimum_graphcore #bert #generated_from_trainer #dataset-Graphcore/wikipedia-bert-128 #dataset-Graphcore/wikipedia-bert-512 #arxiv-1904.00962 #license-apache-2.0 #endpoints_compatible #region-us \n# Graphcore/bert-large-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. \n\nIt was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.\n\nIt reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.## Intended uses & limitations\n\nThis model is a pre-trained BERT-Large trained in two phases on the Graphcore/wikipedia-bert-128 and Graphcore/wikipedia-bert-512 datasets.## Training and evaluation data\n\nTrained on wikipedia datasets:\n- Graphcore/wikipedia-bert-128\n- Graphcore/wikipedia-bert-512## Training procedure\n\nTrained MLM and NSP pre-training scheme from Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.\nTrained on 64 Graphcore Mk2 IPUs using 'optimum-graphcore'\n\nCommand lines:\n\nPhase 1:\n\n\nPhase 2:### Training hyperparameters\n\nThe following hyperparameters were used during phase 1 training:\n- learning_rate: 0.006\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 65536\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.28\n- training_steps: 10550\n- training precision: Mixed Precision\n\nThe following hyperparameters were used during phase 2 training:\n- learning_rate: 0.002828\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: IPU\n- gradient_accumulation_steps: 512\n- total_train_batch_size: 16384\n- total_eval_batch_size: 512\n- optimizer: LAMB\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.128\n- training_steps: 2038\n- training precision: Mixed Precision### Training results### Framework versions\n\n- Transformers 4.17.0\n- Pytorch 1.10.0+cpu\n- Datasets 2.0.0\n- Tokenizers 0.11.6" ]

[ "optimum_graphcore", "arxiv:2006.03654", "region:us" ]

[ "2006.03654" ]

[]

[ "# Graphcore/deberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nDeBERTa(Decoding-enhanced BERT with Disentangled Attention ) improves the BERT and RoBERTa models using the disentangled attention mechanism and an enhanced mask decoder which is used to replace the output softmax layer to predict the masked tokens for model pretraining. \nThrough two techniques, it could significantly improve the efficiency of model pre-training and performance of downstream tasks.", "# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the DeBERTa-base model (e.g. microsoft/deberta-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #arxiv-2006.03654 #region-us \n", "# Graphcore/deberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nDeBERTa(Decoding-enhanced BERT with Disentangled Attention ) improves the BERT and RoBERTa models using the disentangled attention mechanism and an enhanced mask decoder which is used to replace the output softmax layer to predict the masked tokens for model pretraining. \nThrough two techniques, it could significantly improve the efficiency of model pre-training and performance of downstream tasks.", "# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the DeBERTa-base model (e.g. microsoft/deberta-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 21, 199, 81, 61, 3 ]

[ "TAGS\n#optimum_graphcore #arxiv-2006.03654 #region-us \n# Graphcore/deberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nDeBERTa(Decoding-enhanced BERT with Disentangled Attention ) improves the BERT and RoBERTa models using the disentangled attention mechanism and an enhanced mask decoder which is used to replace the output softmax layer to predict the masked tokens for model pretraining. \nThrough two techniques, it could significantly improve the efficiency of model pre-training and performance of downstream tasks.# Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the DeBERTa-base model (e.g. microsoft/deberta-base) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Graphcore/gpt2-medium-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the HuggingFace/gpt2-medium model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n", "# Graphcore/gpt2-medium-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the HuggingFace/gpt2-medium model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 19, 199, 86, 52, 3 ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n# Graphcore/gpt2-medium-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the HuggingFace/gpt2-medium model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Graphcore/gpt2-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the GPT2 Small model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n", "# Graphcore/gpt2-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the GPT2 Small model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 19, 199, 86, 48, 3 ]

[ "TAGS\n#optimum_graphcore #license-apache-2.0 #region-us \n# Graphcore/gpt2-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\nGPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. \n \nPaper link : Language Models are Unsupervised Multitask Learners## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the GPT2 Small model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "arxiv:1907.11692", "license:apache-2.0", "region:us" ]

[ "1907.11692" ]

[]

[ "# Graphcore/roberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-base model on Graphcore IPUs.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #arxiv-1907.11692 #license-apache-2.0 #region-us \n", "# Graphcore/roberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-base model on Graphcore IPUs.", "## Usage" ]

[ 29, 197, 126, 32, 3 ]

[ "TAGS\n#optimum_graphcore #arxiv-1907.11692 #license-apache-2.0 #region-us \n# Graphcore/roberta-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-base model on Graphcore IPUs.## Usage" ]

[ "optimum_graphcore", "arxiv:1907.11692", "region:us" ]

[ "1907.11692" ]

[]

[ "# Graphcore/roberta-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-large model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #arxiv-1907.11692 #region-us \n", "# Graphcore/roberta-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-large model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 21, 197, 126, 47, 3 ]

[ "TAGS\n#optimum_graphcore #arxiv-1907.11692 #region-us \n# Graphcore/roberta-large-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\nRoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. \n\nIt suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.\n\nAs a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.\n\nPaper link : RoBERTa: A Robustly Optimized BERT Pretraining Approach## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the roberta-large model on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]

[ "1910.10683" ]

[]

[ "# Graphcore/t5-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nText-to-Text Transfer Transformer (T5), is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. \n\nPaper link :Exploring the Limits of Transfer Learning with a Unified\nText-to-Text Transformer", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the T5 Small model (e.g. HuggingFace/t5-small) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #arxiv-1910.10683 #license-apache-2.0 #region-us \n", "# Graphcore/t5-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nText-to-Text Transfer Transformer (T5), is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. \n\nPaper link :Exploring the Limits of Transfer Learning with a Unified\nText-to-Text Transformer", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the T5 Small model (e.g. HuggingFace/t5-small) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 29, 198, 115, 60, 3 ]

[ "TAGS\n#optimum_graphcore #arxiv-1910.10683 #license-apache-2.0 #region-us \n# Graphcore/t5-small-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nText-to-Text Transfer Transformer (T5), is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. \n\nPaper link :Exploring the Limits of Transfer Learning with a Unified\nText-to-Text Transformer## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the T5 Small model (e.g. HuggingFace/t5-small) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "optimum_graphcore", "arxiv:2010.11929", "region:us" ]

[ "2010.11929" ]

[]

[ "# Graphcore/vit-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nThe Vision Transformer (ViT) is a model for image recognition that employs a Transformer-like architecture over patches of the image which was widely used for NLP pretraining. \n\nIt uses a standard Transformer encoder as used in NLP and simple, yet scalable, strategy works surprisingly well when coupled with pre-training on large amounts of dataset and tranferred to multiple size image recognition benchmarks while requiring substantially fewer computational resources to train. \n \nPaper link : AN IMAGE IS WORTH 16X16 WORDS:TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the ViT base model (e.g. vit-base-patch16-224-in21k or deit-base-patch16-384) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ "TAGS\n#optimum_graphcore #arxiv-2010.11929 #region-us \n", "# Graphcore/vit-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.", "## Model description\n\nThe Vision Transformer (ViT) is a model for image recognition that employs a Transformer-like architecture over patches of the image which was widely used for NLP pretraining. \n\nIt uses a standard Transformer encoder as used in NLP and simple, yet scalable, strategy works surprisingly well when coupled with pre-training on large amounts of dataset and tranferred to multiple size image recognition benchmarks while requiring substantially fewer computational resources to train. \n \nPaper link : AN IMAGE IS WORTH 16X16 WORDS:TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE", "## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the ViT base model (e.g. vit-base-patch16-224-in21k or deit-base-patch16-384) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.", "## Usage" ]

[ 20, 198, 117, 77, 3 ]

[ "TAGS\n#optimum_graphcore #arxiv-2010.11929 #region-us \n# Graphcore/vit-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at URL\n\nThrough HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.## Model description\n\nThe Vision Transformer (ViT) is a model for image recognition that employs a Transformer-like architecture over patches of the image which was widely used for NLP pretraining. \n\nIt uses a standard Transformer encoder as used in NLP and simple, yet scalable, strategy works surprisingly well when coupled with pre-training on large amounts of dataset and tranferred to multiple size image recognition benchmarks while requiring substantially fewer computational resources to train. \n \nPaper link : AN IMAGE IS WORTH 16X16 WORDS:TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE## Intended uses & limitations\n\nThis model contains just the 'IPUConfig' files for running the ViT base model (e.g. vit-base-patch16-224-in21k or deit-base-patch16-384) on Graphcore IPUs.\n\nThis model contains no model weights, only an IPUConfig.## Usage" ]

[ "adapter-transformers", "bert", "adapterhub:quality_estimation/wmt21", "region:us" ]

[]

[]

[ "# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased\n\nAn adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ "TAGS\n#adapter-transformers #bert #adapterhub-quality_estimation/wmt21 #region-us \n", "# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased\n\nAn adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ 25, 88, 53, 5, 4 ]

[ "TAGS\n#adapter-transformers #bert #adapterhub-quality_estimation/wmt21 #region-us \n# Adapter 'Gregor/bert-base-multilingual-cased-wmt21-qe' for bert-base-multilingual-cased\n\nAn adapter for the bert-base-multilingual-cased model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training## Evaluation results" ]

[ "adapter-transformers", "xlm-roberta", "adapterhub:quality_estimation/wmt21", "region:us" ]

[]

[]

[ "# Adapter 'Gregor/xlm-roberta-base-wmt21-qe' for xlm-roberta-base\n\nAn adapter for the xlm-roberta-base model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ "TAGS\n#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us \n", "# Adapter 'Gregor/xlm-roberta-base-wmt21-qe' for xlm-roberta-base\n\nAn adapter for the xlm-roberta-base model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ 28, 76, 53, 5, 4 ]

[ "TAGS\n#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us \n# Adapter 'Gregor/xlm-roberta-base-wmt21-qe' for xlm-roberta-base\n\nAn adapter for the xlm-roberta-base model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training## Evaluation results" ]

[ "adapter-transformers", "xlm-roberta", "adapterhub:quality_estimation/wmt21", "region:us" ]

[]

[]

[ "# Adapter 'Gregor/xlm-roberta-large-wmt21-qe' for xlm-roberta-large\n\nAn adapter for the xlm-roberta-large model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ "TAGS\n#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us \n", "# Adapter 'Gregor/xlm-roberta-large-wmt21-qe' for xlm-roberta-large\n\nAn adapter for the xlm-roberta-large model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.", "## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ 28, 76, 53, 5, 4 ]

[ "TAGS\n#adapter-transformers #xlm-roberta #adapterhub-quality_estimation/wmt21 #region-us \n# Adapter 'Gregor/xlm-roberta-large-wmt21-qe' for xlm-roberta-large\n\nAn adapter for the xlm-roberta-large model that was trained on the quality_estimation/wmt21 dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.## Usage\n\nFirst, install 'adapter-transformers':\n\n\n_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More_\n\nNow, the adapter can be loaded and activated like this:## Architecture & Training## Evaluation results" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "PyTorch", "Transformers", "lm-head", "causal-lm", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# rick and morty" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #PyTorch #Transformers #lm-head #causal-lm #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# rick and morty" ]

[ 56, 5 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #PyTorch #Transformers #lm-head #causal-lm #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# rick and morty" ]

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

[]

[]

[ "# The Owl House DialoGPT Model" ]

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

[ 39, 8 ]

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

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "BERTje", "fy", "arxiv:2105.02855", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2105.02855" ]

[ "fy" ]

[ "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n", "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ 50, 30, 27, 108, 124 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "BERTje", "gos", "arxiv:2105.02855", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2105.02855" ]

[ "gos" ]

[ "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n", "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ 50, 30, 27, 108, 124 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #BERTje #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "BERTje", "pos", "fy", "arxiv:2105.02855", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2105.02855" ]

[ "fy" ]

[ "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n", "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ 53, 30, 27, 108, 124 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #fy #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "BERTje", "pos", "gos", "arxiv:2105.02855", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2105.02855" ]

[ "gos" ]

[ "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n", "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ 53, 30, 27, 108, 124 ]

[ "TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #BERTje #pos #gos #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "token-classification", "BERTje", "pos", "nl", "arxiv:2105.02855", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2105.02855" ]

[ "nl" ]

[ "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #BERTje #pos #nl #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n", "# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code", "## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:", "### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)", "### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ 56, 30, 27, 108, 124 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #BERTje #pos #nl #arxiv-2105.02855 #autotrain_compatible #endpoints_compatible #region-us \n# Adapting Monolingual Models: Data can be Scarce when Language Similarity is High\n\nThis model is part of this paper + code:\n\n- Paper\n- Code## Models\n\nThe best fine-tuned models for Gronings and West Frisian are available on the HuggingFace model hub:### Lexical layers\nThese models are identical to BERTje, but with different lexical layers ('bert.embeddings.word_embeddings').\n\n - 'GroNLP/bert-base-dutch-cased' (Dutch; source language)\n - 'GroNLP/bert-base-dutch-cased-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-frisian' (West Frisian)### POS tagging\nThese models share the same fine-tuned Transformer layers + classification head, but with the retrained lexical layers from the models above.\n\n - 'GroNLP/bert-base-dutch-cased-upos-alpino' (Dutch)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-gronings' (Gronings)\n - 'GroNLP/bert-base-dutch-cased-upos-alpino-frisian' (West Frisian)" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "BERTje", "nl", "arxiv:1912.09582", "doi:10.57967/hf/0149", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "1912.09582" ]

[ "nl" ]

[ "### Named Entity Recognition", "### Part-of-speech tagging", "### BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #BERTje #nl #arxiv-1912.09582 #doi-10.57967/hf/0149 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Named Entity Recognition", "### Part-of-speech tagging", "### BibTeX entry and citation info" ]

[ 71, 6, 10, 10 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #BERTje #nl #arxiv-1912.09582 #doi-10.57967/hf/0149 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Named Entity Recognition### Part-of-speech tagging### BibTeX entry and citation info" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-medium", "nl", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "nl" ]

[ "# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-medium #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 68, 31, 78, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-medium #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-medium", "it", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "it" ]

[ "# GPT-2 recycled for Italian (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-medium #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GPT-2 recycled for Italian (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 68, 31, 78, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-medium #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT-2 recycled for Italian (medium, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the medium OpenAI GPT-2 ('gpt2-medium') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-small", "nl", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "nl" ]

[ "# GPT-2 recycled for Dutch (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GPT-2 recycled for Dutch (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 68, 31, 76, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT-2 recycled for Dutch (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-small", "nl", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "nl" ]

[ "# GPT-2 recycled for Dutch (small)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# GPT-2 recycled for Dutch (small)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 72, 23, 45, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #nl #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# GPT-2 recycled for Dutch (small)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-small", "it", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "it" ]

[ "# GPT-2 recycled for Italian (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# GPT-2 recycled for Italian (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 72, 31, 76, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# GPT-2 recycled for Italian (small, adapted lexical embeddings)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nThe Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "adaption", "recycled", "gpt2-small", "it", "arxiv:2012.05628", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2012.05628" ]

[ "it" ]

[ "# GPT-2 recycled for Italian (small)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GPT-2 recycled for Italian (small)\nWietse de Vries •\nMalvina Nissim", "## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.", "## Related models", "### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.", "### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.", "## How to use", "## BibTeX entry" ]

[ 68, 23, 45, 4, 103, 103, 5, 6 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-small #it #arxiv-2012.05628 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT-2 recycled for Italian (small)\nWietse de Vries •\nMalvina Nissim## Model description\n\nThis model is based on the small OpenAI GPT-2 ('gpt2') model.\n\nFor details, check out our paper on arXiv and the code on Github.## Related models### Dutch\n - 'gpt2-small-dutch-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-dutch': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-dutch-embeddings': Medium model size with only retrained lexical embeddings.### Italian\n - 'gpt2-small-italian-embeddings': Small model size with only retrained lexical embeddings.\n - 'gpt2-small-italian': Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)\n - 'gpt2-medium-italian-embeddings': Medium model size with only retrained lexical embeddings.## How to use## BibTeX entry" ]

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "HateBERT", "text classification", "abusive language", "hate speech", "offensive language", "en", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en" ]

[ "# \nTommaso Caselli •\nValerio Basile •\nJelena Mitrovic •\nMichael Granizter", "## Model description\n\nHateBERT is an English pre-trained BERT model obtained by further training the English BERT base uncased model with more than 1 million posts from banned communites from Reddit. The model has been developed as a collaboration between the University of Groningen, the university of Turin, and the University of Passau.\n\nFor details, check out the paper presented at WOAH 2021. The code and the fine-tuned models are available on OSF.", "### BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #HateBERT #text classification #abusive language #hate speech #offensive language #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# \nTommaso Caselli •\nValerio Basile •\nJelena Mitrovic •\nMichael Granizter", "## Model description\n\nHateBERT is an English pre-trained BERT model obtained by further training the English BERT base uncased model with more than 1 million posts from banned communites from Reddit. The model has been developed as a collaboration between the University of Groningen, the university of Turin, and the University of Passau.\n\nFor details, check out the paper presented at WOAH 2021. The code and the fine-tuned models are available on OSF.", "### BibTeX entry and citation info" ]

[ 53, 22, 93, 10 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #HateBERT #text classification #abusive language #hate speech #offensive language #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n# \nTommaso Caselli •\nValerio Basile •\nJelena Mitrovic •\nMichael Granizter## Model description\n\nHateBERT is an English pre-trained BERT model obtained by further training the English BERT base uncased model with more than 1 million posts from banned communites from Reddit. The model has been developed as a collaboration between the University of Groningen, the university of Turin, and the University of Passau.\n\nFor details, check out the paper presented at WOAH 2021. The code and the fine-tuned models are available on OSF.### BibTeX entry and citation info" ]

[ "region:us" ]

[]

[]

[ "### The MelGAN vocoder for StyleSpeech", "#### About StyleSpeech\n* StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation\n* The StyleSpeech model can be trained by official implementation (URL", "#### About MelGAN vocoder\n* This MelGAN vocoder is used to transform the mel-spectrogram back to the waveform. \n* StyleSpeech is based on 16k Hz sampling rate, and there is no available 16k Hz multi-speaker vocoder.\n* Thus I train this vocoder from scratch using Libri-TTS train-100 hour dataset. The training pipeline is the same as the official MelGAN (URL\n* The synthesized sounds are close to the official demo with good quality.", "#### Usage\n* Please follow the official MelGAN (URL to load pre-trained checkpoint and convert your mel-spectrogram back to the waveform.", "#### Training Details \n* GPU: RTX 2080Ti\n* Training epoch: 3000" ]

[ "TAGS\n#region-us \n", "### The MelGAN vocoder for StyleSpeech", "#### About StyleSpeech\n* StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation\n* The StyleSpeech model can be trained by official implementation (URL", "#### About MelGAN vocoder\n* This MelGAN vocoder is used to transform the mel-spectrogram back to the waveform. \n* StyleSpeech is based on 16k Hz sampling rate, and there is no available 16k Hz multi-speaker vocoder.\n* Thus I train this vocoder from scratch using Libri-TTS train-100 hour dataset. The training pipeline is the same as the official MelGAN (URL\n* The synthesized sounds are close to the official demo with good quality.", "#### Usage\n* Please follow the official MelGAN (URL to load pre-trained checkpoint and convert your mel-spectrogram back to the waveform.", "#### Training Details \n* GPU: RTX 2080Ti\n* Training epoch: 3000" ]

[ 5, 13, 47, 112, 35, 20 ]

[ "TAGS\n#region-us \n### The MelGAN vocoder for StyleSpeech#### About StyleSpeech\n* StyleSpeech or Meta-StyleSpeech is a model for Multi-Speaker Adaptive Text-to-Speech Generation\n* The StyleSpeech model can be trained by official implementation (URL#### About MelGAN vocoder\n* This MelGAN vocoder is used to transform the mel-spectrogram back to the waveform. \n* StyleSpeech is based on 16k Hz sampling rate, and there is no available 16k Hz multi-speaker vocoder.\n* Thus I train this vocoder from scratch using Libri-TTS train-100 hour dataset. The training pipeline is the same as the official MelGAN (URL\n* The synthesized sounds are close to the official demo with good quality.#### Usage\n* Please follow the official MelGAN (URL to load pre-trained checkpoint and convert your mel-spectrogram back to the waveform.#### Training Details \n* GPU: RTX 2080Ti\n* Training epoch: 3000" ]

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

[]

[]

[ "# Rick Sanchez DialoGPT Model" ]

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

[ 39, 7 ]

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

[]

[]

[ "# Game of Thrones DialoGPT Model" ]

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

[ 39, 9 ]

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

[ "espnet", "audio", "text-to-speech", "en", "dataset:talromur", "arxiv:1804.00015", "license:cc-by-4.0", "has_space", "region:us" ]

[ "1804.00015" ]

[ "en" ]

[ "## ESPnet2 TTS model", "### 'GunnarThor/talromur_f_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.", "### Demo: How to use in ESPnet2", "## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>", "### Citing ESPnet\n\n\n\nor arXiv:" ]

[ "TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n", "## ESPnet2 TTS model", "### 'GunnarThor/talromur_f_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.", "### Demo: How to use in ESPnet2", "## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>", "### Citing ESPnet\n\n\n\nor arXiv:" ]

[ 48, 8, 34, 12, 22, 11 ]

[ "TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n## ESPnet2 TTS model### 'GunnarThor/talromur_f_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.### Demo: How to use in ESPnet2## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>### Citing ESPnet\n\n\n\nor arXiv:" ]

[ "region:us" ]

[]

[]

[]

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

[ 5 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 5 ]

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

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #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: 64\n* eval\\_batch\\_size: 64\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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 47, 112, 5, 40 ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #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: 64\n* eval\\_batch\\_size: 64\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\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# roberta-base-finetuned\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.4057\n- eval_runtime: 3.7087\n- eval_samples_per_second: 167.712\n- eval_steps_per_second: 2.696\n- epoch: 2.11\n- step: 2053", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\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\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.1\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# roberta-base-finetuned\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.4057\n- eval_runtime: 3.7087\n- eval_samples_per_second: 167.712\n- eval_steps_per_second: 2.696\n- epoch: 2.11\n- step: 2053", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\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\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.1\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ 41, 98, 7, 9, 9, 4, 102, 40 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-finetuned\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.4057\n- eval_runtime: 3.7087\n- eval_samples_per_second: 167.712\n- eval_steps_per_second: 2.696\n- epoch: 2.11\n- step: 2053## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 64\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\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.1\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tf", "bert", "text-classification", "Hatespeech", "Danish", "BERT", "da", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "da" ]

[ "# DKbert-hatespeech-classification\n\nUse this model to detect hatespeech in Danish. For details, guide and command line tool see DK hate github", "## Training data\n\nTraining data is from OffensEval2020 which can be found here", "## Performance\n\nThe model achieves a macro F1-score of 0.78 \n\nPrecision hateful: 0.77\n\nRecall hateful: 0.49\n\nSee more on DK hate github", "## Training procedure\n\n- BOTXO Nordic Bert\n- Learning rate: 1e-5,\n- Batch size: 16\n- Max sequence length: 128", "## Project information\n\nThis model was made in collaboration between Johan Horsmans and Gustav Aarup Lauridsen for their Cultural Data Science Exam." ]

[ "TAGS\n#transformers #pytorch #tf #bert #text-classification #Hatespeech #Danish #BERT #da #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# DKbert-hatespeech-classification\n\nUse this model to detect hatespeech in Danish. For details, guide and command line tool see DK hate github", "## Training data\n\nTraining data is from OffensEval2020 which can be found here", "## Performance\n\nThe model achieves a macro F1-score of 0.78 \n\nPrecision hateful: 0.77\n\nRecall hateful: 0.49\n\nSee more on DK hate github", "## Training procedure\n\n- BOTXO Nordic Bert\n- Learning rate: 1e-5,\n- Batch size: 16\n- Max sequence length: 128", "## Project information\n\nThis model was made in collaboration between Johan Horsmans and Gustav Aarup Lauridsen for their Cultural Data Science Exam." ]

[ 45, 36, 17, 39, 30, 29 ]

[ "TAGS\n#transformers #pytorch #tf #bert #text-classification #Hatespeech #Danish #BERT #da #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# DKbert-hatespeech-classification\n\nUse this model to detect hatespeech in Danish. For details, guide and command line tool see DK hate github## Training data\n\nTraining data is from OffensEval2020 which can be found here## Performance\n\nThe model achieves a macro F1-score of 0.78 \n\nPrecision hateful: 0.77\n\nRecall hateful: 0.49\n\nSee more on DK hate github## Training procedure\n\n- BOTXO Nordic Bert\n- Learning rate: 1e-5,\n- Batch size: 16\n- Max sequence length: 128## Project information\n\nThis model was made in collaboration between Johan Horsmans and Gustav Aarup Lauridsen for their Cultural Data Science Exam." ]

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

[]

[]

[ "# Zero Two DialoGPT Model" ]

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

[ 39, 7 ]

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

[ "transformers", "pytorch", "led", "text2text-generation", "en", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en" ]

[ "## DistilLED Large CNN 16384\n\n*distil-led-large-cnn-16384* was initialized from sshleifer/distilbart-cnn-12-6, in a fashion similar to allenai/led-large-16384.\n\nTo be able to process 16K tokens, *sshleifer/distilbart-cnn-12-6*'s position embedding matrix was simply copied 16 times.\n\nThis checkpoint should be loaded into 'LEDForConditionalGeneration.from_pretrained'. See the LED documentation for more information." ]

[ "TAGS\n#transformers #pytorch #led #text2text-generation #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "## DistilLED Large CNN 16384\n\n*distil-led-large-cnn-16384* was initialized from sshleifer/distilbart-cnn-12-6, in a fashion similar to allenai/led-large-16384.\n\nTo be able to process 16K tokens, *sshleifer/distilbart-cnn-12-6*'s position embedding matrix was simply copied 16 times.\n\nThis checkpoint should be loaded into 'LEDForConditionalGeneration.from_pretrained'. See the LED documentation for more information." ]

[ 52, 127 ]

[ "TAGS\n#transformers #pytorch #led #text2text-generation #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## DistilLED Large CNN 16384\n\n*distil-led-large-cnn-16384* was initialized from sshleifer/distilbart-cnn-12-6, in a fashion similar to allenai/led-large-16384.\n\nTo be able to process 16K tokens, *sshleifer/distilbart-cnn-12-6*'s position embedding matrix was simply copied 16 times.\n\nThis checkpoint should be loaded into 'LEDForConditionalGeneration.from_pretrained'. See the LED documentation for more information." ]

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

[]

[]

[ "# household-rooms\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", "#### bathroom\n\n!bathroom", "#### bedroom\n\n!bedroom", "#### dining room\n\n!dining room", "#### kitchen\n\n!kitchen", "#### living room\n\n!living room" ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# household-rooms\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", "#### bathroom\n\n!bathroom", "#### bedroom\n\n!bedroom", "#### dining room\n\n!dining room", "#### kitchen\n\n!kitchen", "#### living room\n\n!living room" ]

[ 40, 42, 4, 7, 7, 9, 7, 9 ]

[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# household-rooms\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#### bathroom\n\n!bathroom#### bedroom\n\n!bedroom#### dining room\n\n!dining room#### kitchen\n\n!kitchen#### living room\n\n!living room" ]

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

[]

[]

[]

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

[ 38 ]

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

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

[]

[]

[ "### Model information\n\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 4943.9641 secs\n Loss: 0.0291" ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Model information\n\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 4943.9641 secs\n Loss: 0.0291" ]

[ 38, 34 ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Model information\n\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 4943.9641 secs\n Loss: 0.0291" ]

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

[]

[]

[ "### Model information\n \n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large \n Epoch: 30\n Train runtime: 17861.6048 secs\n Loss: 0.0412\n\n\nAPI page: Ainize\n\nDemo page: End-point", "### ===Teachable NLP=== ###\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other fairytale model: showcase" ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Model information\n \n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large \n Epoch: 30\n Train runtime: 17861.6048 secs\n Loss: 0.0412\n\n\nAPI page: Ainize\n\nDemo page: End-point", "### ===Teachable NLP=== ###\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other fairytale model: showcase" ]

[ 38, 57, 73 ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Model information\n \n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large \n Epoch: 30\n Train runtime: 17861.6048 secs\n Loss: 0.0412\n\n\nAPI page: Ainize\n\nDemo page: End-point### ===Teachable NLP=== ###\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other fairytale model: showcase" ]

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

[]

[]

[ "### Model information\n\n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 10307.3488 secs\n Loss: 0.0292\n\n\nAPI page: Ainize\n\nDemo page: End-point", "### ===Teachable NLP===\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other lovecraft model: showcase" ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Model information\n\n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 10307.3488 secs\n Loss: 0.0292\n\n\nAPI page: Ainize\n\nDemo page: End-point", "### ===Teachable NLP===\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other lovecraft model: showcase" ]

[ 38, 60, 70 ]

[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Model information\n\n Fine tuning data: URL\n License: CC0: Public Domain\n Base model: gpt-2 large\n Epoch: 30\n Train runtime: 10307.3488 secs\n Loss: 0.0292\n\n\nAPI page: Ainize\n\nDemo page: End-point### ===Teachable NLP===\n\nTo train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.\n\nTeachable NLP: Teachable NLP\n\nTutorial: Tutorial\n\nAnd my other lovecraft model: showcase" ]

[ "region:us" ]

[]

[]

[]

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

[ 5 ]

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

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

[]

[]

[ "# My Awesome Model" ]

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

[ 39, 4 ]

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

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

[]

[]

[ "# Peter from Your Boyfriend Game." ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Peter from Your Boyfriend Game." ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Peter from Your Boyfriend Game." ]

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

[]

[]

[ "# Jake DialoGPT-large-jake" ]

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

[ 39, 9 ]

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

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

[]

[]

[ "# Jake DialoGPT-large-jake2" ]

[ "TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Jake DialoGPT-large-jake2" ]

[ 43, 10 ]

[ "TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jake DialoGPT-large-jake2" ]

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

[]

[]

[ "# Jake DialoGPT-large-jake" ]

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

[ 39, 9 ]

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

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

[]

[]

[ "# Jake DialoGPT-large-jake" ]

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

[ 39, 9 ]

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

[ "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", "gpt2", "text-generation", "en", "arxiv:1706.03762", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[ "1706.03762" ]

[ "en" ]

[ "## GPT2-Home\n\nThis model is fine-tuned using GPT-2 on amazon home products metadata. \nIt can generate descriptions for your home products by getting a text prompt.", "### Model description\n\n\nGPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.", "### Live Demo\nFor testing model with special configuration, please visit Demo", "### Blog Post\nFor more detailed information about project development please refer to my blog post.", "### How to use\nFor best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my GitHub\n\nYou can use this model directly with a pipeline for text generation.\n\n\ninfo" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #en #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "## GPT2-Home\n\nThis model is fine-tuned using GPT-2 on amazon home products metadata. \nIt can generate descriptions for your home products by getting a text prompt.", "### Model description\n\n\nGPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.", "### Live Demo\nFor testing model with special configuration, please visit Demo", "### Blog Post\nFor more detailed information about project development please refer to my blog post.", "### How to use\nFor best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my GitHub\n\nYou can use this model directly with a pipeline for text generation.\n\n\ninfo" ]

[ 62, 38, 117, 15, 19, 47 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #en #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## GPT2-Home\n\nThis model is fine-tuned using GPT-2 on amazon home products metadata. \nIt can generate descriptions for your home products by getting a text prompt.### Model description\n\n\nGPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.### Live Demo\nFor testing model with special configuration, please visit Demo### Blog Post\nFor more detailed information about project development please refer to my blog post.### How to use\nFor best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my GitHub\n\nYou can use this model directly with a pipeline for text generation.\n\n\ninfo" ]

[ "region:us" ]

[]

[]

[]

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

[ 5 ]

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

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ 55, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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: 3### Training results### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

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

[]

[]

[ "# Rick from Rick & Morty DialoGPT Model" ]

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

[ 39, 11 ]

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

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

[]

[]

[ "# Shrek from Shrek DialoGPT Model" ]

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

[ 39, 10 ]

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

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]

[ 53, 103, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ur" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 92, 155, 5, 47 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ur", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ur" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 200.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ur #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 200.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 67, 155, 5, 47 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ur #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 200.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ur" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000388\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 750\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000388\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 750\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 67, 154, 5, 47 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000388\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 750\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ur" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 92, 155, 5, 47 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ur", "robust-speech-event", "hf-asr-leaderboard", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ur" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 92, 155, 5, 47 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ur #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

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

[]

[]

[ "# Harry Potter DailogGPT Model" ]

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

[ 39, 8 ]

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

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

[]

[]

[ "# Jack Sparrow GPT" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# Jack Sparrow GPT" ]

[ 43, 5 ]

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

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

[]

[]

[ "# Tony Stark GPT\n\nMy first AI model still learning, used small dataset so don't expect much" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Tony Stark GPT\n\nMy first AI model still learning, used small dataset so don't expect much" ]

[ 39, 22 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Tony Stark GPT\n\nMy first AI model still learning, used small dataset so don't expect much" ]

[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "en", "dataset:Harshveer/autonlp-data-formality_scoring_2", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Model Trained Using AutoNLP\n\n- Problem type: Single Column Regression\n- Model ID: 32597818\n- CO2 Emissions (in grams): 8.655894631203154", "## Validation Metrics\n\n- Loss: 0.5410276651382446\n- MSE: 0.5410276651382446\n- MAE: 0.5694561004638672\n- R2: 0.6830431129198475\n- RMSE: 0.735545814037323\n- Explained Variance: 0.6834385395050049", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-Harshveer/autonlp-data-formality_scoring_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Single Column Regression\n- Model ID: 32597818\n- CO2 Emissions (in grams): 8.655894631203154", "## Validation Metrics\n\n- Loss: 0.5410276651382446\n- MSE: 0.5410276651382446\n- MAE: 0.5694561004638672\n- R2: 0.6830431129198475\n- RMSE: 0.735545814037323\n- Explained Variance: 0.6834385395050049", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ 62, 42, 91, 16 ]

[ "TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-Harshveer/autonlp-data-formality_scoring_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Single Column Regression\n- Model ID: 32597818\n- CO2 Emissions (in grams): 8.655894631203154## Validation Metrics\n\n- Loss: 0.5410276651382446\n- MSE: 0.5410276651382446\n- MAE: 0.5694561004638672\n- R2: 0.6830431129198475\n- RMSE: 0.735545814037323\n- Explained Variance: 0.6834385395050049## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "hi", "model_for_talk", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "dataset:Harveenchadha/indic-voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "hi" ]

[ "# hindi_base_wav2vec2" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #hi #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-Harveenchadha/indic-voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# hindi_base_wav2vec2" ]

[ 94, 11 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #hi #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-Harveenchadha/indic-voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# hindi_base_wav2vec2" ]

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 33 ]

[ "TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "keras", "nlp", "region:us" ]

[]

[]

[ "## Multimodal entailment\nAuthor: Sayak Paul\nDate created: 2021/08/08\nLast modified: 2021/08/15\nDescription: Training a multimodal model for predicting entailment.", "### What is multimodal entailment?\nOn social media platforms, to audit and moderate content we may want to find answers to the following questions in near real-time:\n\nDoes a given piece of information contradict the other?\nDoes a given piece of information imply the other?\nIn NLP, this task is called analyzing textual entailment. However, that's only when the information comes from text content. In practice, it's often the case the information available comes not just from text content, but from a multimodal combination of text, images, audio, video, etc. Multimodal entailment is simply the extension of textual entailment to a variety of new input modalities." ]

[ "TAGS\n#keras #nlp #region-us \n", "## Multimodal entailment\nAuthor: Sayak Paul\nDate created: 2021/08/08\nLast modified: 2021/08/15\nDescription: Training a multimodal model for predicting entailment.", "### What is multimodal entailment?\nOn social media platforms, to audit and moderate content we may want to find answers to the following questions in near real-time:\n\nDoes a given piece of information contradict the other?\nDoes a given piece of information imply the other?\nIn NLP, this task is called analyzing textual entailment. However, that's only when the information comes from text content. In practice, it's often the case the information available comes not just from text content, but from a multimodal combination of text, images, audio, video, etc. Multimodal entailment is simply the extension of textual entailment to a variety of new input modalities." ]

[ 11, 43, 149 ]

[ "TAGS\n#keras #nlp #region-us \n## Multimodal entailment\nAuthor: Sayak Paul\nDate created: 2021/08/08\nLast modified: 2021/08/15\nDescription: Training a multimodal model for predicting entailment.### What is multimodal entailment?\nOn social media platforms, to audit and moderate content we may want to find answers to the following questions in near real-time:\n\nDoes a given piece of information contradict the other?\nDoes a given piece of information imply the other?\nIn NLP, this task is called analyzing textual entailment. However, that's only when the information comes from text content. In practice, it's often the case the information available comes not just from text content, but from a multimodal combination of text, images, audio, video, etc. Multimodal entailment is simply the extension of textual entailment to a variety of new input modalities." ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "hi", "arxiv:2107.07402", "license:mit", "model-index", "endpoints_compatible", "has_space", "region:us" ]

[ "2107.07402" ]

[ "hi" ]

[ "## Spaces Demo\nCheck the spaces demo here", "## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.", "## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.", "## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.", "## Colab Demo", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 33.17 %\n\nColab Evaluation", "## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hi #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n", "## Spaces Demo\nCheck the spaces demo here", "## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.", "## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.", "## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.", "## Colab Demo", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 33.17 %\n\nColab Evaluation", "## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ 59, 9, 75, 30, 43, 5, 18, 29, 34 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hi #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n## Spaces Demo\nCheck the spaces demo here## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.## Colab Demo## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 33.17 %\n\nColab Evaluation## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pa", "arxiv:2107.07402", "license:mit", "model-index", "endpoints_compatible", "has_space", "region:us" ]

[ "2107.07402" ]

[ "pa" ]

[]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pa #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n" ]

[ 59 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pa #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n" ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "ta", "arxiv:2107.07402", "license:mit", "model-index", "endpoints_compatible", "has_space", "region:us" ]

[ "2107.07402" ]

[ "ta" ]

[ "## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.", "## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.", "## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.", "## Colab Demo", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 53.64 %\n\nColab Evaluation", "## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #ta #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n", "## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.", "## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.", "## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.", "## Colab Demo", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 53.64 %\n\nColab Evaluation", "## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ 59, 75, 30, 43, 5, 18, 29, 34 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #ta #arxiv-2107.07402 #license-mit #model-index #endpoints_compatible #has_space #region-us \n## Pretrained Model\n\nFine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: The result from this model is without a language model so you may witness a higher WER in some cases.## Dataset\n\nThis model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.## Training Script\n\nModels were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository.\n\nIn case you want to explore training logs on wandb they are here.## Colab Demo## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\nTest Result: 53.64 %\n\nColab Evaluation## Credits\nThanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages." ]

[ "transformers", "pytorch", "wav2vec2", "pretraining", "hf-asr-leaderboard", "hi", "model_for_talk", "pretrained", "robust-speech-event", "speech", "arxiv:2107.07402", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[ "2107.07402" ]

[ "hi" ]

[]

[ "TAGS\n#transformers #pytorch #wav2vec2 #pretraining #hf-asr-leaderboard #hi #model_for_talk #pretrained #robust-speech-event #speech #arxiv-2107.07402 #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ 76 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #pretraining #hf-asr-leaderboard #hi #model_for_talk #pretrained #robust-speech-event #speech #arxiv-2107.07402 #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "wav2vec2", "feature-extraction", "arxiv:2107.07402", "endpoints_compatible", "region:us" ]

[ "2107.07402" ]

[]

[]

[ "TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "bert", "text-classification", "en", "dataset:hatexplain", "arxiv:2012.10289", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2012.10289" ]

[ "en" ]

[ "## Table of Contents\n- Model Details\n- How to Get Started With the Model\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- Technical Specifications\n- Citation Information", "## Model Details\nModel Description: \nThe model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence\n\n\n- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee \n- Model Type: Text Classification\n- Language(s): English\n- License: Apache-2.0\n- Parent Model: See the BERT base uncased model for more information about the BERT base model.\n- Resources for more information:\n - Research Paper Accepted at AAAI 2021.\n - GitHub Repo with datatsets and models", "## How to Get Started with the Model\n\nDetails of usage\n\nPlease use the Model_Rational_Label class inside URL to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.", "## Uses", "#### Direct Use\n\nThis model can be used for Text Classification", "#### Downstream Use\n\n[More information needed]", "#### Misuse and Out-of-scope Use\n\nThe model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.", "## Risks, Limitations and Biases\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n(and if you can generate an example of a biased prediction, also something like this): \n\nPredictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example: \n\nThe model author's also note in their HateXplain paper that they \n> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*", "#### Training Procedure", "##### Preprocessing\n\nThe authors detail their preprocessing procedure in the Github repository", "## Evaluation\nThe mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the associated paper", "#### Results \n\nThe model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned !models" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #en #dataset-hatexplain #arxiv-2012.10289 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "## Table of Contents\n- Model Details\n- How to Get Started With the Model\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- Technical Specifications\n- Citation Information", "## Model Details\nModel Description: \nThe model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence\n\n\n- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee \n- Model Type: Text Classification\n- Language(s): English\n- License: Apache-2.0\n- Parent Model: See the BERT base uncased model for more information about the BERT base model.\n- Resources for more information:\n - Research Paper Accepted at AAAI 2021.\n - GitHub Repo with datatsets and models", "## How to Get Started with the Model\n\nDetails of usage\n\nPlease use the Model_Rational_Label class inside URL to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.", "## Uses", "#### Direct Use\n\nThis model can be used for Text Classification", "#### Downstream Use\n\n[More information needed]", "#### Misuse and Out-of-scope Use\n\nThe model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.", "## Risks, Limitations and Biases\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n(and if you can generate an example of a biased prediction, also something like this): \n\nPredictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example: \n\nThe model author's also note in their HateXplain paper that they \n> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*", "#### Training Procedure", "##### Preprocessing\n\nThe authors detail their preprocessing procedure in the Github repository", "## Evaluation\nThe mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the associated paper", "#### Results \n\nThe model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned !models" ]

[ 60, 35, 180, 51, 3, 14, 11, 71, 194, 6, 24, 27, 47 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #en #dataset-hatexplain #arxiv-2012.10289 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Table of Contents\n- Model Details\n- How to Get Started With the Model\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- Technical Specifications\n- Citation Information## Model Details\nModel Description: \nThe model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence\n\n\n- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee \n- Model Type: Text Classification\n- Language(s): English\n- License: Apache-2.0\n- Parent Model: See the BERT base uncased model for more information about the BERT base model.\n- Resources for more information:\n - Research Paper Accepted at AAAI 2021.\n - GitHub Repo with datatsets and models## How to Get Started with the Model\n\nDetails of usage\n\nPlease use the Model_Rational_Label class inside URL to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.## Uses#### Direct Use\n\nThis model can be used for Text Classification#### Downstream Use\n\n[More information needed]#### Misuse and Out-of-scope Use\n\nThe model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.## Risks, Limitations and Biases\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n(and if you can generate an example of a biased prediction, also something like this): \n\nPredictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example: \n\nThe model author's also note in their HateXplain paper that they \n> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*#### Training Procedure##### Preprocessing\n\nThe authors detail their preprocessing procedure in the Github repository## Evaluation\nThe mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the associated paper#### Results \n\nThe model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned !models" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "en", "dataset:hatexplain", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-hatexplain #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]

[ 52 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-hatexplain #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "ar", "arxiv:2004.06465", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2004.06465" ]

[ "ar" ]

[ "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #ar #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ 50, 154 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #ar #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "en", "arxiv:2004.06465", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "2004.06465" ]

[ "en" ]

[ "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~" ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~" ]

[ 54, 154 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "fr", "arxiv:2004.06465", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2004.06465" ]

[ "fr" ]

[ "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #fr #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ 50, 154 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #fr #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "de", "arxiv:2004.06465", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2004.06465" ]

[ "de" ]

[ "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #de #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ 50, 154 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #de #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "transformers", "pytorch", "jax", "bert", "text-classification", "arxiv:2004.06465", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2004.06465" ]

[]

[ "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us \n", "### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]

[ 40, 154 ]

[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~" ]