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## Overview **Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 4x V100 GPU **Published**: Dec 8th, 2021 ## Details - haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model. ## Hyperparameters ``` batch_size = 80 n_epochs = 4 max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 temperature = 1.5 distillation_loss_weight = 0.75 ``` ## Performance ``` "exact": 79.8366040596311 "f1": 83.916407079888 ``` ## Authors **Timo Möller:** [email protected] **Julian Risch:** [email protected] **Malte Pietsch:** [email protected] **Michel Bartels:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/roberta-base-squad2-distilled", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 80.8593, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVjNzkxNmNiNDkzNzdiYjJjZGM3ZTViMGJhOGM2ZjFmYjg1MjYxMDM2YzM5NWMwNDIyYzNlN2QwNGYyNDMzZSIsInZlcnNpb24iOjF9.Rgww8tf8D7nF2dh2U_DMrFzmp87k8s7RFibrDXSvQyA66PGWXwjlsd1552lzjHnNV5hvHUM1-h3PTuY_5p64BA"}, {"type": "f1", "value": 84.0104, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTAyZDViNWYzNjA4OWQ5MzgyYmQ2ZDlhNWRhMTIzYTYxYzViMmI4NWE4ZGU5MzVhZTAwNTRlZmRlNWUwMjI0ZSIsInZlcnNpb24iOjF9.Er21BNgJ3jJXLuZtpubTYq9wCwO1i_VLQFwS5ET0e4eAYVVj0aOA40I5FvP5pZac3LjkCnVacxzsFWGCYVmnDA"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 86.225, "name": "Exact Match"}, {"type": "f1", "value": 92.483, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 29.9, "name": "Exact Match"}, {"type": "f1", "value": 41.183, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_adversarial", "type": "squad_adversarial", "config": "AddOneSent", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 79.071, "name": "Exact Match"}, {"type": "f1", "value": 84.472, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 70.733, "name": "Exact Match"}, {"type": "f1", "value": 83.958, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts new_wiki", "type": "squadshifts", "config": "new_wiki", "split": "test"}, "metrics": [{"type": "exact_match", "value": 82.011, "name": "Exact Match"}, {"type": "f1", "value": 91.092, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 84.203, "name": "Exact Match"}, {"type": "f1", "value": 91.521, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 72.029, "name": "Exact Match"}, {"type": "f1", "value": 83.454, "name": "F1"}]}]}]}
question-answering
deepset/roberta-base-squad2-distilled
[ "transformers", "pytorch", "safetensors", "roberta", "question-answering", "exbert", "en", "dataset:squad_v2", "license:mit", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us
## Overview Language model: deepset/roberta-base-squad2-distilled Language: English Training data: SQuAD 2.0 training set Eval data: SQuAD 2.0 dev set Infrastructure: 4x V100 GPU Published: Dec 8th, 2021 ## Details - haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model. ## Hyperparameters ## Performance ## Authors Timo Möller: timo.moeller@URL Julian Risch: URL@URL Malte Pietsch: malte.pietsch@URL Michel Bartels: michel.bartels@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - Distilled roberta-base-squad2 (aka "tinyroberta-squad2") - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set\nEval data: SQuAD 2.0 dev set\nInfrastructure: 4x V100 GPU \nPublished: Dec 8th, 2021", "## Details\n- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.", "## Hyperparameters", "## Performance", "## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us \n", "## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set\nEval data: SQuAD 2.0 dev set\nInfrastructure: 4x V100 GPU \nPublished: Dec 8th, 2021", "## Details\n- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.", "## Hyperparameters", "## Performance", "## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 62, 57, 36, 5, 2, 44, 251, 113 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us \n## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set\nEval data: SQuAD 2.0 dev set\nInfrastructure: 4x V100 GPU \nPublished: Dec 8th, 2021## Details\n- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.## Hyperparameters## Performance## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")" ]
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transformers
# roberta-base for QA This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/roberta-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "HasAns_total": 5928, "NoAns_exact": 81.79983179142137, "NoAns_f1": 81.79983179142137, "NoAns_total": 5945 ``` ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
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question-answering
deepset/roberta-base-squad2
[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "roberta", "question-answering", "en", "dataset:squad_v2", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tf #jax #rust #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
# roberta-base for QA This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview Language model: roberta-base Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example QA pipeline on Haystack Infrastructure: 4x Tesla v100 ## Hyperparameters ## Using a distilled model instead Please note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: For a complete example of ''roberta-base-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation ### In Transformers ## Performance Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Authors Branden Chan: URL@URL Timo Möller: timo.moeller@URL Malte Pietsch: malte.pietsch@URL Tanay Soni: URL@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - Distilled roberta-base-squad2 (aka "tinyroberta-squad2") - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# roberta-base for QA \n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.", "## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-base-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n", "# roberta-base for QA \n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.", "## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-base-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 71, 59, 60, 5, 57, 3, 84, 6, 19, 41, 251, 113 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n# roberta-base for QA \n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100## Hyperparameters## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/tinyroberta-squad2. The distilled model has a comparable prediction quality and runs at twice the speed of the base model.## Usage### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-base-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation### In Transformers## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL" ]
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null
transformers
# roberta-large for QA This is the [roberta-large](https://huggingface.co/roberta-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** roberta-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` base_LM_model = "roberta-large" ``` ## Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/roberta-base-squad2-distilled](https://huggingface.co/deepset/roberta-base-squad2-distilled). The distilled model has a comparable prediction quality and runs at twice the speed of the large model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-large-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2") ``` For a complete example of ``roberta-large-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/roberta-large-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
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question-answering
deepset/roberta-large-squad2
[ "transformers", "pytorch", "jax", "safetensors", "roberta", "question-answering", "en", "dataset:squad_v2", "base_model:roberta-large", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #base_model-roberta-large #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
# roberta-large for QA This is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview Language model: roberta-large Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example QA pipeline on Haystack Infrastructure: 4x Tesla v100 ## Hyperparameters ## Using a distilled model instead Please note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: For a complete example of ''roberta-large-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation ### In Transformers ## Authors Branden Chan: URL@URL Timo Möller: timo.moeller@URL Malte Pietsch: malte.pietsch@URL Tanay Soni: URL@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - Distilled roberta-base-squad2 (aka "tinyroberta-squad2") - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# roberta-large for QA \n\nThis is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.", "## Overview\nLanguage model: roberta-large \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-large-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation", "### In Transformers", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #base_model-roberta-large #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n", "# roberta-large for QA \n\nThis is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.", "## Overview\nLanguage model: roberta-large \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-large-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation", "### In Transformers", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 76, 61, 61, 5, 62, 3, 85, 6, 41, 251, 113 ]
[ "passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #base_model-roberta-large #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n# roberta-large for QA \n\nThis is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.## Overview\nLanguage model: roberta-large \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100## Hyperparameters## Using a distilled model instead\nPlease note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model.## Usage### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''roberta-large-squad2'' being used for Question Answering, check out the Tutorials in Haystack Documentation### In Transformers## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL" ]
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null
null
transformers
This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (https://github.com/UKPLab/sentence-transformers)
{"license": "apache-2.0"}
null
deepset/sentence_bert
[ "transformers", "pytorch", "jax", "bert", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #jax #bert #license-apache-2.0 #endpoints_compatible #has_space #region-us
This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (URL
[]
[ "TAGS\n#transformers #pytorch #jax #bert #license-apache-2.0 #endpoints_compatible #has_space #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #license-apache-2.0 #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models). It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions that can be used differing only in the table scoring head. The default one has an adapted table scoring head in order to be able to generate probabilities out of the logits. The other (non-default) version corresponds to the original checkpoint from the TaPas repository and can be accessed by setting `revision="original"`. # Usage ## In Haystack If you want to use this model for question-answering over tables, you can load it in [Haystack](https://github.com/deepset-ai/haystack/): ```python from haystack.nodes import TableReader table_reader = TableReader(model_name_or_path="deepset/tapas-large-nq-hn-reader") ```
{"language": "en", "license": "apache-2.0", "tags": ["tapas"]}
null
deepset/tapas-large-nq-hn-reader
[ "transformers", "pytorch", "tapas", "en", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #has_space #region-us
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the TaPas repository. It is described in Herzig et al.'s (2021) paper _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions that can be used differing only in the table scoring head. The default one has an adapted table scoring head in order to be able to generate probabilities out of the logits. The other (non-default) version corresponds to the original checkpoint from the TaPas repository and can be accessed by setting 'revision="original"'. # Usage ## In Haystack If you want to use this model for question-answering over tables, you can load it in Haystack:
[ "# Usage", "## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
[ "TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n", "# Usage", "## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
[ 38, 3, 30 ]
[ "passage: TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# Usage## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
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null
null
transformers
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models). It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions which can be used differing only in the table scoring head. The default one has an adapted table scoring head in order to be able to generate probabilities out of the logits. The other (non-default) version corredponds to the original checkpoint from the TaPas repository and can be accessed setting `revision="original"`. # Usage ## In Haystack If you want to use this model for question-answering over tables, you can load it in [Haystack](https://github.com/deepset-ai/haystack/): ```python from haystack.nodes import TableReader table_reader = TableReader(model_name_or_path="deepset/tapas-large-nq-reader") ```
{"language": "en", "license": "apache-2.0", "tags": ["tapas"]}
null
deepset/tapas-large-nq-reader
[ "transformers", "pytorch", "tapas", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #region-us
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the TaPas repository. It is described in Herzig et al.'s (2021) paper _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions which can be used differing only in the table scoring head. The default one has an adapted table scoring head in order to be able to generate probabilities out of the logits. The other (non-default) version corredponds to the original checkpoint from the TaPas repository and can be accessed setting 'revision="original"'. # Usage ## In Haystack If you want to use this model for question-answering over tables, you can load it in Haystack:
[ "# Usage", "## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
[ "TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #region-us \n", "# Usage", "## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
[ 34, 3, 30 ]
[ "passage: TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #region-us \n# Usage## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:" ]
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null
null
transformers
## Overview **Language model:** deepset/tinybert-6L-768D-squad2 **Language:** English **Training data:** SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 1x V100 GPU **Published**: Dec 8th, 2021 ## Details - haystack's intermediate layer and prediction layer distillation features were used for training (based on [TinyBERT](https://arxiv.org/pdf/1909.10351.pdf)). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model. ## Hyperparameters ### Intermediate layer distillation ``` batch_size = 26 n_epochs = 5 max_seq_len = 384 learning_rate = 5e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 temperature = 1 ``` ### Prediction layer distillation ``` batch_size = 26 n_epochs = 5 max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 temperature = 1 distillation_loss_weight = 1.0 ``` ## Performance ``` "exact": 71.87736882001179 "f1": 76.36111895973675 ``` ## Authors - Timo Möller: `timo.moeller [at] deepset.ai` - Julian Risch: `julian.risch [at] deepset.ai` - Malte Pietsch: `malte.pietsch [at] deepset.ai` - Michel Bartels: `michel.bartels [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/tinybert-6l-768d-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 73.8248, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFmZmFiN2E5ODZkOTkyMjQ1NTUzMmQwMjc0M2RlYzVlNmM4YTFlNzA4YzIwY2JkY2EyNDg2ZTY3OTdjZTVlZiIsInZlcnNpb24iOjF9.ZZ6c2OI3lzeNhuSWTh28j00zk-sPrqkTvdVBZv2wJc1D4YnR-xOj72haybT6MV_xeYqTg3-x9L8PsWSS20NaDw"}, {"type": "f1", "value": 77.1684, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzAxMDk1YzI5ZjA2N2ZmMzAxNjgxYzJiNzAzYmI1ZWU5ZDRmYWY3OWJmMjlmNDcyMGE0YWY5NjNhZTk4YWY5ZSIsInZlcnNpb24iOjF9.rF3raNGUSYv5D2xzWLZztD99vwDKvWb22LG32RomrDGP6XKTbCVqZzAw5UFw93jKb0VoLApbQQ-AOGxLj3U_Cg"}]}]}]}
question-answering
deepset/tinybert-6l-768d-squad2
[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "exbert", "en", "dataset:squad_v2", "arxiv:1909.10351", "license:mit", "model-index", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1909.10351" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #arxiv-1909.10351 #license-mit #model-index #endpoints_compatible #region-us
## Overview Language model: deepset/tinybert-6L-768D-squad2 Language: English Training data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation Eval data: SQuAD 2.0 dev set Infrastructure: 1x V100 GPU Published: Dec 8th, 2021 ## Details - haystack's intermediate layer and prediction layer distillation features were used for training (based on TinyBERT). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model. ## Hyperparameters ### Intermediate layer distillation ### Prediction layer distillation ## Performance ## Authors - Timo Möller: 'timo.moeller [at] URL' - Julian Risch: 'URL [at] URL' - Malte Pietsch: 'malte.pietsch [at] URL' - Michel Bartels: 'michel.bartels [at] URL' ## About us !deepset logo We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") - FARM - Haystack Get in touch: Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "## Overview\nLanguage model: deepset/tinybert-6L-768D-squad2 \nLanguage: English \nTraining data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation \nEval data: SQuAD 2.0 dev set \nInfrastructure: 1x V100 GPU \nPublished: Dec 8th, 2021", "## Details\n- haystack's intermediate layer and prediction layer distillation features were used for training (based on TinyBERT). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model.", "## Hyperparameters", "### Intermediate layer distillation", "### Prediction layer distillation", "## Performance", "## Authors\n- Timo Möller: 'timo.moeller [at] URL'\n- Julian Risch: 'URL [at] URL'\n- Malte Pietsch: 'malte.pietsch [at] URL'\n- Michel Bartels: 'michel.bartels [at] URL'", "## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #arxiv-1909.10351 #license-mit #model-index #endpoints_compatible #region-us \n", "## Overview\nLanguage model: deepset/tinybert-6L-768D-squad2 \nLanguage: English \nTraining data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation \nEval data: SQuAD 2.0 dev set \nInfrastructure: 1x V100 GPU \nPublished: Dec 8th, 2021", "## Details\n- haystack's intermediate layer and prediction layer distillation features were used for training (based on TinyBERT). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model.", "## Hyperparameters", "### Intermediate layer distillation", "### Prediction layer distillation", "## Performance", "## Authors\n- Timo Möller: 'timo.moeller [at] URL'\n- Julian Risch: 'URL [at] URL'\n- Malte Pietsch: 'malte.pietsch [at] URL'\n- Michel Bartels: 'michel.bartels [at] URL'", "## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 65, 72, 77, 5, 9, 9, 2, 63, 129 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #arxiv-1909.10351 #license-mit #model-index #endpoints_compatible #region-us \n## Overview\nLanguage model: deepset/tinybert-6L-768D-squad2 \nLanguage: English \nTraining data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation \nEval data: SQuAD 2.0 dev set \nInfrastructure: 1x V100 GPU \nPublished: Dec 8th, 2021## Details\n- haystack's intermediate layer and prediction layer distillation features were used for training (based on TinyBERT). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model.## Hyperparameters### Intermediate layer distillation### Prediction layer distillation## Performance## Authors\n- Timo Möller: 'timo.moeller [at] URL'\n- Julian Risch: 'URL [at] URL'\n- Malte Pietsch: 'malte.pietsch [at] URL'\n- Michel Bartels: 'michel.bartels [at] URL'## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
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null
null
transformers
# tinyroberta-squad2 ## Overview **Language model:** tinyroberta-squad2 **Language:** English **Training data:** The PILE **Code:** **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 4 base_LM_model = "deepset/tinyroberta-squad2-step1" max_seq_len = 384 learning_rate = 1e-4 lr_schedule = LinearWarmup warmup_proportion = 0.2 teacher = "deepset/roberta-base" ``` ## Distillation This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack). We have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d). This model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new [distillation functionality](https://haystack.deepset.ai/guides/model-distillation). You can also check out [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2) for a model that is already distilled on an extractive QA downstream task. ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/tinyroberta-squad2" model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ### In FARM ```python from farm.modeling.adaptive_model import AdaptiveModel from farm.modeling.tokenization import Tokenizer from farm.infer import Inferencer model_name = "deepset/tinyroberta-squad2" model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") tokenizer = Tokenizer.load(model_name) ``` ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` ## Authors Branden Chan: `branden.chan [at] deepset.ai` Timo Möller: `timo.moeller [at] deepset.ai` Malte Pietsch: `malte.pietsch [at] deepset.ai` Tanay Soni: `tanay.soni [at] deepset.ai` Michel Bartels: `michel.bartels [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]}
question-answering
deepset/tinyroberta-6l-768d
[ "transformers", "pytorch", "safetensors", "roberta", "question-answering", "en", "dataset:squad_v2", "arxiv:1909.10351", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1909.10351" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #endpoints_compatible #region-us
# tinyroberta-squad2 ## Overview Language model: tinyroberta-squad2 Language: English Training data: The PILE Code: Infrastructure: 4x Tesla v100 ## Hyperparameters ## Distillation This model was distilled using the TinyBERT approach described in this paper and implemented in haystack. We have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d. This model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new distillation functionality. You can also check out deepset/tinyroberta-squad2 for a model that is already distilled on an extractive QA downstream task. ## Usage ### In Transformers ### In FARM ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack: ## Authors Branden Chan: 'URL [at] URL' Timo Möller: 'timo.moeller [at] URL' Malte Pietsch: 'malte.pietsch [at] URL' Tanay Soni: 'URL [at] URL' Michel Bartels: 'michel.bartels [at] URL' ## About us !deepset logo We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") - FARM - Haystack Get in touch: Twitter | LinkedIn | Slack | GitHub Discussions | Website By the way: we're hiring!
[ "# tinyroberta-squad2", "## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nTraining data: The PILE \nCode: \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nWe have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nThis model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new distillation functionality. You can also check out deepset/tinyroberta-squad2 for a model that is already distilled on an extractive QA downstream task.", "## Usage", "### In Transformers", "### In FARM", "### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'\nMichel Bartels: 'michel.bartels [at] URL'", "## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Slack | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #endpoints_compatible #region-us \n", "# tinyroberta-squad2", "## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nTraining data: The PILE \nCode: \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nWe have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nThis model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new distillation functionality. You can also check out deepset/tinyroberta-squad2 for a model that is already distilled on an extractive QA downstream task.", "## Usage", "### In Transformers", "### In FARM", "### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'\nMichel Bartels: 'michel.bartels [at] URL'", "## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Slack | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 63, 9, 33, 5, 146, 3, 6, 5, 36, 71, 129 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #endpoints_compatible #region-us \n# tinyroberta-squad2## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nTraining data: The PILE \nCode: \nInfrastructure: 4x Tesla v100## Hyperparameters## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nWe have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nThis model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new distillation functionality. You can also check out deepset/tinyroberta-squad2 for a model that is already distilled on an extractive QA downstream task.## Usage### In Transformers### In FARM### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'\nMichel Bartels: 'michel.bartels [at] URL'## About us\n!deepset logo\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Slack | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
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transformers
# tinyroberta-squad2 This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model. ## Overview **Language model:** tinyroberta-squad2 **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 4 base_LM_model = "deepset/tinyroberta-squad2-step1" max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride = 128 max_query_length = 64 distillation_loss_weight = 0.75 temperature = 1.5 teacher = "deepset/robert-large-squad2" ``` ## Distillation This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack). Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d). Secondly, we have performed task-specific distillation with [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) as the teacher for prediction layer distillation. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/tinyroberta-squad2") # or reader = TransformersReader(model_name_or_path="deepset/tinyroberta-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/tinyroberta-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 78.69114798281817, "f1": 81.9198998536977, "total": 11873, "HasAns_exact": 76.19770580296895, "HasAns_f1": 82.66446878592329, "HasAns_total": 5928, "NoAns_exact": 81.17746005046257, "NoAns_f1": 81.17746005046257, "NoAns_total": 5945 ``` ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] **Michel Bartels:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [roberta-base-squad2]([https://huggingface.co/deepset/roberta-base-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
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question-answering
deepset/tinyroberta-squad2
[ "transformers", "pytorch", "safetensors", "roberta", "question-answering", "en", "dataset:squad_v2", "arxiv:1909.10351", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1909.10351" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
# tinyroberta-squad2 This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model. ## Overview Language model: tinyroberta-squad2 Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example QA pipeline on Haystack Infrastructure: 4x Tesla v100 ## Hyperparameters ## Distillation This model was distilled using the TinyBERT approach described in this paper and implemented in haystack. Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d. Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: ### In Transformers ## Performance Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Authors Branden Chan: URL@URL Timo Möller: timo.moeller@URL Malte Pietsch: malte.pietsch@URL Tanay Soni: URL@URL Michel Bartels: michel.bartels@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - roberta-base-squad2 - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# tinyroberta-squad2\n\nThis is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.", "## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nFirstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nSecondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL \nMichel Bartels: michel.bartels@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- roberta-base-squad2\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n", "# tinyroberta-squad2\n\nThis is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.", "## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nFirstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nSecondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation.", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL \nMichel Bartels: michel.bartels@URL", "## About us\n\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- roberta-base-squad2\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 71, 52, 64, 5, 140, 3, 51, 6, 19, 52, 237, 113 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n# tinyroberta-squad2\n\nThis is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 4x Tesla v100## Hyperparameters## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.\nFirstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.\nSecondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation.## Usage### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:### In Transformers## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL \nMichel Bartels: michel.bartels@URL" ]
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null
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transformers
# deepset/xlm-roberta-base-squad2-distilled - haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model. ## Overview **Language model:** deepset/xlm-roberta-base-squad2-distilled **Language:** Multilingual **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 1x Tesla v100 ## Hyperparameters ``` batch_size = 56 n_epochs = 4 max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 temperature = 3 distillation_loss_weight = 0.75 ``` ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled") # or reader = TransformersReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled",tokenizer="deepset/xlm-roberta-base-squad2-distilled") ``` For a complete example of ``deepset/xlm-roberta-base-squad2-distilled`` being used for [question answering], check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/xlm-roberta-base-squad2-distilled" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set ``` "exact": 74.06721131980123% "f1": 76.39919553344667% ``` ## Authors **Timo Möller:** [email protected] **Julian Risch:** [email protected] **Malte Pietsch:** [email protected] **Michel Bartels:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"language": "multilingual", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"}
question-answering
deepset/xlm-roberta-base-squad2-distilled
[ "transformers", "pytorch", "safetensors", "xlm-roberta", "question-answering", "exbert", "multilingual", "dataset:squad_v2", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "multilingual" ]
TAGS #transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us
# deepset/xlm-roberta-base-squad2-distilled - haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model. ## Overview Language model: deepset/xlm-roberta-base-squad2-distilled Language: Multilingual Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example QA pipeline on Haystack Infrastructure: 1x Tesla v100 ## Hyperparameters ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack: For a complete example of ''deepset/xlm-roberta-base-squad2-distilled'' being used for [question answering], check out the Tutorials in Haystack Documentation ### In Transformers ## Performance Evaluated on the SQuAD 2.0 dev set ## Authors Timo Möller: timo.moeller@URL Julian Risch: URL@URL Malte Pietsch: malte.pietsch@URL Michel Bartels: michel.bartels@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - Distilled roberta-base-squad2 (aka "tinyroberta-squad2") - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# deepset/xlm-roberta-base-squad2-distilled\n- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.", "## Overview\nLanguage model: deepset/xlm-roberta-base-squad2-distilled \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 1x Tesla v100", "## Hyperparameters", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''deepset/xlm-roberta-base-squad2-distilled'' being used for [question answering], check out the Tutorials in Haystack Documentation", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set", "## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us \n", "# deepset/xlm-roberta-base-squad2-distilled\n- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.", "## Overview\nLanguage model: deepset/xlm-roberta-base-squad2-distilled \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 1x Tesla v100", "## Hyperparameters", "## Usage", "### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''deepset/xlm-roberta-base-squad2-distilled'' being used for [question answering], check out the Tutorials in Haystack Documentation", "### In Transformers", "## Performance\nEvaluated on the SQuAD 2.0 dev set", "## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 63, 55, 75, 5, 3, 96, 6, 12, 44, 251, 113 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us \n# deepset/xlm-roberta-base-squad2-distilled\n- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.## Overview\nLanguage model: deepset/xlm-roberta-base-squad2-distilled \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack \nInfrastructure: 1x Tesla v100## Hyperparameters## Usage### In Haystack\nHaystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in Haystack:\n\nFor a complete example of ''deepset/xlm-roberta-base-squad2-distilled'' being used for [question answering], check out the Tutorials in Haystack Documentation### In Transformers## Performance\nEvaluated on the SQuAD 2.0 dev set## Authors\nTimo Möller: timo.moeller@URL \nJulian Risch: URL@URL \nMalte Pietsch: malte.pietsch@URL \nMichel Bartels: michel.bartels@URL" ]
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null
null
transformers
# Multilingual XLM-RoBERTa base for QA on various languages ## Overview **Language model:** xlm-roberta-base **Language:** Multilingual **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 dev set - German MLQA - German XQuAD **Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 22*4 n_epochs = 2 max_seq_len=256, doc_stride=128, learning_rate=2e-5, ``` Corresponding experiment logs in mlflow: [link](https://public-mlflow.deepset.ai/#/experiments/2/runs/b25ec75e07614accb3f1ce03d43dbe08) ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 73.91560683904657 "f1": 77.14103746689592 ``` Evaluated on German MLQA: test-context-de-question-de.json "exact": 33.67279167589108 "f1": 44.34437105434842 "total": 4517 Evaluated on German XQuAD: xquad.de.json "exact": 48.739495798319325 "f1": 62.552615701071495 "total": 1190 ## Usage ### In Transformers ```python from transformers.pipelines import pipeline from transformers.modeling_auto import AutoModelForQuestionAnswering from transformers.tokenization_auto import AutoTokenizer model_name = "deepset/xlm-roberta-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ### In FARM ```python from farm.modeling.adaptive_model import AdaptiveModel from farm.modeling.tokenization import Tokenizer from farm.infer import Inferencer model_name = "deepset/xlm-roberta-base-squad2" # a) Get predictions nlp = Inferencer.load(model_name, task_type="question_answering") QA_input = [{"questions": ["Why is model conversion important?"], "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}] res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True) # b) Load model & tokenizer model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") tokenizer = Tokenizer.load(model_name) ``` ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2") # or reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/xlm-roberta-base-squad2") ``` ## Authors Branden Chan: `branden.chan [at] deepset.ai` Timo Möller: `timo.moeller [at] deepset.ai` Malte Pietsch: `malte.pietsch [at] deepset.ai` Tanay Soni: `tanay.soni [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/xlm-roberta-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 74.0354, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWMxNWQ2ODJkNWIzZGQwOWI4OTZjYjU3ZDVjZGQzMjI5MzljNjliZTY4Mzk4YTk4OTMzZWYxZjUxYmZhYTBhZSIsInZlcnNpb24iOjF9.eEeFYYJ30BfJDd-JYfI1kjlxJrRF6OFtj2GnkTCOO4kqX31inFy8ptDWusVlLFsUphm4dNWfTKXC5e-gytLBDA"}, {"type": "f1", "value": 77.1833, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg4MjNkOTA4Y2I5OGFlYTk1NWZjMWFlNjI5M2Y0NGZhMThhN2M4YmY2Y2RhZjcwYzU0MGNjN2RkZDljZmJmNiIsInZlcnNpb24iOjF9.TX42YMXpH4e0qu7cC4ARDlZWSkd55dwwyeyFXmOlXERNnEicDuFBCsy8WHLaqQCLUkzODJ22Hw4zhv81rwnlAQ"}]}]}]}
question-answering
deepset/xlm-roberta-base-squad2
[ "transformers", "pytorch", "safetensors", "xlm-roberta", "question-answering", "dataset:squad_v2", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #safetensors #xlm-roberta #question-answering #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
# Multilingual XLM-RoBERTa base for QA on various languages ## Overview Language model: xlm-roberta-base Language: Multilingual Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 dev set - German MLQA - German XQuAD Code: See example in FARM Infrastructure: 4x Tesla v100 ## Hyperparameters Corresponding experiment logs in mlflow: link ## Performance Evaluated on the SQuAD 2.0 dev set with the official eval script. Evaluated on German MLQA: URL "exact": 33.67279167589108 "f1": 44.34437105434842 "total": 4517 Evaluated on German XQuAD: URL "exact": 48.739495798319325 "f1": 62.552615701071495 "total": 1190 ## Usage ### In Transformers ### In FARM ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack: ## Authors Branden Chan: 'URL [at] URL' Timo Möller: 'timo.moeller [at] URL' Malte Pietsch: 'malte.pietsch [at] URL' Tanay Soni: 'URL [at] URL' ## About us !deepset logo We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") - FARM - Haystack Get in touch: Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# Multilingual XLM-RoBERTa base for QA on various languages", "## Overview\nLanguage model: xlm-roberta-base \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 dev set - German MLQA - German XQuAD \nCode: See example in FARM \nInfrastructure: 4x Tesla v100", "## Hyperparameters\n\n \n\nCorresponding experiment logs in mlflow: link", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n \"exact\": 33.67279167589108\n \"f1\": 44.34437105434842\n \"total\": 4517\n\nEvaluated on German XQuAD: URL\n\"exact\": 48.739495798319325\n \"f1\": 62.552615701071495\n \"total\": 1190", "## Usage", "### In Transformers", "### In FARM", "### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'", "## About us\n!deepset logo\n\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n", "# Multilingual XLM-RoBERTa base for QA on various languages", "## Overview\nLanguage model: xlm-roberta-base \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 dev set - German MLQA - German XQuAD \nCode: See example in FARM \nInfrastructure: 4x Tesla v100", "## Hyperparameters\n\n \n\nCorresponding experiment logs in mlflow: link", "## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n \"exact\": 33.67279167589108\n \"f1\": 44.34437105434842\n \"total\": 4517\n\nEvaluated on German XQuAD: URL\n\"exact\": 48.739495798319325\n \"f1\": 62.552615701071495\n \"total\": 1190", "## Usage", "### In Transformers", "### In FARM", "### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'", "## About us\n!deepset logo\n\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 64, 18, 71, 16, 100, 3, 6, 5, 36, 56, 129 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n# Multilingual XLM-RoBERTa base for QA on various languages## Overview\nLanguage model: xlm-roberta-base \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 dev set - German MLQA - German XQuAD \nCode: See example in FARM \nInfrastructure: 4x Tesla v100## Hyperparameters\n\n \n\nCorresponding experiment logs in mlflow: link## Performance\nEvaluated on the SQuAD 2.0 dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n \"exact\": 33.67279167589108\n \"f1\": 44.34437105434842\n \"total\": 4517\n\nEvaluated on German XQuAD: URL\n\"exact\": 48.739495798319325\n \"f1\": 62.552615701071495\n \"total\": 1190## Usage### In Transformers### In FARM### In haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:## Authors\nBranden Chan: 'URL [at] URL'\nTimo Möller: 'timo.moeller [at] URL'\nMalte Pietsch: 'malte.pietsch [at] URL'\nTanay Soni: 'URL [at] URL'## About us\n!deepset logo\n\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")\n- FARM\n- Haystack\n\nGet in touch:\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
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null
null
transformers
# Multilingual XLM-RoBERTa large for QA on various languages ## Overview **Language model:** xlm-roberta-large **Language:** Multilingual **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD dev set - German MLQA - German XQuAD **Training run:** [MLFlow link](https://public-mlflow.deepset.ai/#/experiments/124/runs/3a540e3f3ecf4dd98eae8fc6d457ff20) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "xlm-roberta-large" max_seq_len = 256 learning_rate = 1e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD 2.0 English dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.45759285774446, "f1": 83.79259828925511, "total": 11873, "HasAns_exact": 71.96356275303644, "HasAns_f1": 80.6460053117963, "HasAns_total": 5928, "NoAns_exact": 86.93019343986543, "NoAns_f1": 86.93019343986543, "NoAns_total": 5945 ``` Evaluated on German [MLQA: test-context-de-question-de.json](https://github.com/facebookresearch/MLQA) ``` "exact": 49.34691166703564, "f1": 66.15582561674236, "total": 4517, ``` Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad) ``` "exact": 61.51260504201681, "f1": 78.80206098332569, "total": 1190, ``` ## Usage ### In Haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2") # or reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/xlm-roberta-large-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
{"language": "multilingual", "license": "cc-by-4.0", "tags": ["question-answering"], "datasets": ["squad_v2"], "model-index": [{"name": "deepset/xlm-roberta-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 81.8281, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA"}, {"type": "f1", "value": 84.8886, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ"}]}]}]}
question-answering
deepset/xlm-roberta-large-squad2
[ "transformers", "pytorch", "safetensors", "xlm-roberta", "question-answering", "multilingual", "dataset:squad_v2", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "multilingual" ]
TAGS #transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
# Multilingual XLM-RoBERTa large for QA on various languages ## Overview Language model: xlm-roberta-large Language: Multilingual Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD dev set - German MLQA - German XQuAD Training run: MLFlow link Infrastructure: 4x Tesla v100 ## Hyperparameters ## Performance Evaluated on the SQuAD 2.0 English dev set with the official eval script. Evaluated on German MLQA: URL Evaluated on German XQuAD: URL ## Usage ### In Haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack: ### In Transformers ## Authors Branden Chan: URL@URL Timo Möller: timo.moeller@URL Malte Pietsch: malte.pietsch@URL Tanay Soni: URL@URL ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="URL class="w-40"/> </div> </div> deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - Distilled roberta-base-squad2 (aka "tinyroberta-squad2") - German BERT (aka "bert-base-german-cased") - GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p> Twitter | LinkedIn | Discord | GitHub Discussions | Website By the way: we're hiring!
[ "# Multilingual XLM-RoBERTa large for QA on various languages", "## Overview\nLanguage model: xlm-roberta-large \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD dev set - German MLQA - German XQuAD \nTraining run: MLFlow link \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Performance\nEvaluated on the SQuAD 2.0 English dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n\n\nEvaluated on German XQuAD: URL", "## Usage", "### In Haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "### In Transformers", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n", "# Multilingual XLM-RoBERTa large for QA on various languages", "## Overview\nLanguage model: xlm-roberta-large \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD dev set - German MLQA - German XQuAD \nTraining run: MLFlow link \nInfrastructure: 4x Tesla v100", "## Hyperparameters", "## Performance\nEvaluated on the SQuAD 2.0 English dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n\n\nEvaluated on German XQuAD: URL", "## Usage", "### In Haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:", "### In Transformers", "## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL", "## About us\n<div class=\"grid lg:grid-cols-2 gap-x-4 gap-y-3\">\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n <div class=\"w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center\">\n <img alt=\"\" src=\"URL class=\"w-40\"/>\n </div>\n</div>\n\ndeepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.\n\n\nSome of our other work: \n- Distilled roberta-base-squad2 (aka \"tinyroberta-squad2\")\n- German BERT (aka \"bert-base-german-cased\")\n- GermanQuAD and GermanDPR datasets and models (aka \"gelectra-base-germanquad\", \"gbert-base-germandpr\")", "## Get in touch and join the Haystack community\n\n<p>For more info on Haystack, visit our <strong><a href=\"URL repo and <strong><a href=\"URL\">Documentation</a></strong>. \n\nWe also have a <strong><a class=\"h-7\" href=\"URL community open to everyone!</a></strong></p>\n\nTwitter | LinkedIn | Discord | GitHub Discussions | Website\n\nBy the way: we're hiring!" ]
[ 68, 18, 71, 5, 38, 3, 36, 6, 41, 251, 113 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n# Multilingual XLM-RoBERTa large for QA on various languages## Overview\nLanguage model: xlm-roberta-large \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD dev set - German MLQA - German XQuAD \nTraining run: MLFlow link \nInfrastructure: 4x Tesla v100## Hyperparameters## Performance\nEvaluated on the SQuAD 2.0 English dev set with the official eval script.\n\n\nEvaluated on German MLQA: URL\n\n\nEvaluated on German XQuAD: URL## Usage### In Haystack\nFor doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:### In Transformers## Authors\nBranden Chan: URL@URL \nTimo Möller: timo.moeller@URL \nMalte Pietsch: malte.pietsch@URL \nTanay Soni: URL@URL" ]
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null
null
transformers
``` from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline model = BertForSequenceClassification.from_pretrained("deeq/dbert-sentiment") tokenizer = BertTokenizer.from_pretrained("deeq/dbert") nlp = TextClassificationPipeline(model=model, tokenizer=tokenizer) print(nlp("좋아요")) print(nlp("글쎄요")) ```
{}
text-classification
baikal-nlp/dbert-sentiment
[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
[]
[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 36 ]
[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
deeqBERT-base --- - model: bert-base - vocab: bert-wordpiece, 35k - version: latest
{"language": "ko", "datasets": ["kowiki", "news"]}
fill-mask
baikal-nlp/dbert
[ "transformers", "pytorch", "bert", "fill-mask", "ko", "dataset:kowiki", "dataset:news", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ko" ]
TAGS #transformers #pytorch #bert #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us
deeqBERT-base --- - model: bert-base - vocab: bert-wordpiece, 35k - version: latest
[]
[ "TAGS\n#transformers #pytorch #bert #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 49 ]
[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
deeqBERT5 --- - model: bert-base - vocab: deeqnlp 1.5, 50k - version: latest/3.5
{}
null
baikal-nlp/dbert5
[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #endpoints_compatible #region-us
deeqBERT5 --- - model: bert-base - vocab: deeqnlp 1.5, 50k - version: latest/3.5
[]
[ "TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n" ]
[ 21 ]
[ "passage: TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n" ]
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null
null
transformers
deeqELECTRA-base --- - model: electra-base-generator - vocab: bert-wordpiece, 35k - version: beta, 1.71M
{"language": "ko", "datasets": ["kowiki", "news"]}
fill-mask
baikal-nlp/delectra-generator
[ "transformers", "pytorch", "electra", "fill-mask", "ko", "dataset:kowiki", "dataset:news", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ko" ]
TAGS #transformers #pytorch #electra #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us
deeqELECTRA-base --- - model: electra-base-generator - vocab: bert-wordpiece, 35k - version: beta, 1.71M
[]
[ "TAGS\n#transformers #pytorch #electra #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 50 ]
[ "passage: TAGS\n#transformers #pytorch #electra #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
deeqELECTRA-base --- - model: electra-base-discriminator - vocab: bert-wordpiece, 35k - version: beta, 1.71M
{"language": "ko", "datasets": ["kowiki", "news"]}
null
baikal-nlp/delectra
[ "transformers", "pytorch", "electra", "pretraining", "ko", "dataset:kowiki", "dataset:news", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ko" ]
TAGS #transformers #pytorch #electra #pretraining #ko #dataset-kowiki #dataset-news #endpoints_compatible #region-us
deeqELECTRA-base --- - model: electra-base-discriminator - vocab: bert-wordpiece, 35k - version: beta, 1.71M
[]
[ "TAGS\n#transformers #pytorch #electra #pretraining #ko #dataset-kowiki #dataset-news #endpoints_compatible #region-us \n" ]
[ 40 ]
[ "passage: TAGS\n#transformers #pytorch #electra #pretraining #ko #dataset-kowiki #dataset-news #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-amazon-reviews This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-amazon-reviews", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]}
text-generation
defex/distilgpt2-finetuned-amazon-reviews
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# distilgpt2-finetuned-amazon-reviews This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3
[ "# distilgpt2-finetuned-amazon-reviews\n\nThis model was trained from scratch on the None 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: 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", "### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.9.0\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# distilgpt2-finetuned-amazon-reviews\n\nThis model was trained from scratch on the None 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: 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", "### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.9.0\n- Tokenizers 0.10.3" ]
[ 58, 29, 6, 12, 8, 3, 90, 35 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# distilgpt2-finetuned-amazon-reviews\n\nThis model was trained from scratch on the None 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: 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### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.9.0\n- Tokenizers 0.10.3" ]
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null
null
transformers
# german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions. ## Task example #### Input generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert. #### Expected Question Zu welchen Gelegenheiten passt der Monk Sour gut? ## Model description The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). We have not yet open sourced the dataset, since we do not own copyright on the source material. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ## Evaluation It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
{"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "generate question: Der Monk Sour Drink ist ein somit eine aromatische \u00dcberraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert."}], "model-index": [{"name": "german-qg-t5-drink600", "results": []}]}
text2text-generation
dehio/german-qg-t5-drink600
[ "transformers", "pytorch", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de" ]
TAGS #transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions. ## Task example #### Input generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert. #### Expected Question Zu welchen Gelegenheiten passt der Monk Sour gut? ## Model description The model is based on german-qg-t5-quad, which was pre-trained on GermanQUAD. We further pre-trained it on questions annotated on drink receipts from Mixology ("drink600"). We have not yet open sourced the dataset, since we do not own copyright on the source material. ## Training and evaluation data The training script can be accessed here. ## Evaluation It achieves a BLEU-4 score of 29.80 on the drink600 test set (n=120) and 11.30 on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of 10.76 on the drink600 test set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
[ "# german-qg-t5-drink600\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions.", "## Task example", "#### Input\n\ngenerate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, \ndie sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert.", "#### Expected Question\nZu welchen Gelegenheiten passt der Monk Sour gut?", "## Model description\n\nThe model is based on german-qg-t5-quad, which was pre-trained on GermanQUAD. We further pre-trained it on questions annotated on drink receipts from Mixology (\"drink600\"). \nWe have not yet open sourced the dataset, since we do not own copyright on the source material.", "## Training and evaluation data\n\nThe training script can be accessed here.", "## Evaluation\n\nIt achieves a BLEU-4 score of 29.80 on the drink600 test set (n=120) and 11.30 on the GermanQUAD test set. \nThus, fine-tuning on drink600 did not affect performance on GermanQuAD.\n\nIn comparison, *german-qg-t5-quad* achieves a BLEU-4 score of 10.76 on the drink600 test set.", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10", "### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# german-qg-t5-drink600\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions.", "## Task example", "#### Input\n\ngenerate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, \ndie sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert.", "#### Expected Question\nZu welchen Gelegenheiten passt der Monk Sour gut?", "## Model description\n\nThe model is based on german-qg-t5-quad, which was pre-trained on GermanQUAD. We further pre-trained it on questions annotated on drink receipts from Mixology (\"drink600\"). \nWe have not yet open sourced the dataset, since we do not own copyright on the source material.", "## Training and evaluation data\n\nThe training script can be accessed here.", "## Evaluation\n\nIt achieves a BLEU-4 score of 29.80 on the drink600 test set (n=120) and 11.30 on the GermanQUAD test set. \nThus, fine-tuning on drink600 did not affect performance on GermanQuAD.\n\nIn comparison, *german-qg-t5-quad* achieves a BLEU-4 score of 10.76 on the drink600 test set.", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10", "### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ 69, 63, 4, 43, 18, 75, 14, 84, 112, 36 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# german-qg-t5-drink600\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions.## Task example#### Input\n\ngenerate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, \ndie sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert.#### Expected Question\nZu welchen Gelegenheiten passt der Monk Sour gut?## Model description\n\nThe model is based on german-qg-t5-quad, which was pre-trained on GermanQUAD. We further pre-trained it on questions annotated on drink receipts from Mixology (\"drink600\"). \nWe have not yet open sourced the dataset, since we do not own copyright on the source material.## Training and evaluation data\n\nThe training script can be accessed here.## Evaluation\n\nIt achieves a BLEU-4 score of 29.80 on the drink600 test set (n=120) and 11.30 on the GermanQUAD test set. \nThus, fine-tuning on drink600 did not affect performance on GermanQuAD.\n\nIn comparison, *german-qg-t5-quad* achieves a BLEU-4 score of 10.76 on the drink600 test set.### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # german-qg-t5-e2e-quad (Work in progress) This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of [valhalla/t5-base-e2e-qg](https://huggingface.co/valhalla/t5-base-e2e-qg) on the [GermanQuAD dataset from deepset](https://huggingface.co/datasets/deepset/germanquad). ## Model description More information needed ## Training and evaluation data Bleu_1: 0.196051 Bleu_2: 0.122380 Bleu_3: 0.079980 Bleu_4: 0.053672 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "Naturschutzwarte haben auf der ostfriesischen Insel Wangerooge zwei seltene Kurzschn\u00e4uzige Seepferdchen entdeckt. Die Tiere seien vergangene Woche bei einer sogenannten Sp\u00fclsaumkontrolle entdeckt worden, bei der die Str\u00e4nde eigentlich nach M\u00fcll und toten V\u00f6geln abgesucht w\u00fcrden, sagte der Gesch\u00e4ftsf\u00fchrer der zust\u00e4ndigen Naturschutz- und Forschungsgemeinschaft Mellumrat, Mathias Heckroth. Dabei seien den Natursch\u00fctzern am Nordstrand kurz hintereinander die beiden leblosen, nur wenige Zentimeter gro\u00dfen Tiere aufgefallen. Experten der Nationalparkverwaltung bestimmten beide Tiere als Kurzschn\u00e4uzige Seepferdchen (Hippocampus hippocampus)."}], "inference": {"parameters": {"max_length": 128}}, "model-index": [{"name": "german-qg-t5-e2e-quad", "results": []}]}
text2text-generation
dehio/german-qg-t5-e2e-quad
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de" ]
TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
# german-qg-t5-e2e-quad (Work in progress) This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset. ## Model description More information needed ## Training and evaluation data Bleu_1: 0.196051 Bleu_2: 0.122380 Bleu_3: 0.079980 Bleu_4: 0.053672 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
[ "# german-qg-t5-e2e-quad (Work in progress)\n\nThis model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.", "## Model description \n\nMore information needed", "## Training and evaluation data\n\nBleu_1: 0.196051 \nBleu_2: 0.122380 \nBleu_3: 0.079980 \nBleu_4: 0.053672", "## 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: 4\n- seed: 42\n- gradient_accumulation_steps: 8\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: 10.0", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# german-qg-t5-e2e-quad (Work in progress)\n\nThis model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.", "## Model description \n\nMore information needed", "## Training and evaluation data\n\nBleu_1: 0.196051 \nBleu_2: 0.122380 \nBleu_3: 0.079980 \nBleu_4: 0.053672", "## 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: 4\n- seed: 42\n- gradient_accumulation_steps: 8\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: 10.0", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ 77, 80, 6, 37, 3, 113, 4, 38 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# german-qg-t5-e2e-quad (Work in progress)\n\nThis model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.## Model description \n\nMore information needed## Training and evaluation data\n\nBleu_1: 0.196051 \nBleu_2: 0.122380 \nBleu_3: 0.079980 \nBleu_4: 0.053672## 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: 4\n- seed: 42\n- gradient_accumulation_steps: 8\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: 10.0### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
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null
null
transformers
# german-qg-t5-quad This model is fine-tuned in question generation in German. The expected answer must be highlighted with a &lt;hl> token. ## Task example #### Input generate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...] #### Expected output Von welchem Gesetzt stammt das Amerikanische ab? ## Model description This model is a fine-tuned version of [valhalla/t5-base-qg-hl](https://huggingface.co/valhalla/t5-base-qg-hl) on the [GermanQUAD](https://www.deepset.ai/germanquad) dataset. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ### Evaluation The model achieves a BLEU-4 score of **11.30** on the GermanQuAD test set (n=2204). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
{"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl>britischen Common Laws<hl> sind, setzt sich das amerikanische Recht bedeutend davon ab."}], "model-index": [{"name": "german-qg-t5-quad", "results": []}]}
text2text-generation
dehio/german-qg-t5-quad
[ "transformers", "pytorch", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de" ]
TAGS #transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# german-qg-t5-quad This model is fine-tuned in question generation in German. The expected answer must be highlighted with a &lt;hl> token. ## Task example #### Input generate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...] #### Expected output Von welchem Gesetzt stammt das Amerikanische ab? ## Model description This model is a fine-tuned version of valhalla/t5-base-qg-hl on the GermanQUAD dataset. ## Training and evaluation data The training script can be accessed here. ### Evaluation The model achieves a BLEU-4 score of 11.30 on the GermanQuAD test set (n=2204). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
[ "# german-qg-t5-quad\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with a\n&lt;hl> token.", "## Task example", "#### Input\n\ngenerate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...]", "#### Expected output\n\nVon welchem Gesetzt stammt das Amerikanische ab?", "## Model description\n\nThis model is a fine-tuned version of valhalla/t5-base-qg-hl on the GermanQUAD dataset.", "## Training and evaluation data\n\nThe training script can be accessed here.", "### Evaluation\n\nThe model achieves a BLEU-4 score of 11.30 on the GermanQuAD test set (n=2204).", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10", "### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# german-qg-t5-quad\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with a\n&lt;hl> token.", "## Task example", "#### Input\n\ngenerate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...]", "#### Expected output\n\nVon welchem Gesetzt stammt das Amerikanische ab?", "## Model description\n\nThis model is a fine-tuned version of valhalla/t5-base-qg-hl on the GermanQUAD dataset.", "## Training and evaluation data\n\nThe training script can be accessed here.", "### Evaluation\n\nThe model achieves a BLEU-4 score of 11.30 on the GermanQuAD test set (n=2204).", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10", "### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ 69, 39, 4, 63, 17, 32, 14, 27, 112, 36 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# german-qg-t5-quad\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with a\n&lt;hl> token.## Task example#### Input\n\ngenerate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...]#### Expected output\n\nVon welchem Gesetzt stammt das Amerikanische ab?## Model description\n\nThis model is a fine-tuned version of valhalla/t5-base-qg-hl on the GermanQUAD dataset.## Training and evaluation data\n\nThe training script can be accessed here.### Evaluation\n\nThe model achieves a BLEU-4 score of 11.30 on the GermanQuAD test set (n=2204).### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 100\n- gradient_accumulation_steps: 8\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- num_epochs: 10### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu102\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 - Precision: 0.9251 - Recall: 0.9370 - F1: 0.9310 - Accuracy: 0.9839 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2435 | 1.0 | 878 | 0.0685 | 0.9182 | 0.9221 | 0.9202 | 0.9816 | | 0.0515 | 2.0 | 1756 | 0.0602 | 0.9212 | 0.9368 | 0.9289 | 0.9834 | | 0.0301 | 3.0 | 2634 | 0.0602 | 0.9251 | 0.9370 | 0.9310 | 0.9839 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.925115970841617, "name": "Precision"}, {"type": "recall", "value": 0.9370175634858485, "name": "Recall"}, {"type": "f1", "value": 0.9310287333963209, "name": "F1"}, {"type": "accuracy", "value": 0.9839388692074285, "name": "Accuracy"}]}]}]}
token-classification
delpart/distilbert-base-uncased-finetuned-ner
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-ner ===================================== This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set: * Loss: 0.0602 * Precision: 0.9251 * Recall: 0.9370 * F1: 0.9310 * Accuracy: 0.9839 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### 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 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 69, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs.
{"tags": ["conversational"]}
text-generation
delvan/DialoGPT-medium-DwightV1
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs.
[]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821) , train unsupervised on 570K stroke sentences from : stroke books, quora medical, quora's stroke and human annotates. ### Extract sentence representation ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_simcse") text = "What are disease related to red stroke's causes?" inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)[1] ``` ### Build up embedding for database ``` database = [ 'What is the daily checklist for stroke returning home', 'What are some tips for stroke adapt new life', 'What should I consider when using nursing-home care' ] embedding = torch.zeros((len(database),768)) for i in range(len(database)): inputs = tokenizer(database[i], return_tensors="pt") outputs = model(**inputs)[1] embedding[i] = outputs print(embedding.shape) ``` ### Result On our Poc testset , which contains pairs of matching question related to stroke from human-generated. | Model | Top-1 Accuracy | | ------------- | ------------- | | SimCSE (supervised) | 75.83 | | SimCSE (ours) | 76.66 |
{}
feature-extraction
demdecuong/stroke_simcse
[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2104.08821", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2104.08821" ]
[]
TAGS #transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us
This is finetune version of SimCSE: Simple Contrastive Learning of Sentence Embeddings , train unsupervised on 570K stroke sentences from : stroke books, quora medical, quora's stroke and human annotates. ### Extract sentence representation ### Build up embedding for database ### Result On our Poc testset , which contains pairs of matching question related to stroke from human-generated.
[ "### Extract sentence representation", "### Build up embedding for database", "### Result\n\n\nOn our Poc testset , which contains pairs of matching question related to stroke from human-generated." ]
[ "TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n", "### Extract sentence representation", "### Build up embedding for database", "### Result\n\n\nOn our Poc testset , which contains pairs of matching question related to stroke from human-generated." ]
[ 38, 7, 10, 29 ]
[ "passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n### Extract sentence representation### Build up embedding for database### Result\n\n\nOn our Poc testset , which contains pairs of matching question related to stroke from human-generated." ]
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null
null
transformers
This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821) - Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates. - Positive sentences are generated by paraphrasing and back-translate. - Negative sentences are randomly selected in general domain. ### Extract sentence representation ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_sup_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_sup_simcse") text = "What are disease related to red stroke's causes?" inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)[1] ``` ### Build up embedding for database ``` database = [ 'What is the daily checklist for stroke returning home', 'What are some tips for stroke adapt new life', 'What should I consider when using nursing-home care' ] embedding = torch.zeros((len(database),768)) for i in range(len(database)): inputs = tokenizer(database[i], return_tensors="pt") outputs = model(**inputs)[1] embedding[i] = outputs print(embedding.shape) ``` ### Result On our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation. - SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain - SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain | Model | Top-1 Accuracy | | ------------- | ------------- | | SimCSE supervised (author) | 75.83 | | SimCSE unsupervised (ours) | 76.66 | | SimCSE supervised + 100k (ours) | 73.33 | | SimCSE supervised + 42k (ours) | 75.83 |
{}
feature-extraction
demdecuong/stroke_sup_simcse
[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2104.08821", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2104.08821" ]
[]
TAGS #transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us
This is finetune version of SimCSE: Simple Contrastive Learning of Sentence Embeddings * Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates. * Positive sentences are generated by paraphrasing and back-translate. * Negative sentences are randomly selected in general domain. ### Extract sentence representation ### Build up embedding for database ### Result On our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation. * SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain * SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain
[ "### Extract sentence representation", "### Build up embedding for database", "### Result\n\n\nOn our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation.\n\n\n* SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain\n* SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain" ]
[ "TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n", "### Extract sentence representation", "### Build up embedding for database", "### Result\n\n\nOn our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation.\n\n\n* SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain\n* SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain" ]
[ 38, 7, 10, 90 ]
[ "passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n### Extract sentence representation### Build up embedding for database### Result\n\n\nOn our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation.\n\n\n* SimCSE supervised + 100k : Train on 100K triplet samples contains : medical, stroke and general domain\n* SimCSE supervised + 42k : Train on 42K triplet samples contains : medical, stroke domain" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # iloko_model This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0095 - Wer: 0.0840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.2784 | 1.11 | 100 | 2.9875 | 1.0 | | 2.6899 | 2.22 | 200 | 2.6741 | 1.0 | | 2.6177 | 3.33 | 300 | 2.6516 | 1.0 | | 2.5327 | 4.44 | 400 | 2.4530 | 1.0 | | 0.8653 | 5.56 | 500 | 0.5227 | 0.6547 | | 0.3414 | 6.67 | 600 | 0.1830 | 0.2487 | | 0.2299 | 7.78 | 700 | 0.1212 | 0.1877 | | 0.1739 | 8.89 | 800 | 0.0843 | 0.1441 | | 0.1242 | 10.0 | 900 | 0.0766 | 0.1441 | | 0.1116 | 11.11 | 1000 | 0.0530 | 0.1145 | | 0.0861 | 12.22 | 1100 | 0.0442 | 0.1047 | | 0.1007 | 13.33 | 1200 | 0.0379 | 0.1023 | | 0.0613 | 14.44 | 1300 | 0.0291 | 0.1006 | | 0.0629 | 15.56 | 1400 | 0.0264 | 0.0961 | | 0.047 | 16.67 | 1500 | 0.0238 | 0.0935 | | 0.0797 | 17.78 | 1600 | 0.0226 | 0.0913 | | 0.034 | 18.89 | 1700 | 0.0197 | 0.0893 | | 0.0485 | 20.0 | 1800 | 0.0173 | 0.0905 | | 0.0402 | 21.11 | 1900 | 0.0148 | 0.0902 | | 0.0231 | 22.22 | 2000 | 0.0135 | 0.0891 | | 0.0512 | 23.33 | 2100 | 0.0134 | 0.0861 | | 0.0181 | 24.44 | 2200 | 0.0118 | 0.0842 | | 0.0371 | 25.56 | 2300 | 0.0116 | 0.0867 | | 0.0342 | 26.67 | 2400 | 0.0104 | 0.0863 | | 0.0344 | 27.78 | 2500 | 0.0100 | 0.0850 | | 0.0182 | 28.89 | 2600 | 0.0096 | 0.0839 | | 0.0171 | 30.0 | 2700 | 0.0095 | 0.0840 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "pipeline_tag": "automatic-speech-recognition"}
automatic-speech-recognition
denden/iloko_model
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
iloko\_model ============ This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0095 * Wer: 0.0840 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 30 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu102 * Datasets 1.13.3 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 500\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\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: 0.0003\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: 500\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]
[ 56, 143, 4, 33 ]
[ "passage: 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: 0.0003\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: 500\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]
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null
null
transformers
FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3
{"language": ["en"], "license": "afl-3.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"}
automatic-speech-recognition
denden/new_iloko
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "en", "dataset:timit_asr", "license:afl-3.0", "model-index", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-afl-3.0 #model-index #endpoints_compatible #region-us
FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3
[]
[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-afl-3.0 #model-index #endpoints_compatible #region-us \n" ]
[ 69 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-afl-3.0 #model-index #endpoints_compatible #region-us \n" ]
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null
null
transformers
# BERT-Wiki-Paragraphs Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz Contact us at `<lastname>@informatik.uni-heidelberg.de` Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619). The training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model. Find the associated training data here: [wiki-paragraphs](https://huggingface.co/datasets/dennlinger/wiki-paragraphs) Training is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not. We utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent. We use the same articles as ([Koshorek et al., 2018](https://arxiv.org/abs/1803.09337)), albeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level. ## Usage Preferred usage is through `transformers.pipeline`: ```python from transformers import pipeline pipe = pipeline("text-classification", model="dennlinger/bert-wiki-paragraphs") pipe("{First paragraph} [SEP] {Second paragraph}") ``` A predicted "1" means that paragraphs belong to the same topic, a "0" indicates a disconnect. ## Training Setup The model was trained for 3 epochs from `bert-base-uncased` on paragraph pairs (limited to 512 subwork with the `longest_first` truncation strategy). We use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5. Training was performed on a single Titan RTX GPU over the duration of 3 weeks.
{"language": ["en"], "license": "mit", "tags": ["sentence-similarity", "text-classification"], "datasets": ["dennlinger/wiki-paragraphs"], "metrics": ["f1"]}
text-classification
dennlinger/bert-wiki-paragraphs
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "sentence-similarity", "en", "dataset:dennlinger/wiki-paragraphs", "arxiv:2012.03619", "arxiv:1803.09337", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2012.03619", "1803.09337" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #en #dataset-dennlinger/wiki-paragraphs #arxiv-2012.03619 #arxiv-1803.09337 #license-mit #autotrain_compatible #endpoints_compatible #region-us
# BERT-Wiki-Paragraphs Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz Contact us at '<lastname>@URL' Details for the training method can be found in our work Structural Text Segmentation of Legal Documents. The training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model. Find the associated training data here: wiki-paragraphs Training is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not. We utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent. We use the same articles as (Koshorek et al., 2018), albeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level. ## Usage Preferred usage is through 'transformers.pipeline': A predicted "1" means that paragraphs belong to the same topic, a "0" indicates a disconnect. ## Training Setup The model was trained for 3 epochs from 'bert-base-uncased' on paragraph pairs (limited to 512 subwork with the 'longest_first' truncation strategy). We use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5. Training was performed on a single Titan RTX GPU over the duration of 3 weeks.
[ "# BERT-Wiki-Paragraphs\n\nAuthors: Satya Almasian\\*, Dennis Aumiller\\*, Lucienne-Sophie Marmé, Michael Gertz \nContact us at '<lastname>@URL' \nDetails for the training method can be found in our work Structural Text Segmentation of Legal Documents.\nThe training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model.\nFind the associated training data here: wiki-paragraphs\n\nTraining is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not.\nWe utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent. \nWe use the same articles as (Koshorek et al., 2018), \nalbeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.", "## Usage\nPreferred usage is through 'transformers.pipeline':\n\n\nA predicted \"1\" means that paragraphs belong to the same topic, a \"0\" indicates a disconnect.", "## Training Setup\nThe model was trained for 3 epochs from 'bert-base-uncased' on paragraph pairs (limited to 512 subwork with the 'longest_first' truncation strategy).\nWe use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5.\nTraining was performed on a single Titan RTX GPU over the duration of 3 weeks." ]
[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #en #dataset-dennlinger/wiki-paragraphs #arxiv-2012.03619 #arxiv-1803.09337 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT-Wiki-Paragraphs\n\nAuthors: Satya Almasian\\*, Dennis Aumiller\\*, Lucienne-Sophie Marmé, Michael Gertz \nContact us at '<lastname>@URL' \nDetails for the training method can be found in our work Structural Text Segmentation of Legal Documents.\nThe training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model.\nFind the associated training data here: wiki-paragraphs\n\nTraining is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not.\nWe utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent. \nWe use the same articles as (Koshorek et al., 2018), \nalbeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.", "## Usage\nPreferred usage is through 'transformers.pipeline':\n\n\nA predicted \"1\" means that paragraphs belong to the same topic, a \"0\" indicates a disconnect.", "## Training Setup\nThe model was trained for 3 epochs from 'bert-base-uncased' on paragraph pairs (limited to 512 subwork with the 'longest_first' truncation strategy).\nWe use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5.\nTraining was performed on a single Titan RTX GPU over the duration of 3 weeks." ]
[ 85, 205, 44, 111 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #en #dataset-dennlinger/wiki-paragraphs #arxiv-2012.03619 #arxiv-1803.09337 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# BERT-Wiki-Paragraphs\n\nAuthors: Satya Almasian\\*, Dennis Aumiller\\*, Lucienne-Sophie Marmé, Michael Gertz \nContact us at '<lastname>@URL' \nDetails for the training method can be found in our work Structural Text Segmentation of Legal Documents.\nThe training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model.\nFind the associated training data here: wiki-paragraphs\n\nTraining is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not.\nWe utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent. \nWe use the same articles as (Koshorek et al., 2018), \nalbeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.## Usage\nPreferred usage is through 'transformers.pipeline':\n\n\nA predicted \"1\" means that paragraphs belong to the same topic, a \"0\" indicates a disconnect.## Training Setup\nThe model was trained for 3 epochs from 'bert-base-uncased' on paragraph pairs (limited to 512 subwork with the 'longest_first' truncation strategy).\nWe use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5.\nTraining was performed on a single Titan RTX GPU over the duration of 3 weeks." ]
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null
null
transformers
# About this model: Topical Change Detection in Documents This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper [here](https://github.com/dennlinger/TopicalChange), or read the [paper itself](https://arxiv.org/abs/2012.03619). The weights are based on RoBERTa-base. # Load the model The preferred way is through pipelines ```python from transformers import pipeline pipe = pipeline("text-classification", model="dennlinger/roberta-cls-consec") pipe("{First paragraph} [SEP] {Second paragraph}") ``` # Input Format The model expects two segments that are separated with the `[SEP]` token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on "general" topics, such as news articles or Wikipedia. # Training objective The training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the "coherence" of two segments. If you are experimenting via the Huggingface Model API, the following are interpretations of the `LABEL`s: * `LABEL_0`: Two input segments separated by `[SEP]` do *not* belong to the same topic. * `LABEL_1`: Two input segments separated by `[SEP]` do belong to the same topic. # Performance The results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper. Note that this model is *not* trained to work on classifying single texts, but only works with two (separated) inputs.
{}
text-classification
dennlinger/roberta-cls-consec
[ "transformers", "pytorch", "jax", "safetensors", "roberta", "text-classification", "arxiv:2012.03619", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2012.03619" ]
[]
TAGS #transformers #pytorch #jax #safetensors #roberta #text-classification #arxiv-2012.03619 #autotrain_compatible #endpoints_compatible #region-us
# About this model: Topical Change Detection in Documents This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper here, or read the paper itself. The weights are based on RoBERTa-base. # Load the model The preferred way is through pipelines # Input Format The model expects two segments that are separated with the '[SEP]' token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on "general" topics, such as news articles or Wikipedia. # Training objective The training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the "coherence" of two segments. If you are experimenting via the Huggingface Model API, the following are interpretations of the 'LABEL's: * 'LABEL_0': Two input segments separated by '[SEP]' do *not* belong to the same topic. * 'LABEL_1': Two input segments separated by '[SEP]' do belong to the same topic. # Performance The results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper. Note that this model is *not* trained to work on classifying single texts, but only works with two (separated) inputs.
[ "# About this model: Topical Change Detection in Documents\nThis network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper here, or read the paper itself. The weights are based on RoBERTa-base.", "# Load the model\nThe preferred way is through pipelines", "# Input Format\nThe model expects two segments that are separated with the '[SEP]' token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on \"general\" topics, such as news articles or Wikipedia.", "# Training objective\nThe training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the \"coherence\" of two segments. \nIf you are experimenting via the Huggingface Model API, the following are interpretations of the 'LABEL's:\n* 'LABEL_0': Two input segments separated by '[SEP]' do *not* belong to the same topic.\n* 'LABEL_1': Two input segments separated by '[SEP]' do belong to the same topic.", "# Performance\nThe results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper.\n\nNote that this model is *not* trained to work on classifying single texts, but only works with two (separated) inputs." ]
[ "TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #arxiv-2012.03619 #autotrain_compatible #endpoints_compatible #region-us \n", "# About this model: Topical Change Detection in Documents\nThis network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper here, or read the paper itself. The weights are based on RoBERTa-base.", "# Load the model\nThe preferred way is through pipelines", "# Input Format\nThe model expects two segments that are separated with the '[SEP]' token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on \"general\" topics, such as news articles or Wikipedia.", "# Training objective\nThe training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the \"coherence\" of two segments. \nIf you are experimenting via the Huggingface Model API, the following are interpretations of the 'LABEL's:\n* 'LABEL_0': Two input segments separated by '[SEP]' do *not* belong to the same topic.\n* 'LABEL_1': Two input segments separated by '[SEP]' do belong to the same topic.", "# Performance\nThe results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper.\n\nNote that this model is *not* trained to work on classifying single texts, but only works with two (separated) inputs." ]
[ 54, 97, 13, 84, 147, 78 ]
[ "passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #arxiv-2012.03619 #autotrain_compatible #endpoints_compatible #region-us \n# About this model: Topical Change Detection in Documents\nThis network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper here, or read the paper itself. The weights are based on RoBERTa-base.# Load the model\nThe preferred way is through pipelines# Input Format\nThe model expects two segments that are separated with the '[SEP]' token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on \"general\" topics, such as news articles or Wikipedia.# Training objective\nThe training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the \"coherence\" of two segments. \nIf you are experimenting via the Huggingface Model API, the following are interpretations of the 'LABEL's:\n* 'LABEL_0': Two input segments separated by '[SEP]' do *not* belong to the same topic.\n* 'LABEL_1': Two input segments separated by '[SEP]' do belong to the same topic.# Performance\nThe results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper.\n\nNote that this model is *not* trained to work on classifying single texts, but only works with two (separated) inputs." ]
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null
null
transformers
# Bilingual English + German SQuAD2.0 We created German Squad 2.0 (**deQuAD 2.0**) and merged with [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) into an English and German training data for question answering. The [**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md) is used to fine-tune bilingual QA downstream task. ## Details of deQuAD 2.0 [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) was auto-translated into German. We hired professional editors to proofread the translated transcripts, correct mistakes and double check the answers to further polish the text and enhance annotation quality. The final German deQuAD dataset contains **130k** training and **11k** test samples. ## Overview - **Language model:** bert-base-multilingual-cased - **Language:** German, English - **Training data:** deQuAD2.0 + SQuAD2.0 training set - **Evaluation data:** SQuAD2.0 test set; deQuAD2.0 test set - **Infrastructure:** 8xV100 GPU - **Published**: July 9th, 2021 ## Evaluation on English SQuAD2.0 ``` HasAns_exact = 85.79622132253711 HasAns_f1 = 90.92004586077663 HasAns_total = 5928 NoAns_exact = 94.76871320437343 NoAns_f1 = 94.76871320437343 NoAns_total = 5945 exact = 90.28889076054915 f1 = 92.84713483219753 total = 11873 ``` ## Evaluation on German deQuAD2.0 ``` HasAns_exact = 63.80526406330638 HasAns_f1 = 72.47269140789888 HasAns_total = 5813 NoAns_exact = 82.0291893792861 NoAns_f1 = 82.0291893792861 NoAns_total = 5687 exact = 72.81739130434782 f1 = 77.19858740470603 total = 11500 ``` ## Use Model in Pipeline ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="deutsche-telekom/bert-multi-english-german-squad2", tokenizer="deutsche-telekom/bert-multi-english-german-squad2" ) contexts = ["Die Allianz Arena ist ein Fußballstadion im Norden von München und bietet bei Bundesligaspielen 75.021 Plätze, zusammengesetzt aus 57.343 Sitzplätzen, 13.794 Stehplätzen, 1.374 Logenplätzen, 2.152 Business Seats und 966 Sponsorenplätzen. In der Allianz Arena bestreitet der FC Bayern München seit der Saison 2005/06 seine Heimspiele. Bis zum Saisonende 2017 war die Allianz Arena auch Spielstätte des TSV 1860 München.", "Harvard is a large, highly residential research university. It operates several arts, cultural, and scientific museums, alongside the Harvard Library, which is the world's largest academic and private library system, comprising 79 individual libraries with over 18 million volumes. "] questions = ["Wo befindet sich die Allianz Arena?", "What is the worlds largest academic and private library system?"] qa_pipeline(context=contexts, question=questions) ``` # Output: ```json [{'score': 0.7290093898773193, 'start': 44, 'end': 62, 'answer': 'Norden von München'}, {'score': 0.7979822754859924, 'start': 134, 'end': 149, 'answer': 'Harvard Library'}] ``` ## License - The MIT License Copyright (c) 2021 Fang Xu, Deutsche Telekom AG
{"language": ["de", "en", "multilingual"], "license": "mit", "tags": ["english", "german"]}
question-answering
deutsche-telekom/bert-multi-english-german-squad2
[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "english", "german", "de", "en", "multilingual", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de", "en", "multilingual" ]
TAGS #transformers #pytorch #safetensors #bert #question-answering #english #german #de #en #multilingual #license-mit #endpoints_compatible #has_space #region-us
# Bilingual English + German SQuAD2.0 We created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task. ## Details of deQuAD 2.0 SQuAD2.0 was auto-translated into German. We hired professional editors to proofread the translated transcripts, correct mistakes and double check the answers to further polish the text and enhance annotation quality. The final German deQuAD dataset contains 130k training and 11k test samples. ## Overview - Language model: bert-base-multilingual-cased - Language: German, English - Training data: deQuAD2.0 + SQuAD2.0 training set - Evaluation data: SQuAD2.0 test set; deQuAD2.0 test set - Infrastructure: 8xV100 GPU - Published: July 9th, 2021 ## Evaluation on English SQuAD2.0 ## Evaluation on German deQuAD2.0 ## Use Model in Pipeline # Output: ## License - The MIT License Copyright (c) 2021 Fang Xu, Deutsche Telekom AG
[ "# Bilingual English + German SQuAD2.0\n\nWe created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task.", "## Details of deQuAD 2.0\nSQuAD2.0 was auto-translated into German. We hired professional editors to proofread the translated transcripts, correct mistakes and double check the answers to further polish the text and enhance annotation quality. The final German deQuAD dataset contains 130k training and 11k test samples.", "## Overview\n- Language model: bert-base-multilingual-cased \n- Language: German, English \n- Training data: deQuAD2.0 + SQuAD2.0 training set \n- Evaluation data: SQuAD2.0 test set; deQuAD2.0 test set\n- Infrastructure: 8xV100 GPU \n- Published: July 9th, 2021", "## Evaluation on English SQuAD2.0", "## Evaluation on German deQuAD2.0", "## Use Model in Pipeline", "# Output:", "## License - The MIT License\nCopyright (c) 2021 Fang Xu, Deutsche Telekom AG" ]
[ "TAGS\n#transformers #pytorch #safetensors #bert #question-answering #english #german #de #en #multilingual #license-mit #endpoints_compatible #has_space #region-us \n", "# Bilingual English + German SQuAD2.0\n\nWe created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task.", "## Details of deQuAD 2.0\nSQuAD2.0 was auto-translated into German. We hired professional editors to proofread the translated transcripts, correct mistakes and double check the answers to further polish the text and enhance annotation quality. The final German deQuAD dataset contains 130k training and 11k test samples.", "## Overview\n- Language model: bert-base-multilingual-cased \n- Language: German, English \n- Training data: deQuAD2.0 + SQuAD2.0 training set \n- Evaluation data: SQuAD2.0 test set; deQuAD2.0 test set\n- Infrastructure: 8xV100 GPU \n- Published: July 9th, 2021", "## Evaluation on English SQuAD2.0", "## Evaluation on German deQuAD2.0", "## Use Model in Pipeline", "# Output:", "## License - The MIT License\nCopyright (c) 2021 Fang Xu, Deutsche Telekom AG" ]
[ 57, 70, 79, 74, 9, 9, 6, 4, 18 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #question-answering #english #german #de #en #multilingual #license-mit #endpoints_compatible #has_space #region-us \n# Bilingual English + German SQuAD2.0\n\nWe created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task.## Details of deQuAD 2.0\nSQuAD2.0 was auto-translated into German. We hired professional editors to proofread the translated transcripts, correct mistakes and double check the answers to further polish the text and enhance annotation quality. The final German deQuAD dataset contains 130k training and 11k test samples.## Overview\n- Language model: bert-base-multilingual-cased \n- Language: German, English \n- Training data: deQuAD2.0 + SQuAD2.0 training set \n- Evaluation data: SQuAD2.0 test set; deQuAD2.0 test set\n- Infrastructure: 8xV100 GPU \n- Published: July 9th, 2021## Evaluation on English SQuAD2.0## Evaluation on German deQuAD2.0## Use Model in Pipeline# Output:## License - The MIT License\nCopyright (c) 2021 Fang Xu, Deutsche Telekom AG" ]
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null
null
transformers
We released the German Question Answering model fine-tuned with our own German Question Answering dataset (**deQuAD**) containing **130k** training and **11k** test QA pairs. ## Overview - **Language model:** [electra-base-german-uncased](https://huggingface.co/german-nlp-group/electra-base-german-uncased) - **Language:** German - **Training data:** deQuAD2.0 training set (~42MB) - **Evaluation data:** deQuAD2.0 test set (~4MB) - **Infrastructure:** 8xV100 GPU ## Evaluation We benchmarked the question answering performance on our deQuAD test data with some German language models. The fine-tuned electra-base-german-uncased model gives the best performance (Exact Match/F1). | Model | All | HasAns | NoAns | |-------|--------|--------|--------| | electra-base-german-uncased | 70.97/76.18 | 67.73/78.02 | 74.29/74.29 | | bert-base-german-cased |58.98/64.77| 49.19/60.63| 69.03/69.03| |bert-base-german-dbmdz-uncased|63.70/68.00| 57.03/65.52| 70.51/70.51 | |dbmdz/bert-base-german-europeana-uncased| 58.79/63.38| 52.14/61.22| 65.59/65.59| ## Use Model in Pipeline ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="deutsche-telekom/electra-base-de-squad2", tokenizer="deutsche-telekom/electra-base-de-squad2" ) contexts = ['''Die Robert Bosch GmbH ist ein im Jahr 1886 von Robert Bosch gegründetes multinationales deutsches Unternehmen. Es ist tätig als Automobilzulieferer, Hersteller von Gebrauchsgütern und Industrie- und Gebäudetechnik und darüber hinaus in der automatisierten Verpackungstechnik, wo Bosch den führenden Platz einnimmt. Die Robert Bosch GmbH und ihre rund 460 Tochter- und Regionalgesellschaften in mehr als 60 Ländern bilden die Bosch-Gruppe. Der Sitz der Geschäftsführung befindet sich auf der Schillerhöhe in Gerlingen, der Firmensitz in Stuttgart. Seit dem 1. Juli 2012 ist Volkmar Denner Vorsitzender der Geschäftsführung. Im Jahr 2015 konnte Bosch die Spitzenposition zurückgewinnen. Die Automobilsparte war im Jahr 2018 für 61 % des Konzernumsatzes von Bosch verantwortlich. Das Unternehmen hatte im Jahr 2018 in Deutschland an 85 Standorten 139.400 Mitarbeiter.''']*2 questions = ["Wer leitet die Robert Bosch GmbH?", "Wer begründete die Robert Bosch GmbH?"] qa_pipeline(context=contexts, question=questions) ``` ## Output ```json [{'score': 0.9537325501441956, 'start': 577, 'end': 591, 'answer': 'Volkmar Denner'}, {'score': 0.8804352879524231, 'start': 47, 'end': 59, 'answer': 'Robert Bosch'}] ``` ## License - The MIT License Copyright (c) 2021 Fang Xu, Deutsche Telekom AG
{"language": "de", "license": "mit", "tags": ["german"]}
question-answering
deutsche-telekom/electra-base-de-squad2
[ "transformers", "pytorch", "safetensors", "electra", "question-answering", "german", "de", "license:mit", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de" ]
TAGS #transformers #pytorch #safetensors #electra #question-answering #german #de #license-mit #endpoints_compatible #region-us
We released the German Question Answering model fine-tuned with our own German Question Answering dataset (deQuAD) containing 130k training and 11k test QA pairs. Overview -------- * Language model: electra-base-german-uncased * Language: German * Training data: deQuAD2.0 training set (~42MB) * Evaluation data: deQuAD2.0 test set (~4MB) * Infrastructure: 8xV100 GPU Evaluation ---------- We benchmarked the question answering performance on our deQuAD test data with some German language models. The fine-tuned electra-base-german-uncased model gives the best performance (Exact Match/F1). Use Model in Pipeline --------------------- Output ------ License - The MIT License ------------------------- Copyright (c) 2021 Fang Xu, Deutsche Telekom AG
[]
[ "TAGS\n#transformers #pytorch #safetensors #electra #question-answering #german #de #license-mit #endpoints_compatible #region-us \n" ]
[ 45 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #electra #question-answering #german #de #license-mit #endpoints_compatible #region-us \n" ]
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null
null
transformers
# mT5-small-sum-de-en-v1 This is a bilingual summarization model for English and German. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small). [![One Conversation](https://raw.githubusercontent.com/telekom/HPOflow/main/docs/source/imgs/1c-logo.png)](https://www.welove.ai/) This model is provided by the [One Conversation](https://www.welove.ai/) team of [Deutsche Telekom AG](https://www.telekom.com/). ## Training The training was conducted with the following hyperparameters: - base model: [google/mt5-small](https://huggingface.co/google/mt5-small) - source_prefix: `"summarize: "` - batch size: 3 - max_source_length: 800 - max_target_length: 96 - warmup_ratio: 0.3 - number of train epochs: 10 - gradient accumulation steps: 2 - learning rate: 5e-5 ## Datasets and Preprocessing The datasets were preprocessed as follows: The summary was tokenized with the [google/mt5-small](https://huggingface.co/google/mt5-small) tokenizer. Then only the records with no more than 94 summary tokens were selected. The MLSUM dataset has a special characteristic. In the text, the summary is often included completely as one or more sentences. These have been removed from the texts. The reason is that we do not want to train a model that ultimately extracts only sentences as a summary. This model is trained on the following datasets: | Name | Language | Size | License |------|----------|------|-------- | [CNN Daily - Train](https://github.com/abisee/cnn-dailymail) | en | 218,223 | The license is unclear. The data comes from CNN and Daily Mail. We assume that it may only be used for research purposes and not commercially. | [Extreme Summarization (XSum) - Train](https://github.com/EdinburghNLP/XSum) | en | 204,005 | The license is unclear. The data comes from BBC. We assume that it may only be used for research purposes and not commercially. | [wiki_lingua English](https://github.com/esdurmus/Wikilingua) | en | 130,331 | [Creative Commons CC BY-NC-SA 3.0 License](https://www.wikihow.com/wikiHow:Terms-of-Use) | [wiki_lingua German](https://github.com/esdurmus/Wikilingua) | de | 48,390 | [Creative Commons CC BY-NC-SA 3.0 License](https://www.wikihow.com/wikiHow:Terms-of-Use) | [MLSUM German - Train](https://github.com/ThomasScialom/MLSUM) | de | 218,043 | Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders (see [here](https://github.com/ThomasScialom/MLSUM#mlsum)). | [SwissText 2019 - Train](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html) | de | 84,564 | The license is unclear. The data was published in the [German Text Summarization Challenge](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html). We assume that they may be used for research purposes and not commercially. | Language | Size |------|------ | German | 350,997 | English | 552,559 | Total | 903,556 ## Evaluation on MLSUM German Test Set (no beams) | Model | rouge1 | rouge2 | rougeL | rougeLsum |-------|--------|--------|--------|---------- | [ml6team/mt5-small-german-finetune-mlsum](https://huggingface.co/ml6team/mt5-small-german-finetune-mlsum) | 18.3607 | 5.3604 | 14.5456 | 16.1946 | **deutsche-telekom/mT5-small-sum-de-en-01 (this)** | **21.7336** | **7.2614** | **17.1323** | **19.3977** ## Evaluation on CNN Daily English Test Set (no beams) | Model | rouge1 | rouge2 | rougeL | rougeLsum |-------|--------|--------|--------|---------- | [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) | 26.7664 | 8.8243 | 18.3703 | 23.2614 | [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) | 28.5374 | 9.8565 | 19.4829 | 24.7364 | [mrm8488/t5-base-finetuned-summarize-news](https://huggingface.co/mrm8488/t5-base-finetuned-summarize-news) | 37.576 | 14.7389 | 24.0254 | 34.4634 | **deutsche-telekom/mT5-small-sum-de-en-01 (this)** | **37.6339** | **16.5317** | **27.1418** | **34.9951** ## Evaluation on Extreme Summarization (XSum) English Test Set (no beams) | Model | rouge1 | rouge2 | rougeL | rougeLsum |-------|--------|--------|--------|---------- | [mrm8488/t5-base-finetuned-summarize-news](https://huggingface.co/mrm8488/t5-base-finetuned-summarize-news) | 18.6204 | 3.535 | 12.3997 | 15.2111 | [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) | 28.5374 | 9.8565 | 19.4829 | 24.7364 | deutsche-telekom/mT5-small-sum-de-en-01 (this) | 32.3416 | 10.6191 | 25.3799 | 25.3908 | [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) | 44.2553 &clubs; | 21.4289 &clubs; | 36.2639 &clubs; | 36.2696 &clubs; &clubs;: These values seem to be unusually high. It could be that the test set was used in the training data. ## License Copyright (c) 2021 Philip May, Deutsche Telekom AG This work is licensed under the [Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)](https://creativecommons.org/licenses/by-nc-sa/3.0/) license.
{"language": ["de", "en", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum", "wiki_lingua", "mlsum", "swiss_text_2019"]}
summarization
deutsche-telekom/mt5-small-sum-de-en-v1
[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "summarization", "de", "en", "multilingual", "dataset:cnn_dailymail", "dataset:xsum", "dataset:wiki_lingua", "dataset:mlsum", "dataset:swiss_text_2019", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de", "en", "multilingual" ]
TAGS #transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #en #multilingual #dataset-cnn_dailymail #dataset-xsum #dataset-wiki_lingua #dataset-mlsum #dataset-swiss_text_2019 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
mT5-small-sum-de-en-v1 ====================== This is a bilingual summarization model for English and German. It is based on the multilingual T5 model google/mt5-small. ![One Conversation](URL This model is provided by the One Conversation team of Deutsche Telekom AG. Training -------- The training was conducted with the following hyperparameters: * base model: google/mt5-small * source\_prefix: '"summarize: "' * batch size: 3 * max\_source\_length: 800 * max\_target\_length: 96 * warmup\_ratio: 0.3 * number of train epochs: 10 * gradient accumulation steps: 2 * learning rate: 5e-5 Datasets and Preprocessing -------------------------- The datasets were preprocessed as follows: The summary was tokenized with the google/mt5-small tokenizer. Then only the records with no more than 94 summary tokens were selected. The MLSUM dataset has a special characteristic. In the text, the summary is often included completely as one or more sentences. These have been removed from the texts. The reason is that we do not want to train a model that ultimately extracts only sentences as a summary. This model is trained on the following datasets: Evaluation on MLSUM German Test Set (no beams) ---------------------------------------------- Evaluation on CNN Daily English Test Set (no beams) --------------------------------------------------- Evaluation on Extreme Summarization (XSum) English Test Set (no beams) ---------------------------------------------------------------------- ♣: These values seem to be unusually high. It could be that the test set was used in the training data. License ------- Copyright (c) 2021 Philip May, Deutsche Telekom AG This work is licensed under the Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) license.
[]
[ "TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #en #multilingual #dataset-cnn_dailymail #dataset-xsum #dataset-wiki_lingua #dataset-mlsum #dataset-swiss_text_2019 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 119 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #en #multilingual #dataset-cnn_dailymail #dataset-xsum #dataset-wiki_lingua #dataset-mlsum #dataset-swiss_text_2019 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
# mT5-small-sum-de-mit-v1 This is a German summarization model. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small). The special characteristic of this model is that, unlike many other models, it is licensed under a permissive open source license (MIT). Among other things, this license allows commercial use. [![One Conversation](https://raw.githubusercontent.com/telekom/HPOflow/main/docs/source/imgs/1c-logo.png)](https://www.welove.ai/) This model is provided by the [One Conversation](https://www.welove.ai/) team of [Deutsche Telekom AG](https://www.telekom.com/). ## Training The training was conducted with the following hyperparameters: - base model: [google/mt5-small](https://huggingface.co/google/mt5-small) - source_prefix: `"summarize: "` - batch size: 3 (6) - max_source_length: 800 - max_target_length: 96 - warmup_ratio: 0.3 - number of train epochs: 10 - gradient accumulation steps: 2 - learning rate: 5e-5 ## Datasets and Preprocessing The datasets were preprocessed as follows: The summary was tokenized with the [google/mt5-small](https://huggingface.co/google/mt5-small) tokenizer. Then only the records with no more than 94 summary tokens were selected. This model is trained on the following dataset: | Name | Language | Size | License |------|----------|------|-------- | [SwissText 2019 - Train](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html) | de | 84,564 | Concrete license is unclear. The data was published in the [German Text Summarization Challenge](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html). We have permission to use the Swisstext dataset and release the resulting summarization model under MIT license (see [permission-declaration-swisstext.pdf](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-mit-v1/resolve/main/permission-declaration-swisstext.pdf)). ## Evaluation on MLSUM German Test Set (no beams) | Model | rouge1 | rouge2 | rougeL | rougeLsum |-------|--------|--------|--------|---------- | deutsche-telekom/mt5-small-sum-de-mit-v1 (this) | 16.8023 | 3.5531 | 12.6884 | 14.7624 | [ml6team/mt5-small-german-finetune-mlsum](https://huggingface.co/ml6team/mt5-small-german-finetune-mlsum) | 18.3607 | 5.3604 | 14.5456 | 16.1946 | **[deutsche-telekom/mt5-small-sum-de-en-01](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-en-v1)** | **21.7336** | **7.2614** | **17.1323** | **19.3977** ## License Copyright (c) 2021 Philip May, Deutsche Telekom AG Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License by reviewing the file [LICENSE](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-mit-v1/blob/main/LICENSE) in the repository.
{"language": ["de"], "license": "mit", "tags": ["summarization"], "datasets": ["swiss_text_2019"]}
summarization
deutsche-telekom/mt5-small-sum-de-mit-v1
[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "summarization", "de", "dataset:swiss_text_2019", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "de" ]
TAGS #transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #dataset-swiss_text_2019 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
mT5-small-sum-de-mit-v1 ======================= This is a German summarization model. It is based on the multilingual T5 model google/mt5-small. The special characteristic of this model is that, unlike many other models, it is licensed under a permissive open source license (MIT). Among other things, this license allows commercial use. ![One Conversation](URL This model is provided by the One Conversation team of Deutsche Telekom AG. Training -------- The training was conducted with the following hyperparameters: * base model: google/mt5-small * source\_prefix: '"summarize: "' * batch size: 3 (6) * max\_source\_length: 800 * max\_target\_length: 96 * warmup\_ratio: 0.3 * number of train epochs: 10 * gradient accumulation steps: 2 * learning rate: 5e-5 Datasets and Preprocessing -------------------------- The datasets were preprocessed as follows: The summary was tokenized with the google/mt5-small tokenizer. Then only the records with no more than 94 summary tokens were selected. This model is trained on the following dataset: We have permission to use the Swisstext dataset and release the resulting summarization model under MIT license (see URL). Evaluation on MLSUM German Test Set (no beams) ---------------------------------------------- License ------- Copyright (c) 2021 Philip May, Deutsche Telekom AG Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.
[]
[ "TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #dataset-swiss_text_2019 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 76 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #dataset-swiss_text_2019 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-NER-finetuned-ner This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the x_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.4380 - Precision: 0.2274 - Recall: 0.1119 - F1: 0.1499 - Accuracy: 0.8485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0822 | 1.0 | 878 | 1.1648 | 0.2068 | 0.1101 | 0.1437 | 0.8471 | | 0.0102 | 2.0 | 1756 | 1.2697 | 0.2073 | 0.1110 | 0.1445 | 0.8447 | | 0.0049 | 3.0 | 2634 | 1.3945 | 0.2006 | 0.1073 | 0.1399 | 0.8368 | | 0.0025 | 4.0 | 3512 | 1.3994 | 0.2243 | 0.1126 | 0.1499 | 0.8501 | | 0.0011 | 5.0 | 4390 | 1.4380 | 0.2274 | 0.1119 | 0.1499 | 0.8485 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["x_glue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-NER-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "x_glue", "type": "x_glue", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.2273838630806846, "name": "Precision"}, {"type": "recall", "value": 0.11185727172496743, "name": "Recall"}, {"type": "f1", "value": 0.14994961370507223, "name": "F1"}, {"type": "accuracy", "value": 0.8485324947589099, "name": "Accuracy"}]}]}]}
token-classification
deval/bert-base-NER-finetuned-ner
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:x_glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
bert-base-NER-finetuned-ner =========================== This model is a fine-tuned version of dslim/bert-base-NER on the x\_glue dataset. It achieves the following results on the evaluation set: * Loss: 1.4380 * Precision: 0.2274 * Recall: 0.1119 * F1: 0.1499 * Accuracy: 0.8485 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 65, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the x_glue dataset. It achieves the following results on the evaluation set: - Loss: 2.7979 - Precision: 0.0919 - Recall: 0.1249 - F1: 0.1059 - Accuracy: 0.4927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1773 | 1.0 | 878 | 1.7953 | 0.1025 | 0.1352 | 0.1166 | 0.5058 | | 0.0397 | 2.0 | 1756 | 2.0827 | 0.0906 | 0.1230 | 0.1043 | 0.4888 | | 0.022 | 3.0 | 2634 | 2.8677 | 0.0864 | 0.1260 | 0.1025 | 0.4098 | | 0.0126 | 4.0 | 3512 | 2.8584 | 0.0848 | 0.1201 | 0.0994 | 0.4424 | | 0.0085 | 5.0 | 4390 | 2.7979 | 0.0919 | 0.1249 | 0.1059 | 0.4927 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["x_glue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "x_glue", "type": "x_glue", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.09187560910782316, "name": "Precision"}, {"type": "recall", "value": 0.1248795761078998, "name": "Recall"}, {"type": "f1", "value": 0.10586493798172632, "name": "F1"}, {"type": "accuracy", "value": 0.492660102891609, "name": "Accuracy"}]}]}]}
token-classification
deval/bert-base-uncased-finetuned-ner
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:x_glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
bert-base-uncased-finetuned-ner =============================== This model is a fine-tuned version of bert-base-uncased on the x\_glue dataset. It achieves the following results on the evaluation set: * Loss: 2.7979 * Precision: 0.0919 * Recall: 0.1249 * F1: 0.1059 * Accuracy: 0.4927 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-apache-2.0 #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 68, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-apache-2.0 #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0606 - Precision: 0.9277 - Recall: 0.9385 - F1: 0.9330 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2454 | 1.0 | 878 | 0.0692 | 0.9106 | 0.9212 | 0.9159 | 0.9809 | | 0.0517 | 2.0 | 1756 | 0.0616 | 0.9203 | 0.9352 | 0.9277 | 0.9834 | | 0.0314 | 3.0 | 2634 | 0.0606 | 0.9277 | 0.9385 | 0.9330 | 0.9844 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9276788676324229, "name": "Precision"}, {"type": "recall", "value": 0.9384718648618414, "name": "Recall"}, {"type": "f1", "value": 0.9330441552663775, "name": "F1"}, {"type": "accuracy", "value": 0.9843836878643939, "name": "Accuracy"}]}]}]}
token-classification
deval/distilbert-base-uncased-finetuned-ner
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-ner ===================================== This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set: * Loss: 0.0606 * Precision: 0.9277 * Recall: 0.9385 * F1: 0.9330 * Accuracy: 0.9844 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.0 * Tokenizers 0.10.3
[ "### 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
[ 69, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
# Fintuned Wav2Vec of Timit - 4001 checkpoint
{}
automatic-speech-recognition
devin132/w2v-timit-ft-4001
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
# Fintuned Wav2Vec of Timit - 4001 checkpoint
[ "# Fintuned Wav2Vec of Timit - 4001 checkpoint" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n", "# Fintuned Wav2Vec of Timit - 4001 checkpoint" ]
[ 37, 17 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n# Fintuned Wav2Vec of Timit - 4001 checkpoint" ]
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null
null
transformers
# Dummy Model This be a dummmmmy
{}
fill-mask
devtrent/dummy-model
[ "transformers", "pytorch", "camembert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
# Dummy Model This be a dummmmmy
[ "# Dummy Model\n\nThis be a dummmmmy" ]
[ "TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n", "# Dummy Model\n\nThis be a dummmmmy" ]
[ 38, 10 ]
[ "passage: TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# Dummy Model\n\nThis be a dummmmmy" ]
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null
null
transformers
DistilBERT model trained on OSCAR nepali corpus from huggingface datasets. We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral. However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training. LABEL_1 = negative LABEL_0 = positive
{}
text-classification
dexhrestha/Nepali-DistilBERT
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
DistilBERT model trained on OSCAR nepali corpus from huggingface datasets. We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral. However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training. LABEL_1 = negative LABEL_0 = positive
[]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
#Aerith GPT model
{"tags": ["conversational"]}
text-generation
df4rfrrf/DialoGPT-medium-Aerith
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
#Aerith GPT model
[]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
This the repo for the final project
{}
text-classification
dhairya2303/bert-base-uncased-emotion-AD
[ "transformers", "tf", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
This the repo for the final project
[]
[ "TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 37 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
{'sadness':0,'joy':1,'love':2,'anger':3,'fear':4,'surprise':5}
{}
text-classification
dhairya2303/bert-base-uncased-emotion_holler
[ "transformers", "tf", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
{'sadness':0,'joy':1,'love':2,'anger':3,'fear':4,'surprise':5}
[]
[ "TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 37 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-finetuned-funsd-test This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.18.0 - Tokenizers 0.11.0
{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-funsd-test", "results": []}]}
token-classification
dhanesh123in/layoutlmv2-finetuned-funsd-test
[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# layoutlmv2-finetuned-funsd-test This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.18.0 - Tokenizers 0.11.0
[ "# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown 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: 5e-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- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown 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: 5e-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- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]
[ 65, 45, 6, 12, 8, 3, 104, 4, 37 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown 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: 5e-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- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1000### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]
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null
null
transformers
# AMy San
{"tags": ["conversational"]}
text-generation
dhanushlnaik/amySan
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# AMy San
[ "# AMy San" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# AMy San" ]
[ 51, 4 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# AMy San" ]
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null
null
transformers
"hello"
{}
text-classification
dhikri/question_answering_glue
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
"hello"
[]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# DistilBert Dummy Sentiment Model ## Purpose This is a dummy model that can be used for testing the transformers `pipeline` with the task `sentiment-analysis`. It should always give random results (i.e. `{"label": "negative", "score": 0.5}`). ## How to use ```python classifier = pipeline("sentiment-analysis", "dhpollack/distilbert-dummy-sentiment") results = classifier(["this is a test", "another test"]) ``` ## Notes This was created as follows: 1. Create a vocab.txt file (in /tmp/vocab.txt in this example). ``` [UNK] [SEP] [PAD] [CLS] [MASK] ``` 2. Open a python shell: ```python import transformers config = transformers.DistilBertConfig(vocab_size=5, n_layers=1, n_heads=1, dim=1, hidden_dim=4 * 1, num_labels=2, id2label={0: "negative", 1: "positive"}, label2id={"negative": 0, "positive": 1}) model = transformers.DistilBertForSequenceClassification(config) tokenizer = transformers.DistilBertTokenizer("/tmp/vocab.txt", model_max_length=512) config.save_pretrained(".") model.save_pretrained(".") tokenizer.save_pretrained(".") ```
{"language": ["multilingual", "en"], "tags": ["sentiment-analysis", "testing", "unit tests"]}
text-classification
dhpollack/distilbert-dummy-sentiment
[ "transformers", "pytorch", "distilbert", "text-classification", "sentiment-analysis", "testing", "unit tests", "multilingual", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "multilingual", "en" ]
TAGS #transformers #pytorch #distilbert #text-classification #sentiment-analysis #testing #unit tests #multilingual #en #autotrain_compatible #endpoints_compatible #region-us
# DistilBert Dummy Sentiment Model ## Purpose This is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{"label": "negative", "score": 0.5}'). ## How to use ## Notes This was created as follows: 1. Create a URL file (in /tmp/URL in this example). 2. Open a python shell:
[ "# DistilBert Dummy Sentiment Model", "## Purpose\n\nThis is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{\"label\": \"negative\", \"score\": 0.5}').", "## How to use", "## Notes\n\nThis was created as follows:\n\n1. Create a URL file (in /tmp/URL in this example).\n\n\n\n2. Open a python shell:" ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #sentiment-analysis #testing #unit tests #multilingual #en #autotrain_compatible #endpoints_compatible #region-us \n", "# DistilBert Dummy Sentiment Model", "## Purpose\n\nThis is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{\"label\": \"negative\", \"score\": 0.5}').", "## How to use", "## Notes\n\nThis was created as follows:\n\n1. Create a URL file (in /tmp/URL in this example).\n\n\n\n2. Open a python shell:" ]
[ 57, 10, 64, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #sentiment-analysis #testing #unit tests #multilingual #en #autotrain_compatible #endpoints_compatible #region-us \n# DistilBert Dummy Sentiment Model## Purpose\n\nThis is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{\"label\": \"negative\", \"score\": 0.5}').## How to use## Notes\n\nThis was created as follows:\n\n1. Create a URL file (in /tmp/URL in this example).\n\n\n\n2. Open a python shell:" ]
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null
null
transformers
### TUNiB-Electra Stereotype Detector Finetuned TUNiB-Electra base with K-StereoSet. Original Code: https://github.com/newfull5/Stereotype-Detector
{}
text-classification
dhtocks/tunib-electra-stereotype-classifier
[ "transformers", "pytorch", "electra", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
### TUNiB-Electra Stereotype Detector Finetuned TUNiB-Electra base with K-StereoSet. Original Code: URL
[ "### TUNiB-Electra Stereotype Detector\n\nFinetuned TUNiB-Electra base with K-StereoSet.\n\nOriginal Code: URL" ]
[ "TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### TUNiB-Electra Stereotype Detector\n\nFinetuned TUNiB-Electra base with K-StereoSet.\n\nOriginal Code: URL" ]
[ 37, 39 ]
[ "passage: TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### TUNiB-Electra Stereotype Detector\n\nFinetuned TUNiB-Electra base with K-StereoSet.\n\nOriginal Code: URL" ]
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null
null
transformers
Language Model 2 For Language agnostic Dense Passage Retrieval
{}
feature-extraction
diarsabri/LaDPR-context-encoder
[ "transformers", "pytorch", "dpr", "feature-extraction", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us
Language Model 2 For Language agnostic Dense Passage Retrieval
[]
[ "TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n" ]
[ 30 ]
[ "passage: TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n" ]
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null
null
transformers
Language Model 1 For Language agnostic Dense Passage Retrieval
{}
feature-extraction
diarsabri/LaDPR-query-encoder
[ "transformers", "pytorch", "dpr", "feature-extraction", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us
Language Model 1 For Language agnostic Dense Passage Retrieval
[]
[ "TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n" ]
[ 30 ]
[ "passage: TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Wav2Vec2-Large-XLSR-53 --- language: gl datasets: - OpenSLR 77 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Galician Wav2Vec2-Large-XLSR-53 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR type: openslr args: gl metrics: - name: Test WER type: wer value: 16.79 --- Wav2Vec2-Large-XLSR-53-galician Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on galician using the [OpenSLR](https://huggingface.co/datasets/common_voice) dataset When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "gl", split="test[:2%]") # This is not available yet, load OpenSLR or your dataset instead processor = Wav2Vec2Processor.from_pretrained("diego-fustes/wav2vec2-large-xlsr-gl") model = Wav2Vec2ForCTC.from_pretrained("diego-fustes/wav2vec2-large-xlsr-gl") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Galician test data of Common Voice (when it is released). ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "gl", split="test") # This is not available yet, load OpenSLR or your dataset instead wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("diego-fustes/wav2vec2-large-xlsr-gl") model = Wav2Vec2ForCTC.from_pretrained("diego-fustes/wav2vec2-large-xlsr-gl") model.to("cuda") chars_to_ignore_regex = '[^a-záéíóúñ ]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 16.79 % on OpenSLR split ## Training The OpenSLR [SLR77](https://openslr.org/77/) dataset was used for training and validation. The dataset was split as 70% for training, 15% for validation and 15% for testing The script used for training can be found [here](https://github.com/diego-fustes/xlsr-fine-tuning-gl)
{}
automatic-speech-recognition
diego-fustes/wav2vec2-large-xlsr-gl
[ "transformers", "pytorch", "jax", "safetensors", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
# Wav2Vec2-Large-XLSR-53 --- language: gl datasets: - OpenSLR 77 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Galician Wav2Vec2-Large-XLSR-53 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR type: openslr args: gl metrics: - name: Test WER type: wer value: 16.79 --- Wav2Vec2-Large-XLSR-53-galician Fine-tuned facebook/wav2vec2-large-xlsr-53 on galician using the OpenSLR dataset When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on the Galician test data of Common Voice (when it is released). Test Result: 16.79 % on OpenSLR split ## Training The OpenSLR SLR77 dataset was used for training and validation. The dataset was split as 70% for training, 15% for validation and 15% for testing The script used for training can be found here
[ "# Wav2Vec2-Large-XLSR-53\n\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache-2.0\nmodel-index:\n- name: Galician Wav2Vec2-Large-XLSR-53\n results:\n - task: \n name: Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: OpenSLR\n type: openslr\n args: gl\n metrics:\n - name: Test WER\n type: wer\n value: 16.79\n---\n\nWav2Vec2-Large-XLSR-53-galician\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on galician using the OpenSLR dataset\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Galician test data of Common Voice (when it is released).\n\n\n\nTest Result: 16.79 % on OpenSLR split", "## Training\n\nThe OpenSLR SLR77 dataset was used for training and validation. The dataset was split as 70% for training, 15% for validation and 15% for testing \n\nThe script used for training can be found here" ]
[ "TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53\n\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache-2.0\nmodel-index:\n- name: Galician Wav2Vec2-Large-XLSR-53\n results:\n - task: \n name: Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: OpenSLR\n type: openslr\n args: gl\n metrics:\n - name: Test WER\n type: wer\n value: 16.79\n---\n\nWav2Vec2-Large-XLSR-53-galician\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on galician using the OpenSLR dataset\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Galician test data of Common Voice (when it is released).\n\n\n\nTest Result: 16.79 % on OpenSLR split", "## Training\n\nThe OpenSLR SLR77 dataset was used for training and validation. The dataset was split as 70% for training, 15% for validation and 15% for testing \n\nThe script used for training can be found here" ]
[ 45, 205, 20, 39, 46 ]
[ "passage: TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53\n\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache-2.0\nmodel-index:\n- name: Galician Wav2Vec2-Large-XLSR-53\n results:\n - task: \n name: Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: OpenSLR\n type: openslr\n args: gl\n metrics:\n - name: Test WER\n type: wer\n value: 16.79\n---\n\nWav2Vec2-Large-XLSR-53-galician\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on galician using the OpenSLR dataset\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Galician test data of Common Voice (when it is released).\n\n\n\nTest Result: 16.79 % on OpenSLR split## Training\n\nThe OpenSLR SLR77 dataset was used for training and validation. The dataset was split as 70% for training, 15% for validation and 15% for testing \n\nThe script used for training can be found here" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": []}]}
text2text-generation
diegor2/t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetu-truncated-d22eed
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16_en_ro_pre_processed", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
[ "# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ 75, 84, 6, 12, 8, 3, 103, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset. It achieves the following results on the evaluation set: - Loss: 6.4897 - Bleu: 0.0002 - Gen Len: 9.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 6.2585 | 1.0 | 76290 | 6.4897 | 0.0002 | 9.0 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0002, "name": "Bleu"}]}]}]}
text2text-generation
diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetu-truncated-41f800
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16_en_ro_pre_processed", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5-tiny-random-length-96-learning\_rate-2e-05-weight\_decay-0.005-finetuned-en-to-ro-TRAIN\_EPOCHS-1 ==================================================================================================== This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset. It achieves the following results on the evaluation set: * Loss: 6.4897 * Bleu: 0.0002 * Gen Len: 9.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.5 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
[ 79, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": []}]}
text2text-generation
diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16_en_ro_pre_processed", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
[ "# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
[ 75, 84, 6, 12, 8, 3, 103, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed 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: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]
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null
null
transformers
# Twitter4SSE This model maps texts to 768 dimensional dense embeddings that encode semantic similarity. It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. It was initialized from [BERTweet](https://huggingface.co/vinai/bertweet-base) and trained with [Sentence-transformers](https://www.sbert.net/). ## Usage The model is easier to use with sentence-trainsformers library ``` pip install -U sentence-transformers ``` ``` from sentence_transformers import SentenceTransformer sentences = ["This is the first tweet", "This is the second tweet"] model = SentenceTransformer('digio/Twitter4SSE') embeddings = model.encode(sentences) print(embeddings) ``` Without sentence-transfomer library, please refer to [this repository](https://huggingface.co/sentence-transformers) for detailed instructions on how to use Sentence Transformers on Huggingface. ## Citing & Authors The official paper [Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings](https://arxiv.org/abs/2110.02030) will be presented at EMNLP 2021. Further details will be available soon. ``` @inproceedings{di-giovanni-brambilla-2021-exploiting, title = "Exploiting {T}witter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings", author = "Di Giovanni, Marco and Brambilla, Marco", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.780", pages = "9902--9910", } ``` The official code is available on [GitHub](https://github.com/marco-digio/Twitter4SSE)
{"language": ["en"], "license": "apache-2.0", "tags": ["Pytorch", "Sentence Transformers", "Transformers"], "pipeline_tag": "sentence-similarity"}
sentence-similarity
digio/Twitter4SSE
[ "transformers", "pytorch", "roberta", "feature-extraction", "Pytorch", "Sentence Transformers", "Transformers", "sentence-similarity", "en", "arxiv:2110.02030", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2110.02030" ]
[ "en" ]
TAGS #transformers #pytorch #roberta #feature-extraction #Pytorch #Sentence Transformers #Transformers #sentence-similarity #en #arxiv-2110.02030 #license-apache-2.0 #endpoints_compatible #region-us
# Twitter4SSE This model maps texts to 768 dimensional dense embeddings that encode semantic similarity. It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. It was initialized from BERTweet and trained with Sentence-transformers. ## Usage The model is easier to use with sentence-trainsformers library Without sentence-transfomer library, please refer to this repository for detailed instructions on how to use Sentence Transformers on Huggingface. ## Citing & Authors The official paper Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings will be presented at EMNLP 2021. Further details will be available soon. The official code is available on GitHub
[ "# Twitter4SSE\n\nThis model maps texts to 768 dimensional dense embeddings that encode semantic similarity. \nIt was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. \nIt was initialized from BERTweet and trained with Sentence-transformers.", "## Usage\n\nThe model is easier to use with sentence-trainsformers library\n\n\n\n\n\n\nWithout sentence-transfomer library, please refer to this repository for detailed instructions on how to use Sentence Transformers on Huggingface.", "## Citing & Authors\n\nThe official paper Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings will be presented at EMNLP 2021. Further details will be available soon. \n\n\n\nThe official code is available on GitHub" ]
[ "TAGS\n#transformers #pytorch #roberta #feature-extraction #Pytorch #Sentence Transformers #Transformers #sentence-similarity #en #arxiv-2110.02030 #license-apache-2.0 #endpoints_compatible #region-us \n", "# Twitter4SSE\n\nThis model maps texts to 768 dimensional dense embeddings that encode semantic similarity. \nIt was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. \nIt was initialized from BERTweet and trained with Sentence-transformers.", "## Usage\n\nThe model is easier to use with sentence-trainsformers library\n\n\n\n\n\n\nWithout sentence-transfomer library, please refer to this repository for detailed instructions on how to use Sentence Transformers on Huggingface.", "## Citing & Authors\n\nThe official paper Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings will be presented at EMNLP 2021. Further details will be available soon. \n\n\n\nThe official code is available on GitHub" ]
[ 72, 70, 51, 61 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #Pytorch #Sentence Transformers #Transformers #sentence-similarity #en #arxiv-2110.02030 #license-apache-2.0 #endpoints_compatible #region-us \n# Twitter4SSE\n\nThis model maps texts to 768 dimensional dense embeddings that encode semantic similarity. \nIt was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. \nIt was initialized from BERTweet and trained with Sentence-transformers.## Usage\n\nThe model is easier to use with sentence-trainsformers library\n\n\n\n\n\n\nWithout sentence-transfomer library, please refer to this repository for detailed instructions on how to use Sentence Transformers on Huggingface.## Citing & Authors\n\nThe official paper Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings will be presented at EMNLP 2021. Further details will be available soon. \n\n\n\nThe official code is available on GitHub" ]
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null
null
transformers
# COVID-Twitter-BERT v2 MNLI ## Model description This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data. The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161). The article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The model is already finetuned on 400'000 generaic logical tasks. We can then use it as a zero-shot classifier by reformulating the classification task as a question. Let's say we want to classify COVID-tweets as vaccine-related and not vaccine-related. The typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes. Then you would finetune the model on this. With the zero-shot mnli-classifier, you can instead reformulate your question as "This text is about vaccines", and use this directly on inference - without any training. Find more info about the model on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert). ## Usage Please note that how you formulate the question can give slightly different results. Collecting a training set and finetuning on this, will most likely give you better accuracy. The easiest way to try this out is by using the Hugging Face pipeline. This uses the default Enlish template where it puts the text "This example is " in front of the text. ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="digitalepidemiologylab/covid-twitter-bert-v2-mnli") ``` You can then use this pipeline to classify sequences into any of the class names you specify. ```python sequence_to_classify = 'To stop the pandemic it is important that everyone turns up for their shots.' candidate_labels = ['health', 'sport', 'vaccine','guns'] hypothesis_template = 'This example is {}.' classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True) ``` ## Training procedure The model is finetuned on the 400k large [MNLI-task](https://cims.nyu.edu/~sbowman/multinli/). ## References ```bibtex @article{muller2020covid, title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter}, author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E}, journal={arXiv preprint arXiv:2005.07503}, year={2020} } ``` or ``` Martin Müller, Marcel Salathé, and Per E. Kummervold. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. arXiv preprint arXiv:2005.07503 (2020). ```
{"language": ["en"], "license": "mit", "tags": ["Twitter", "COVID-19", "text-classification", "pytorch", "tensorflow", "bert"], "datasets": ["mnli"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png", "pipeline_tag": "zero-shot-classification", "widget": [{"text": "To stop the pandemic it is important that everyone turns up for their shots.", "candidate_labels": "health, sport, vaccine, guns"}]}
zero-shot-classification
digitalepidemiologylab/covid-twitter-bert-v2-mnli
[ "transformers", "pytorch", "jax", "bert", "text-classification", "Twitter", "COVID-19", "tensorflow", "zero-shot-classification", "en", "dataset:mnli", "arxiv:1909.00161", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1909.00161" ]
[ "en" ]
TAGS #transformers #pytorch #jax #bert #text-classification #Twitter #COVID-19 #tensorflow #zero-shot-classification #en #dataset-mnli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us
# COVID-Twitter-BERT v2 MNLI ## Model description This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data. The technique is based on Yin et al.. The article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The model is already finetuned on 400'000 generaic logical tasks. We can then use it as a zero-shot classifier by reformulating the classification task as a question. Let's say we want to classify COVID-tweets as vaccine-related and not vaccine-related. The typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes. Then you would finetune the model on this. With the zero-shot mnli-classifier, you can instead reformulate your question as "This text is about vaccines", and use this directly on inference - without any training. Find more info about the model on our GitHub page. ## Usage Please note that how you formulate the question can give slightly different results. Collecting a training set and finetuning on this, will most likely give you better accuracy. The easiest way to try this out is by using the Hugging Face pipeline. This uses the default Enlish template where it puts the text "This example is " in front of the text. You can then use this pipeline to classify sequences into any of the class names you specify. ## Training procedure The model is finetuned on the 400k large MNLI-task. ## References or
[ "# COVID-Twitter-BERT v2 MNLI", "## Model description\nThis model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.\n\nThe technique is based on Yin et al..\nThe article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.\nThe model is already finetuned on 400'000 generaic logical tasks.\nWe can then use it as a zero-shot classifier by reformulating the classification task as a question.\n\nLet's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.\nThe typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.\nThen you would finetune the model on this.\n\nWith the zero-shot mnli-classifier, you can instead reformulate your question as \"This text is about vaccines\", and use this directly on inference - without any training.\n\nFind more info about the model on our GitHub page.", "## Usage\nPlease note that how you formulate the question can give slightly different results.\nCollecting a training set and finetuning on this, will most likely give you better accuracy.\n\nThe easiest way to try this out is by using the Hugging Face pipeline.\nThis uses the default Enlish template where it puts the text \"This example is \" in front of the text.\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.", "## Training procedure\nThe model is finetuned on the 400k large MNLI-task.", "## References\n\nor" ]
[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #Twitter #COVID-19 #tensorflow #zero-shot-classification #en #dataset-mnli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# COVID-Twitter-BERT v2 MNLI", "## Model description\nThis model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.\n\nThe technique is based on Yin et al..\nThe article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.\nThe model is already finetuned on 400'000 generaic logical tasks.\nWe can then use it as a zero-shot classifier by reformulating the classification task as a question.\n\nLet's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.\nThe typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.\nThen you would finetune the model on this.\n\nWith the zero-shot mnli-classifier, you can instead reformulate your question as \"This text is about vaccines\", and use this directly on inference - without any training.\n\nFind more info about the model on our GitHub page.", "## Usage\nPlease note that how you formulate the question can give slightly different results.\nCollecting a training set and finetuning on this, will most likely give you better accuracy.\n\nThe easiest way to try this out is by using the Hugging Face pipeline.\nThis uses the default Enlish template where it puts the text \"This example is \" in front of the text.\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.", "## Training procedure\nThe model is finetuned on the 400k large MNLI-task.", "## References\n\nor" ]
[ 77, 13, 239, 107, 21, 4 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #Twitter #COVID-19 #tensorflow #zero-shot-classification #en #dataset-mnli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# COVID-Twitter-BERT v2 MNLI## Model description\nThis model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.\n\nThe technique is based on Yin et al..\nThe article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.\nThe model is already finetuned on 400'000 generaic logical tasks.\nWe can then use it as a zero-shot classifier by reformulating the classification task as a question.\n\nLet's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.\nThe typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.\nThen you would finetune the model on this.\n\nWith the zero-shot mnli-classifier, you can instead reformulate your question as \"This text is about vaccines\", and use this directly on inference - without any training.\n\nFind more info about the model on our GitHub page.## Usage\nPlease note that how you formulate the question can give slightly different results.\nCollecting a training set and finetuning on this, will most likely give you better accuracy.\n\nThe easiest way to try this out is by using the Hugging Face pipeline.\nThis uses the default Enlish template where it puts the text \"This example is \" in front of the text.\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.## Training procedure\nThe model is finetuned on the 400k large MNLI-task.## References\n\nor" ]
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null
null
transformers
# COVID-Twitter-BERT v2 ## Model description BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to [covid-twitter-bert](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert) - but trained on more data, resulting in higher downstream performance. Find more info on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert). ## Intended uses & limitations The model can e.g. be used in the `fill-mask` task (see below). You can also use the model without the MLM/NSP heads and train a classifier with it. #### How to use ```python from transformers import pipeline import json pipe = pipeline(task='fill-mask', model='digitalepidemiologylab/covid-twitter-bert-v2') out = pipe(f"In places with a lot of people, it's a good idea to wear a {pipe.tokenizer.mask_token}") print(json.dumps(out, indent=4)) [ { "sequence": "[CLS] in places with a lot of people, it's a good idea to wear a mask [SEP]", "score": 0.9998226761817932, "token": 7308, "token_str": "mask" }, ... ] ``` ## Training procedure This model was trained on 97M unique tweets (1.2B training examples) collected between January 12 and July 5, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training. ## Eval results The model was evaluated based on downstream Twitter text classification tasks from previous SemEval challenges. ### BibTeX entry and citation info ```bibtex @article{muller2020covid, title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter}, author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E}, journal={arXiv preprint arXiv:2005.07503}, year={2020} } ``` or ```Martin Müller, Marcel Salathé, and Per E. Kummervold. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. arXiv preprint arXiv:2005.07503 (2020). ```
{"language": "en", "license": "mit", "tags": ["Twitter", "COVID-19"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"}
null
digitalepidemiologylab/covid-twitter-bert-v2
[ "transformers", "pytorch", "tf", "jax", "bert", "Twitter", "COVID-19", "en", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #has_space #region-us
# COVID-Twitter-BERT v2 ## Model description BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance. Find more info on our GitHub page. ## Intended uses & limitations The model can e.g. be used in the 'fill-mask' task (see below). You can also use the model without the MLM/NSP heads and train a classifier with it. #### How to use ## Training procedure This model was trained on 97M unique tweets (1.2B training examples) collected between January 12 and July 5, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training. ## Eval results The model was evaluated based on downstream Twitter text classification tasks from previous SemEval challenges. ### BibTeX entry and citation info or
[ "# COVID-Twitter-BERT v2", "## Model description\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance.\n\nFind more info on our GitHub page.", "## Intended uses & limitations\n\nThe model can e.g. be used in the 'fill-mask' task (see below). You can also use the model without the MLM/NSP heads and train a classifier with it.", "#### How to use", "## Training procedure\nThis model was trained on 97M unique tweets (1.2B training examples) collected between January 12 and July 5, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.", "## Eval results\nThe model was evaluated based on downstream Twitter text classification tasks from previous SemEval challenges.", "### BibTeX entry and citation info\n\n\n\nor" ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #has_space #region-us \n", "# COVID-Twitter-BERT v2", "## Model description\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance.\n\nFind more info on our GitHub page.", "## Intended uses & limitations\n\nThe model can e.g. be used in the 'fill-mask' task (see below). You can also use the model without the MLM/NSP heads and train a classifier with it.", "#### How to use", "## Training procedure\nThis model was trained on 97M unique tweets (1.2B training examples) collected between January 12 and July 5, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.", "## Eval results\nThe model was evaluated based on downstream Twitter text classification tasks from previous SemEval challenges.", "### BibTeX entry and citation info\n\n\n\nor" ]
[ 46, 10, 67, 54, 5, 107, 26, 12 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #has_space #region-us \n# COVID-Twitter-BERT v2## Model description\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance.\n\nFind more info on our GitHub page.## Intended uses & limitations\n\nThe model can e.g. be used in the 'fill-mask' task (see below). You can also use the model without the MLM/NSP heads and train a classifier with it.#### How to use## Training procedure\nThis model was trained on 97M unique tweets (1.2B training examples) collected between January 12 and July 5, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.## Eval results\nThe model was evaluated based on downstream Twitter text classification tasks from previous SemEval challenges.### BibTeX entry and citation info\n\n\n\nor" ]
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null
null
transformers
# COVID-Twitter-BERT (CT-BERT) v1 :warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) which was trained on more recent data and yields better performance_ :warning: BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert). ## Overview This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training. This model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. In order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects [GitHub repo](https://github.com/digitalepidemiologylab/covid-twitter-bert). ## Example usage ```python tokenizer = AutoTokenizer.from_pretrained("digitalepidemiologylab/covid-twitter-bert") model = AutoModel.from_pretrained("digitalepidemiologylab/covid-twitter-bert") ``` You can also use the model with the `pipeline` interface: ```python from transformers import pipeline import json pipe = pipeline(task='fill-mask', model='digitalepidemiologylab/covid-twitter-bert-v2') out = pipe(f"In places with a lot of people, it's a good idea to wear a {pipe.tokenizer.mask_token}") print(json.dumps(out, indent=4)) [ { "sequence": "[CLS] in places with a lot of people, it's a good idea to wear a mask [SEP]", "score": 0.9959408044815063, "token": 7308, "token_str": "mask" }, ... ] ``` ## References [1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).
{"language": "en", "license": "mit", "tags": ["Twitter", "COVID-19"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"}
null
digitalepidemiologylab/covid-twitter-bert
[ "transformers", "pytorch", "tf", "jax", "bert", "Twitter", "COVID-19", "en", "license:mit", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #region-us
# COVID-Twitter-BERT (CT-BERT) v1 :warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page. ## Overview This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training. This model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. In order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects GitHub repo. ## Example usage You can also use the model with the 'pipeline' interface: ## References [1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).
[ "# COVID-Twitter-BERT (CT-BERT) v1\n\n:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: \n\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page.", "## Overview\nThis model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.\n\nThis model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. \n\nIn order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects GitHub repo.", "## Example usage\n\n\nYou can also use the model with the 'pipeline' interface:", "## References\n[1] Martin Müller, Marcel Salaté, Per E Kummervold. \"COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter\" arXiv preprint arXiv:2005.07503 (2020)." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #region-us \n", "# COVID-Twitter-BERT (CT-BERT) v1\n\n:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: \n\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page.", "## Overview\nThis model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.\n\nThis model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. \n\nIn order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects GitHub repo.", "## Example usage\n\n\nYou can also use the model with the 'pipeline' interface:", "## References\n[1] Martin Müller, Marcel Salaté, Per E Kummervold. \"COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter\" arXiv preprint arXiv:2005.07503 (2020)." ]
[ 42, 85, 189, 19, 59 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #region-us \n# COVID-Twitter-BERT (CT-BERT) v1\n\n:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: \n\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page.## Overview\nThis model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords \"wuhan\", \"ncov\", \"coronavirus\", \"covid\", or \"sars-cov-2\". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.\n\nThis model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. \n\nIn order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects GitHub repo.## Example usage\n\n\nYou can also use the model with the 'pipeline' interface:## References\n[1] Martin Müller, Marcel Salaté, Per E Kummervold. \"COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter\" arXiv preprint arXiv:2005.07503 (2020)." ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-AdventureTime This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 279 | 3.3451 | | 3.4534 | 2.0 | 558 | 3.2941 | | 3.4534 | 3.0 | 837 | 3.2740 | | 3.2435 | 4.0 | 1116 | 3.2617 | | 3.2435 | 5.0 | 1395 | 3.2556 | | 3.1729 | 6.0 | 1674 | 3.2490 | | 3.1729 | 7.0 | 1953 | 3.2475 | | 3.1262 | 8.0 | 2232 | 3.2467 | | 3.0972 | 9.0 | 2511 | 3.2448 | | 3.0972 | 10.0 | 2790 | 3.2450 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-AT", "results": []}]}
text-generation
pyordii/distilgpt2-finetuned-AT
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
distilgpt2-finetuned-AdventureTime ================================== This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2450 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.13.0 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3
[ "### 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: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\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: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
[ 66, 98, 4, 33 ]
[ "passage: 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: 10### Training results### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021] Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia About: Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. Previous studies have shown that domain-specific fine-tuning or retraining of models before attempting to solve downstream tasks can lead to excellent results in multiple domains. Fine-tuning/retraining a complex models to identify offensive language has not been substantially explored before and we address this gap by proposing fBERT, a bert-base-uncased model that has been learned using over 1.4 million offensive instances from the SOLID dataset. The shifted fBERT model better incorporates domain-specific offensive language and social media features. The fBERT model achieves better results in both OffensEval and HatEval tasks and in the HS & O dataset over BERT and HateBERT.
{}
fill-mask
diptanu/fBERT
[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021] Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia About: Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. Previous studies have shown that domain-specific fine-tuning or retraining of models before attempting to solve downstream tasks can lead to excellent results in multiple domains. Fine-tuning/retraining a complex models to identify offensive language has not been substantially explored before and we address this gap by proposing fBERT, a bert-base-uncased model that has been learned using over 1.4 million offensive instances from the SOLID dataset. The shifted fBERT model better incorporates domain-specific offensive language and social media features. The fBERT model achieves better results in both OffensEval and HatEval tasks and in the HS & O dataset over BERT and HateBERT.
[]
[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 41 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Moe DialoGPT Model
{"tags": ["conversational"]}
text-generation
disdamoe/DialoGPT-small-moe
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Moe DialoGPT Model
[ "# Moe DialoGPT Model" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Moe DialoGPT Model" ]
[ 51, 8 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Moe DialoGPT Model" ]
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null
null
transformers
# Moe DialoGPT Model
{"tags": ["conversational"]}
text-generation
disdamoe/TheGreatManipulator
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Moe DialoGPT Model
[ "# Moe DialoGPT Model" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Moe DialoGPT Model" ]
[ 51, 8 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Moe DialoGPT Model" ]
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null
null
transformers
# The Manipulator
{"tags": ["conversational"]}
text-generation
disdamoe/TheManipulator
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# The Manipulator
[ "# The Manipulator" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# The Manipulator" ]
[ 51, 4 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# The Manipulator" ]
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<a href="https://www.geogebra.org/m/w8uzjttg">.</a> <a href="https://www.geogebra.org/m/gvn7m78g">.</a> <a href="https://www.geogebra.org/m/arxecanq">.</a> <a href="https://www.geogebra.org/m/xb69bvww">.</a> <a href="https://www.geogebra.org/m/apvepfnd">.</a> <a href="https://www.geogebra.org/m/evmj8ckk">.</a> <a href="https://www.geogebra.org/m/qxcxwmhp">.</a> <a href="https://www.geogebra.org/m/p3cxqh6c">.</a> <a href="https://www.geogebra.org/m/ggrahbgd">.</a> <a href="https://www.geogebra.org/m/pnhymrbc">.</a> <a href="https://www.geogebra.org/m/zjukbtk9">.</a> <a href="https://www.geogebra.org/m/bbezun8r">.</a> <a href="https://www.geogebra.org/m/sgwamtru">.</a> <a href="https://www.geogebra.org/m/fpunkxxp">.</a> <a href="https://www.geogebra.org/m/acxebrr7">.</a> <a href="https://jobs.acm.org/jobs/watch-godzilla-vs-kong-2021-full-1818658-cd">.</a> <a href="https://jobs.acm.org/jobs/123movies-watch-godzilla-vs-kong-online-2021-full-f-r-e-e-1818655-cd">.</a> <a 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href="https://sites.google.com/view/watch-mortal-kombat-2021-f-u-l/">.</a> <a href="https://sites.google.com/view/mortalkombat2/">.</a> <a href="https://sites.google.com/view/mortalkombat3/">.</a> <a href="https://sites.google.com/view/mortalkombat5/">.</a> <a href="https://sites.google.com/view/fullwatchmortalkombat2021-movi/">.</a> <a href="https://sites.google.com/view/mortalkombat7/">.</a> <a href="https://sites.google.com/view/mortalkombat8/">.</a> <a href="https://sites.google.com/view/mortalkombat9/">.</a> <a href="https://sites.google.com/view/mortalkombat10/">.</a> <a href="https://sites.google.com/view/watch-mort-tal-kombat/">.</a> <a href="https://sites.google.com/view/free-watch-mort-tal-kombat/">.</a> <a href="https://sites.google.com/view/watch-mort-tal-kombatfree-/">.</a> <a href="https://sites.google.com/view/full-watch-mortal-kombat/">.</a> <a href="https://sites.google.com/view/watch-mortal-kombat-2021-/">.</a> <a href="https://sites.google.com/view/watch-free-mortal-kombat-2021/">.</a> <a href="https://sites.google.com/view/full-watch-mortal-kombat-/">.</a> <a href="https://sites.google.com/view/watch-mortal-kombat-g-drive/">.</a> <a href="https://sites.google.com/view/g-docs-mortalkombat-g-drive/">.</a> <a href="https://sites.google.com/view/mortal-kombat-2021-full-free/">.</a> <a href="https://sites.google.com/view/mortal-kombat-2021-full-free-o/">.</a> <a href="https://sites.google.com/view/mortal-kombat-2021-full-free-o/">.</a> <a href="https://paiza.io/projects/56xFAEq61pSSn8VnKnHO6Q">.</a> <a href="https://www.posts123.com/post/1450667/mariners-announce-spring-training">.</a> <a href="https://sites.google.com/view/sfdjgkdfghdkfgjherghkkdfjg/home">.</a> <a href="https://dskfjshdkjfewhgf.blogspot.com/2021/03/sdkjfhwekjhfjdherjgfdjg.html">.</a> <a href="https://grahmaulidia.wordpress.com/2021/03/28/mariners-announce-spring-training-roster-moves/">.</a> <a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner-f83a9ea92f89">.</a> <a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner1-b2847091ff9f">.</a> <a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner2-df35041eec3a">.</a> <a href="https://4z5v6wq7a.medium.com">.</a> <a href="https://onlinegdb.com/BJaH8WR4O">.</a>
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dispenst/hgfytgfg
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2022-03-02T23:29:05+00:00
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null
null
transformers
We took `facebook/wav2vec2-large-960h` and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transcription with `facebook/wav2vec2-large-960h` to correct many mistakes in the youtube transcriptions. We can probably achieve better results with more data clean up. On our data we achieved a WER of 13.1%. `facebook/wav2vec2-large-960h` only reached a WER of 27% on our data. ## Usage ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import soundfile as sf import torch # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency") model = Wav2Vec2ForCTC.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency" filename = "INSERT_FILENAME" audio, sampling_rate = sf.read(filename) input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=sampling_rate).input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) tokenizer.batch_decode(predicted_ids ```
{"language": "en", "license": "mit", "tags": ["audio", "automatic-speech-recognition"], "metrics": ["wer"]}
automatic-speech-recognition
distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "en", "license:mit", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #license-mit #endpoints_compatible #region-us
We took 'facebook/wav2vec2-large-960h' and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transcription with 'facebook/wav2vec2-large-960h' to correct many mistakes in the youtube transcriptions. We can probably achieve better results with more data clean up. On our data we achieved a WER of 13.1%. 'facebook/wav2vec2-large-960h' only reached a WER of 27% on our data. ## Usage
[ "## Usage" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #license-mit #endpoints_compatible #region-us \n", "## Usage" ]
[ 47, 3 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #license-mit #endpoints_compatible #region-us \n## Usage" ]
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null
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# Peter from Your Boyfriend Game.
{"tags": ["conversational"]}
text-generation
divi/Peterbot
[ "conversational", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #conversational #region-us
# Peter from Your Boyfriend Game.
[ "# Peter from Your Boyfriend Game." ]
[ "TAGS\n#conversational #region-us \n", "# Peter from Your Boyfriend Game." ]
[ 10, 8 ]
[ "passage: TAGS\n#conversational #region-us \n# Peter from Your Boyfriend Game." ]
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null
null
transformers
# diwank/dyda-deberta-pair Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the [daily-dialog dataset](https://huggingface.co/datasets/daily_dialog) ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)* ## Usage ```python from simpletransformers.classification import ( ClassificationModel, ClassificationArgs ) model = ClassificationModel("deberta", "diwank/dyda-deberta-pair") convert_to_label = lambda n: ["__dummy__ (0), inform (1), question (2), directive (3), commissive (4)".split(', ')[i] for i in n] predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]]) convert_to_label(predictions) # inform (1) ```
{"license": "mit"}
text-classification
diwank/dyda-deberta-pair
[ "transformers", "pytorch", "tf", "deberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
# diwank/dyda-deberta-pair Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the daily-dialog dataset ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)* ## Usage
[ "# diwank/dyda-deberta-pair\r\n\r\nDeberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the daily-dialog dataset ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)*", "## Usage" ]
[ "TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# diwank/dyda-deberta-pair\r\n\r\nDeberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the daily-dialog dataset ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)*", "## Usage" ]
[ 46, 115, 3 ]
[ "passage: TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# diwank/dyda-deberta-pair\r\n\r\nDeberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the daily-dialog dataset ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)*## Usage" ]
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null
null
transformers
# maptask-deberta-pair Deberta-based Daily MapTask style dialog-act annotations classification model ## Example ```python from simpletransformers.classification import ( ClassificationModel, ClassificationArgs ) model = ClassificationModel("deberta", "diwank/maptask-deberta-pair") predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]]) convert_to_label = lambda n: ["acknowledge (0), align (1), check (2), clarify (3), explain (4), instruct (5), query_w (6), query_yn (7), ready (8), reply_n (9), reply_w (10), reply_y (11)".split(', ')[i] for i in n] convert_to_label(predictions) # reply_n (9) ```
{"license": "mit"}
text-classification
diwank/maptask-deberta-pair
[ "transformers", "pytorch", "tf", "deberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
# maptask-deberta-pair Deberta-based Daily MapTask style dialog-act annotations classification model ## Example
[ "# maptask-deberta-pair\r\nDeberta-based Daily MapTask style dialog-act annotations classification model", "## Example" ]
[ "TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# maptask-deberta-pair\r\nDeberta-based Daily MapTask style dialog-act annotations classification model", "## Example" ]
[ 46, 28, 3 ]
[ "passage: TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# maptask-deberta-pair\r\nDeberta-based Daily MapTask style dialog-act annotations classification model## Example" ]
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null
null
transformers
# diwank/silicone-deberta-pair `deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) collection. Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. **Outputs one of 11 labels**: ```python (0, 'acknowledge') (1, 'answer') (2, 'backchannel') (3, 'reply_yes') (4, 'exclaim') (5, 'say') (6, 'reply_no') (7, 'hold') (8, 'ask') (9, 'intent') (10, 'ask_yes_no') ``` ## Example: ```python from simpletransformers.classification import ( ClassificationModel, ClassificationArgs ) model = ClassificationModel("deberta", "diwank/silicone-deberta-pair") convert_to_label = lambda n: [ ['acknowledge', 'answer', 'backchannel', 'reply_yes', 'exclaim', 'say', 'reply_no', 'hold', 'ask', 'intent', 'ask_yes_no' ][i] for i in n ] predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]]) convert_to_label(predictions) # answer ``` ## Report from W&B https://wandb.ai/diwank/da-silicone-combined/reports/silicone-deberta-pair--VmlldzoxNTczNjE5?accessToken=yj1jz4c365z0y5b3olgzye7qgsl7qv9lxvqhmfhtb6300hql6veqa5xiq1skn8ys
{"license": "mit"}
text-classification
diwank/silicone-deberta-pair
[ "transformers", "pytorch", "tf", "deberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
# diwank/silicone-deberta-pair 'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels: ## Example: ## Report from W&B URL
[ "# diwank/silicone-deberta-pair\r\n\r\n'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. \r\n\r\nTakes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels:", "## Example:", "## Report from W&B\r\n\r\nURL" ]
[ "TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# diwank/silicone-deberta-pair\r\n\r\n'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. \r\n\r\nTakes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels:", "## Example:", "## Report from W&B\r\n\r\nURL" ]
[ 46, 117, 4, 7 ]
[ "passage: TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# diwank/silicone-deberta-pair\r\n\r\n'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. \r\n\r\nTakes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels:## Example:## Report from W&B\r\n\r\nURL" ]
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null
null
transformers
Slavic BERT from https://github.com/deepmipt/Slavic-BERT-NER http://files.deeppavlov.ai/deeppavlov_data/bg_cs_pl_ru_cased_L-12_H-768_A-12.tar.gz
{}
null
djstrong/bg_cs_pl_ru_cased_L-12_H-768_A-12
[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #endpoints_compatible #region-us
Slavic BERT from URL URL
[]
[ "TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n" ]
[ 21 ]
[ "passage: TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Harry Potter DialoGPT Model
{"tags": ["conversational"]}
text-generation
dk16gaming/DialoGPT-small-HarryPotter
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Harry Potter DialoGPT Model
[ "# Harry Potter DialoGPT Model" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Harry Potter DialoGPT Model" ]
[ 51, 8 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model" ]
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null
null
transformers
### Bert-News
{}
text-classification
dkhara/bert-news
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
### Bert-News
[ "### Bert-News" ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Bert-News" ]
[ 38, 5 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Bert-News" ]
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transformers
# Polbert - Polish BERT Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. ![PolBERT image](https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png) ## Cased and uncased variants * I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model: * Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly. * I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus. * I didn't use Whole Word Masking. * The cased model improves on the uncased model in the following ways: * All Polish characters and accents should now be tokenized correctly. * I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. * The model is trained with Whole Word Masking. ## Pre-training corpora Below is the list of corpora used along with the output of `wc` command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. ### Uncased | Tables | Lines | Words | Characters | | ------------- |--------------:| -----:| -----:| | [Polish subset of Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 236635408| 1431199601 | 7628097730 | | [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 | | [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 | | [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 | | Total | 262920423 | 1861093257 | 10746349159 | ### Cased | Tables | Lines | Words | Characters | | ------------- |--------------:| -----:| -----:| | [Polish subset of Open Subtitles (Deduplicated) ](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 41998942| 213590656 | 1424873235 | | [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 | | [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 | | [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 | | Total | 68283960 | 646479197 | 4543124667 | ## Pre-training details ### Uncased * Polbert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert) * Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters) * Training set-up: in total 1 million training steps: * 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup) * 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5 * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 * The model was trained on a single Google Cloud TPU v3-8 ### Cased * Same approach as uncased model, with the following differences: * Whole Word Masking * Training set-up: * 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup) * 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5 * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 ## Usage Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/). For an example use as language model, see [this notebook](/LM_testing.ipynb) file. ### Uncased ```python from transformers import * model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-uncased-v1") tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-uncased-v1") nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer) for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."): print(pred) # Output: # {'sequence': '[CLS] adam mickiewicz wielkim polskim poeta był. [SEP]', 'score': 0.47196975350379944, 'token': 26596} # {'sequence': '[CLS] adam mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.09127858281135559, 'token': 10953} # {'sequence': '[CLS] adam mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.0647173821926117, 'token': 5182} # {'sequence': '[CLS] adam mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.05232388526201248, 'token': 24293} # {'sequence': '[CLS] adam mickiewicz wielkim polskim politykiem był. [SEP]', 'score': 0.04554257541894913, 'token': 44095} ``` ### Cased ```python model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-cased-v1") tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-cased-v1") nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer) for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."): print(pred) # Output: # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.5391148328781128, 'token': 37120} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.11683262139558792, 'token': 6810} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.06021466106176376, 'token': 17709} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim mistrzem był. [SEP]', 'score': 0.051870670169591904, 'token': 14652} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim artystą był. [SEP]', 'score': 0.031787533313035965, 'token': 35680} ``` See the next section for an example usage of Polbert in downstream tasks. ## Evaluation Thanks to Allegro, we now have the [KLEJ benchmark](https://klejbenchmark.com/leaderboard/), a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert. | Model | Average | NKJP-NER | CDSC-E | CDSC-R | CBD | PolEmo2.0-IN | PolEmo2.0-OUT | DYK | PSC | AR | | ------------- |--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:| | Polbert cased | 81.7 | 93.6 | 93.4 | 93.8 | 52.7 | 87.4 | 71.1 | 59.1 | 98.6 | 85.2 | | Polbert uncased | 81.4 | 90.1 | 93.9 | 93.5 | 55.0 | 88.1 | 68.8 | 59.4 | 98.8 | 85.4 | Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. ## Bias The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them. ## Acknowledgements * I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you! * Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model. * Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. * Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses! ## Author Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21) ## References * https://github.com/google-research/bert * https://github.com/narusemotoki/srx_segmenter * SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: https://raw.githubusercontent.com/languagetool-org/languagetool/master/languagetool-core/src/main/resources/org/languagetool/resource/segment.srx * [KLEJ benchmark](https://klejbenchmark.com/leaderboard/)
{"language": "pl", "thumbnail": "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png"}
null
dkleczek/bert-base-polish-cased-v1
[ "transformers", "pytorch", "jax", "bert", "pretraining", "pl", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "pl" ]
TAGS #transformers #pytorch #jax #bert #pretraining #pl #endpoints_compatible #has_space #region-us
Polbert - Polish BERT ===================== Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. !PolBERT image Cased and uncased variants -------------------------- * I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model: + Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly. + I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus. + I didn't use Whole Word Masking. * The cased model improves on the uncased model in the following ways: + All Polish characters and accents should now be tokenized correctly. + I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. + The model is trained with Whole Word Masking. Pre-training corpora -------------------- Below is the list of corpora used along with the output of 'wc' command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. ### Uncased ### Cased Pre-training details -------------------- ### Uncased * Polbert was trained with code provided in Google BERT's github repository (URL * Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters) * Training set-up: in total 1 million training steps: + 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup) + 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5 + 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 * The model was trained on a single Google Cloud TPU v3-8 ### Cased * Same approach as uncased model, with the following differences: + Whole Word Masking * Training set-up: + 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup) + 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5 + 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 Usage ----- Polbert is released via HuggingFace Transformers library. For an example use as language model, see this notebook file. ### Uncased ### Cased See the next section for an example usage of Polbert in downstream tasks. Evaluation ---------- Thanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert. Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. Bias ---- The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them. Acknowledgements ---------------- * I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you! * Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model. * Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. * Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses! Author ------ Darek Kłeczek - contact me on Twitter @dk21 References ---------- * URL * URL * SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL * KLEJ benchmark
[ "### Uncased", "### Cased\n\n\n\nPre-training details\n--------------------", "### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8", "### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.", "### Uncased", "### Cased\n\n\nSee the next section for an example usage of Polbert in downstream tasks.\n\n\nEvaluation\n----------\n\n\nThanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.\n\n\n\nNote how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here.\n\n\nBias\n----\n\n\nThe data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.\n\n\nAcknowledgements\n----------------\n\n\n* I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you!\n* Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model.\n* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization.\n* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses!\n\n\nAuthor\n------\n\n\nDarek Kłeczek - contact me on Twitter @dk21\n\n\nReferences\n----------\n\n\n* URL\n* URL\n* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL\n* KLEJ benchmark" ]
[ "TAGS\n#transformers #pytorch #jax #bert #pretraining #pl #endpoints_compatible #has_space #region-us \n", "### Uncased", "### Cased\n\n\n\nPre-training details\n--------------------", "### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8", "### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.", "### Uncased", "### Cased\n\n\nSee the next section for an example usage of Polbert in downstream tasks.\n\n\nEvaluation\n----------\n\n\nThanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.\n\n\n\nNote how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here.\n\n\nBias\n----\n\n\nThe data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.\n\n\nAcknowledgements\n----------------\n\n\n* I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you!\n* Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model.\n* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization.\n* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses!\n\n\nAuthor\n------\n\n\nDarek Kłeczek - contact me on Twitter @dk21\n\n\nReferences\n----------\n\n\n* URL\n* URL\n* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL\n* KLEJ benchmark" ]
[ 35, 5, 10, 160, 131, 5, 397 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #pretraining #pl #endpoints_compatible #has_space #region-us \n### Uncased### Cased\n\n\n\nPre-training details\n--------------------### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.### Uncased" ]
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transformers
# Polbert - Polish BERT Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. ![PolBERT image](https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png) ## Cased and uncased variants * I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model: * Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly. * I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus. * I didn't use Whole Word Masking. * The cased model improves on the uncased model in the following ways: * All Polish characters and accents should now be tokenized correctly. * I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. * The model is trained with Whole Word Masking. ## Pre-training corpora Below is the list of corpora used along with the output of `wc` command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. ### Uncased | Tables | Lines | Words | Characters | | ------------- |--------------:| -----:| -----:| | [Polish subset of Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 236635408| 1431199601 | 7628097730 | | [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 | | [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 | | [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 | | Total | 262920423 | 1861093257 | 10746349159 | ### Cased | Tables | Lines | Words | Characters | | ------------- |--------------:| -----:| -----:| | [Polish subset of Open Subtitles (Deduplicated) ](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 41998942| 213590656 | 1424873235 | | [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 | | [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 | | [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 | | Total | 68283960 | 646479197 | 4543124667 | ## Pre-training details ### Uncased * Polbert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert) * Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters) * Training set-up: in total 1 million training steps: * 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup) * 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5 * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 * The model was trained on a single Google Cloud TPU v3-8 ### Cased * Same approach as uncased model, with the following differences: * Whole Word Masking * Training set-up: * 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup) * 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5 * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 ## Usage Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/). For an example use as language model, see [this notebook](/LM_testing.ipynb) file. ### Uncased ```python from transformers import * model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-uncased-v1") tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-uncased-v1") nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer) for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."): print(pred) # Output: # {'sequence': '[CLS] adam mickiewicz wielkim polskim poeta był. [SEP]', 'score': 0.47196975350379944, 'token': 26596} # {'sequence': '[CLS] adam mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.09127858281135559, 'token': 10953} # {'sequence': '[CLS] adam mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.0647173821926117, 'token': 5182} # {'sequence': '[CLS] adam mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.05232388526201248, 'token': 24293} # {'sequence': '[CLS] adam mickiewicz wielkim polskim politykiem był. [SEP]', 'score': 0.04554257541894913, 'token': 44095} ``` ### Cased ```python model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-cased-v1") tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-cased-v1") nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer) for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."): print(pred) # Output: # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.5391148328781128, 'token': 37120} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.11683262139558792, 'token': 6810} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.06021466106176376, 'token': 17709} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim mistrzem był. [SEP]', 'score': 0.051870670169591904, 'token': 14652} # {'sequence': '[CLS] Adam Mickiewicz wielkim polskim artystą był. [SEP]', 'score': 0.031787533313035965, 'token': 35680} ``` See the next section for an example usage of Polbert in downstream tasks. ## Evaluation Thanks to Allegro, we now have the [KLEJ benchmark](https://klejbenchmark.com/leaderboard/), a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert. | Model | Average | NKJP-NER | CDSC-E | CDSC-R | CBD | PolEmo2.0-IN | PolEmo2.0-OUT | DYK | PSC | AR | | ------------- |--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:| | Polbert cased | 81.7 | 93.6 | 93.4 | 93.8 | 52.7 | 87.4 | 71.1 | 59.1 | 98.6 | 85.2 | | Polbert uncased | 81.4 | 90.1 | 93.9 | 93.5 | 55.0 | 88.1 | 68.8 | 59.4 | 98.8 | 85.4 | Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. ## Bias The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them. ## Acknowledgements * I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you! * Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model. * Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. * Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses! ## Author Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21) ## References * https://github.com/google-research/bert * https://github.com/narusemotoki/srx_segmenter * SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: https://raw.githubusercontent.com/languagetool-org/languagetool/master/languagetool-core/src/main/resources/org/languagetool/resource/segment.srx * [KLEJ benchmark](https://klejbenchmark.com/leaderboard/)
{"language": "pl", "thumbnail": "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png"}
fill-mask
dkleczek/bert-base-polish-uncased-v1
[ "transformers", "pytorch", "jax", "bert", "fill-mask", "pl", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "pl" ]
TAGS #transformers #pytorch #jax #bert #fill-mask #pl #autotrain_compatible #endpoints_compatible #has_space #region-us
Polbert - Polish BERT ===================== Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. !PolBERT image Cased and uncased variants -------------------------- * I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model: + Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly. + I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus. + I didn't use Whole Word Masking. * The cased model improves on the uncased model in the following ways: + All Polish characters and accents should now be tokenized correctly. + I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. + The model is trained with Whole Word Masking. Pre-training corpora -------------------- Below is the list of corpora used along with the output of 'wc' command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. ### Uncased ### Cased Pre-training details -------------------- ### Uncased * Polbert was trained with code provided in Google BERT's github repository (URL * Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters) * Training set-up: in total 1 million training steps: + 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup) + 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5 + 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 * The model was trained on a single Google Cloud TPU v3-8 ### Cased * Same approach as uncased model, with the following differences: + Whole Word Masking * Training set-up: + 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup) + 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5 + 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5 Usage ----- Polbert is released via HuggingFace Transformers library. For an example use as language model, see this notebook file. ### Uncased ### Cased See the next section for an example usage of Polbert in downstream tasks. Evaluation ---------- Thanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert. Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. Bias ---- The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them. Acknowledgements ---------------- * I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you! * Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model. * Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. * Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses! Author ------ Darek Kłeczek - contact me on Twitter @dk21 References ---------- * URL * URL * SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL * KLEJ benchmark
[ "### Uncased", "### Cased\n\n\n\nPre-training details\n--------------------", "### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8", "### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.", "### Uncased", "### Cased\n\n\nSee the next section for an example usage of Polbert in downstream tasks.\n\n\nEvaluation\n----------\n\n\nThanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.\n\n\n\nNote how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here.\n\n\nBias\n----\n\n\nThe data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.\n\n\nAcknowledgements\n----------------\n\n\n* I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you!\n* Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model.\n* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization.\n* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses!\n\n\nAuthor\n------\n\n\nDarek Kłeczek - contact me on Twitter @dk21\n\n\nReferences\n----------\n\n\n* URL\n* URL\n* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL\n* KLEJ benchmark" ]
[ "TAGS\n#transformers #pytorch #jax #bert #fill-mask #pl #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Uncased", "### Cased\n\n\n\nPre-training details\n--------------------", "### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8", "### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.", "### Uncased", "### Cased\n\n\nSee the next section for an example usage of Polbert in downstream tasks.\n\n\nEvaluation\n----------\n\n\nThanks to Allegro, we now have the KLEJ benchmark, a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.\n\n\n\nNote how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here.\n\n\nBias\n----\n\n\nThe data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.\n\n\nAcknowledgements\n----------------\n\n\n* I'd like to express my gratitude to Google TensorFlow Research Cloud (TFRC) for providing the free TPU credits - thank you!\n* Also appreciate the help from Timo Möller from deepset for sharing tips and scripts based on their experience training German BERT model.\n* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization.\n* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from fastai for their NLP and Deep Learning courses!\n\n\nAuthor\n------\n\n\nDarek Kłeczek - contact me on Twitter @dk21\n\n\nReferences\n----------\n\n\n* URL\n* URL\n* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: URL\n* KLEJ benchmark" ]
[ 45, 5, 10, 160, 131, 5, 397 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #pl #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Uncased### Cased\n\n\n\nPre-training details\n--------------------### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training set-up: in total 1 million training steps:\n\t+ 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)\n\t+ 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n* The model was trained on a single Google Cloud TPU v3-8### Cased\n\n\n* Same approach as uncased model, with the following differences:\n\t+ Whole Word Masking\n* Training set-up:\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)\n\t+ 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5\n\t+ 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5\n\n\nUsage\n-----\n\n\nPolbert is released via HuggingFace Transformers library.\n\n\nFor an example use as language model, see this notebook file.### Uncased" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # papuGaPT2-finetuned-wierszyki This model is a fine-tuned version of [flax-community/papuGaPT2](https://huggingface.co/flax-community/papuGaPT2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8122 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 202 | 2.8122 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "model-index": [{"name": "papuGaPT2-finetuned-wierszyki", "results": []}]}
text-generation
dkleczek/papuGaPT2-finetuned-wierszyki
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
papuGaPT2-finetuned-wierszyki ============================= This model is a fine-tuned version of flax-community/papuGaPT2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 2.8122 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.9.0+cu111 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #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: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 58, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #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: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
# papuGaPT2 - Polish GPT2 language model [GPT2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research. Our model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens). ## Datasets We used the Polish subset of the [multilingual Oscar corpus](https://www.aclweb.org/anthology/2020.acl-main.156) to train the model in a self-supervised fashion. ``` from datasets import load_dataset dataset = load_dataset('oscar', 'unshuffled_deduplicated_pl') ``` ## Intended uses & limitations The raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research. ## Bias Analysis There are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in [this notebook](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_bias_analysis.ipynb). ### Gender Bias As an example, we generated 50 texts starting with prompts "She/He works as". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress. ![gender bias](https://huggingface.co/flax-community/papuGaPT2/raw/main/gender_bias.jpeg) ### Ethnicity/Nationality/Gender Bias We generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme: * Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts ("He/She"). * Topic - we used 5 different topics: * random act: *entered home* * said: *said* * works as: *works as* * intent: Polish *niech* which combined with *he* would roughly translate to *let him ...* * define: *is* Each combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts. We used a model trained on [Polish Hate Speech corpus](https://huggingface.co/datasets/hate_speech_pl) to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector. The following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline. ![hate score by ethnicity](https://huggingface.co/flax-community/papuGaPT2/raw/main/hate_by_ethnicity.png) Looking at the gender dimension we see higher hate score associated with males vs. females. ![hate score by gender](https://huggingface.co/flax-community/papuGaPT2/raw/main/hate_by_gender.png) We don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided. ## Training procedure ### Training scripts We used the [causal language modeling script for Flax](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_clm_flax.py). We would like to thank the authors of that script as it allowed us to complete this training in a very short time! ### Preprocessing and Training Details The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens. We have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state: 1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68 2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55 3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79 ## Evaluation results We trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in: * Evaluation loss: 3.082 * Perplexity: 21.79 ## How to use You can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples [here](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_text_generation.ipynb) including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations. ### Text generation Let's first start with the text-generation pipeline. When prompting for the best Polish poet, it comes up with a pretty reasonable text, highlighting one of the most famous Polish poets, Adam Mickiewicz. ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='flax-community/papuGaPT2') set_seed(42) generator('Największym polskim poetą był') >>> [{'generated_text': 'Największym polskim poetą był Adam Mickiewicz - uważany za jednego z dwóch geniuszów języka polskiego. "Pan Tadeusz" był jednym z najpopularniejszych dzieł w historii Polski. W 1801 został wystawiony publicznie w Teatrze Wilama Horzycy. Pod jego'}] ``` The pipeline uses `model.generate()` method in the background. In [our notebook](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_text_generation.ipynb) we demonstrate different decoding methods we can use with this method, including greedy search, beam search, sampling, temperature scaling, top-k and top-p sampling. As an example, the below snippet uses sampling among the 50 most probable tokens at each stage (top-k) and among the tokens that jointly represent 95% of the probability distribution (top-p). It also returns 3 output sequences. ```python from transformers import AutoTokenizer, AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained('flax-community/papuGaPT2') tokenizer = AutoTokenizer.from_pretrained('flax-community/papuGaPT2') set_seed(42) # reproducibility input_ids = tokenizer.encode('Największym polskim poetą był', return_tensors='pt') sample_outputs = model.generate( input_ids, do_sample=True, max_length=50, top_k=50, top_p=0.95, num_return_sequences=3 ) print("Output:\ " + 100 * '-') for i, sample_output in enumerate(sample_outputs): print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True))) >>> Output: >>> ---------------------------------------------------------------------------------------------------- >>> 0: Największym polskim poetą był Roman Ingarden. Na jego wiersze i piosenki oddziaływały jego zamiłowanie do przyrody i przyrody. Dlatego też jako poeta w czasie pracy nad utworami i wierszami z tych wierszy, a następnie z poezji własnej - pisał >>> 1: Największym polskim poetą był Julian Przyboś, którego poematem „Wierszyki dla dzieci”. >>> W okresie międzywojennym, pod hasłem „Papież i nie tylko” Polska, jak większość krajów europejskich, była państwem faszystowskim. >>> Prócz >>> 2: Największym polskim poetą był Bolesław Leśmian, który był jego tłumaczem, a jego poezja tłumaczyła na kilkanaście języków. >>> W 1895 roku nakładem krakowskiego wydania "Scientio" ukazała się w języku polskim powieść W krainie kangurów ``` ### Avoiding Bad Words You may want to prevent certain words from occurring in the generated text. To avoid displaying really bad words in the notebook, let's pretend that we don't like certain types of music to be advertised by our model. The prompt says: *my favorite type of music is*. ```python input_ids = tokenizer.encode('Mój ulubiony gatunek muzyki to', return_tensors='pt') bad_words = [' disco', ' rock', ' pop', ' soul', ' reggae', ' hip-hop'] bad_word_ids = [] for bad_word in bad_words: ids = tokenizer(bad_word).input_ids bad_word_ids.append(ids) sample_outputs = model.generate( input_ids, do_sample=True, max_length=20, top_k=50, top_p=0.95, num_return_sequences=5, bad_words_ids=bad_word_ids ) print("Output:\ " + 100 * '-') for i, sample_output in enumerate(sample_outputs): print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True))) >>> Output: >>> ---------------------------------------------------------------------------------------------------- >>> 0: Mój ulubiony gatunek muzyki to muzyka klasyczna. Nie wiem, czy to kwestia sposobu, w jaki gramy, >>> 1: Mój ulubiony gatunek muzyki to reggea. Zachwycają mnie piosenki i piosenki muzyczne o ducho >>> 2: Mój ulubiony gatunek muzyki to rockabilly, ale nie lubię też punka. Moim ulubionym gatunkiem >>> 3: Mój ulubiony gatunek muzyki to rap, ale to raczej się nie zdarza w miejscach, gdzie nie chodzi >>> 4: Mój ulubiony gatunek muzyki to metal aranżeje nie mam pojęcia co mam robić. Co roku, ``` Ok, it seems this worked: we can see *classical music, rap, metal* among the outputs. Interestingly, *reggae* found a way through via a misspelling *reggea*. Take it as a caution to be careful with curating your bad word lists! ### Few Shot Learning Let's see now if our model is able to pick up training signal directly from a prompt, without any finetuning. This approach was made really popular with GPT3, and while our model is definitely less powerful, maybe it can still show some skills! If you'd like to explore this topic in more depth, check out [the following article](https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api) which we used as reference. ```python prompt = """Tekst: "Nienawidzę smerfów!" Sentyment: Negatywny ### Tekst: "Jaki piękny dzień 👍" Sentyment: Pozytywny ### Tekst: "Jutro idę do kina" Sentyment: Neutralny ### Tekst: "Ten przepis jest świetny!" Sentyment:""" res = generator(prompt, max_length=85, temperature=0.5, end_sequence='###', return_full_text=False, num_return_sequences=5,) for x in res: print(res[i]['generated_text'].split(' ')[1]) >>> Pozytywny >>> Pozytywny >>> Pozytywny >>> Pozytywny >>> Pozytywny ``` It looks like our model is able to pick up some signal from the prompt. Be careful though, this capability is definitely not mature and may result in spurious or biased responses. ### Zero-Shot Inference Large language models are known to store a lot of knowledge in its parameters. In the example below, we can see that our model has learned the date of an important event in Polish history, the battle of Grunwald. ```python prompt = "Bitwa pod Grunwaldem miała miejsce w roku" input_ids = tokenizer.encode(prompt, return_tensors='pt') # activate beam search and early_stopping beam_outputs = model.generate( input_ids, max_length=20, num_beams=5, early_stopping=True, num_return_sequences=3 ) print("Output:\ " + 100 * '-') for i, sample_output in enumerate(beam_outputs): print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True))) >>> Output: >>> ---------------------------------------------------------------------------------------------------- >>> 0: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie pod >>> 1: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie pokona >>> 2: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie, ``` ## BibTeX entry and citation info ```bibtex @misc{papuGaPT2, title={papuGaPT2 - Polish GPT2 language model}, url={https://huggingface.co/flax-community/papuGaPT2}, author={Wojczulis, Michał and Kłeczek, Dariusz}, year={2021} } ```
{"language": "pl", "tags": ["text-generation"], "widget": [{"text": "Najsmaczniejszy polski owoc to"}]}
text-generation
dkleczek/papuGaPT2
[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "pl", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "pl" ]
TAGS #transformers #pytorch #jax #tensorboard #gpt2 #text-generation #pl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# papuGaPT2 - Polish GPT2 language model GPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research. Our model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens). ## Datasets We used the Polish subset of the multilingual Oscar corpus to train the model in a self-supervised fashion. ## Intended uses & limitations The raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research. ## Bias Analysis There are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in this notebook. ### Gender Bias As an example, we generated 50 texts starting with prompts "She/He works as". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress. !gender bias ### Ethnicity/Nationality/Gender Bias We generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme: * Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts ("He/She"). * Topic - we used 5 different topics: * random act: *entered home* * said: *said* * works as: *works as* * intent: Polish *niech* which combined with *he* would roughly translate to *let him ...* * define: *is* Each combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts. We used a model trained on Polish Hate Speech corpus to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector. The following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline. !hate score by ethnicity Looking at the gender dimension we see higher hate score associated with males vs. females. !hate score by gender We don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided. ## Training procedure ### Training scripts We used the causal language modeling script for Flax. We would like to thank the authors of that script as it allowed us to complete this training in a very short time! ### Preprocessing and Training Details The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens. We have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state: 1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68 2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55 3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79 ## Evaluation results We trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in: * Evaluation loss: 3.082 * Perplexity: 21.79 ## How to use You can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples here including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations. ### Text generation Let's first start with the text-generation pipeline. When prompting for the best Polish poet, it comes up with a pretty reasonable text, highlighting one of the most famous Polish poets, Adam Mickiewicz. The pipeline uses 'model.generate()' method in the background. In our notebook we demonstrate different decoding methods we can use with this method, including greedy search, beam search, sampling, temperature scaling, top-k and top-p sampling. As an example, the below snippet uses sampling among the 50 most probable tokens at each stage (top-k) and among the tokens that jointly represent 95% of the probability distribution (top-p). It also returns 3 output sequences. ### Avoiding Bad Words You may want to prevent certain words from occurring in the generated text. To avoid displaying really bad words in the notebook, let's pretend that we don't like certain types of music to be advertised by our model. The prompt says: *my favorite type of music is*. Ok, it seems this worked: we can see *classical music, rap, metal* among the outputs. Interestingly, *reggae* found a way through via a misspelling *reggea*. Take it as a caution to be careful with curating your bad word lists! ### Few Shot Learning Let's see now if our model is able to pick up training signal directly from a prompt, without any finetuning. This approach was made really popular with GPT3, and while our model is definitely less powerful, maybe it can still show some skills! If you'd like to explore this topic in more depth, check out the following article which we used as reference. It looks like our model is able to pick up some signal from the prompt. Be careful though, this capability is definitely not mature and may result in spurious or biased responses. ### Zero-Shot Inference Large language models are known to store a lot of knowledge in its parameters. In the example below, we can see that our model has learned the date of an important event in Polish history, the battle of Grunwald. ## BibTeX entry and citation info
[ "# papuGaPT2 - Polish GPT2 language model\nGPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research. \n\nOur model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens).", "## Datasets\nWe used the Polish subset of the multilingual Oscar corpus to train the model in a self-supervised fashion.", "## Intended uses & limitations\nThe raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research.", "## Bias Analysis\nThere are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in this notebook.", "### Gender Bias\nAs an example, we generated 50 texts starting with prompts \"She/He works as\". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress.\n\n!gender bias", "### Ethnicity/Nationality/Gender Bias\nWe generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme: \n\n* Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts (\"He/She\"). \n* Topic - we used 5 different topics: \n * random act: *entered home*\n * said: *said*\n * works as: *works as*\n * intent: Polish *niech* which combined with *he* would roughly translate to *let him ...*\n * define: *is*\n\nEach combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts. \n\nWe used a model trained on Polish Hate Speech corpus to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector.\n \nThe following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline. \n\n!hate score by ethnicity\n\nLooking at the gender dimension we see higher hate score associated with males vs. females. \n\n!hate score by gender\n\nWe don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided.", "## Training procedure", "### Training scripts\nWe used the causal language modeling script for Flax. We would like to thank the authors of that script as it allowed us to complete this training in a very short time!", "### Preprocessing and Training Details\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.\n\nWe have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state: \n1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68\n2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55\n3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79", "## Evaluation results\nWe trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in: \n* Evaluation loss: 3.082\n* Perplexity: 21.79", "## How to use\nYou can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples here including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations.", "### Text generation\nLet's first start with the text-generation pipeline. When prompting for the best Polish poet, it comes up with a pretty reasonable text, highlighting one of the most famous Polish poets, Adam Mickiewicz.\n \n\n\nThe pipeline uses 'model.generate()' method in the background. In our notebook we demonstrate different decoding methods we can use with this method, including greedy search, beam search, sampling, temperature scaling, top-k and top-p sampling. As an example, the below snippet uses sampling among the 50 most probable tokens at each stage (top-k) and among the tokens that jointly represent 95% of the probability distribution (top-p). It also returns 3 output sequences.", "### Avoiding Bad Words\nYou may want to prevent certain words from occurring in the generated text. To avoid displaying really bad words in the notebook, let's pretend that we don't like certain types of music to be advertised by our model. The prompt says: *my favorite type of music is*. \n\n\nOk, it seems this worked: we can see *classical music, rap, metal* among the outputs. Interestingly, *reggae* found a way through via a misspelling *reggea*. Take it as a caution to be careful with curating your bad word lists!", "### Few Shot Learning\n\nLet's see now if our model is able to pick up training signal directly from a prompt, without any finetuning. This approach was made really popular with GPT3, and while our model is definitely less powerful, maybe it can still show some skills! If you'd like to explore this topic in more depth, check out the following article which we used as reference.\n\n\nIt looks like our model is able to pick up some signal from the prompt. Be careful though, this capability is definitely not mature and may result in spurious or biased responses.", "### Zero-Shot Inference\n\nLarge language models are known to store a lot of knowledge in its parameters. In the example below, we can see that our model has learned the date of an important event in Polish history, the battle of Grunwald.", "## BibTeX entry and citation info" ]
[ "TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #pl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# papuGaPT2 - Polish GPT2 language model\nGPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research. \n\nOur model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens).", "## Datasets\nWe used the Polish subset of the multilingual Oscar corpus to train the model in a self-supervised fashion.", "## Intended uses & limitations\nThe raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research.", "## Bias Analysis\nThere are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in this notebook.", "### Gender Bias\nAs an example, we generated 50 texts starting with prompts \"She/He works as\". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress.\n\n!gender bias", "### Ethnicity/Nationality/Gender Bias\nWe generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme: \n\n* Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts (\"He/She\"). \n* Topic - we used 5 different topics: \n * random act: *entered home*\n * said: *said*\n * works as: *works as*\n * intent: Polish *niech* which combined with *he* would roughly translate to *let him ...*\n * define: *is*\n\nEach combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts. \n\nWe used a model trained on Polish Hate Speech corpus to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector.\n \nThe following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline. \n\n!hate score by ethnicity\n\nLooking at the gender dimension we see higher hate score associated with males vs. females. \n\n!hate score by gender\n\nWe don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided.", "## Training procedure", "### Training scripts\nWe used the causal language modeling script for Flax. We would like to thank the authors of that script as it allowed us to complete this training in a very short time!", "### Preprocessing and Training Details\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.\n\nWe have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state: \n1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68\n2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55\n3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79", "## Evaluation results\nWe trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in: \n* Evaluation loss: 3.082\n* Perplexity: 21.79", "## How to use\nYou can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples here including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations.", "### Text generation\nLet's first start with the text-generation pipeline. When prompting for the best Polish poet, it comes up with a pretty reasonable text, highlighting one of the most famous Polish poets, Adam Mickiewicz.\n \n\n\nThe pipeline uses 'model.generate()' method in the background. In our notebook we demonstrate different decoding methods we can use with this method, including greedy search, beam search, sampling, temperature scaling, top-k and top-p sampling. As an example, the below snippet uses sampling among the 50 most probable tokens at each stage (top-k) and among the tokens that jointly represent 95% of the probability distribution (top-p). It also returns 3 output sequences.", "### Avoiding Bad Words\nYou may want to prevent certain words from occurring in the generated text. To avoid displaying really bad words in the notebook, let's pretend that we don't like certain types of music to be advertised by our model. The prompt says: *my favorite type of music is*. \n\n\nOk, it seems this worked: we can see *classical music, rap, metal* among the outputs. Interestingly, *reggae* found a way through via a misspelling *reggea*. Take it as a caution to be careful with curating your bad word lists!", "### Few Shot Learning\n\nLet's see now if our model is able to pick up training signal directly from a prompt, without any finetuning. This approach was made really popular with GPT3, and while our model is definitely less powerful, maybe it can still show some skills! If you'd like to explore this topic in more depth, check out the following article which we used as reference.\n\n\nIt looks like our model is able to pick up some signal from the prompt. Be careful though, this capability is definitely not mature and may result in spurious or biased responses.", "### Zero-Shot Inference\n\nLarge language models are known to store a lot of knowledge in its parameters. In the example below, we can see that our model has learned the date of an important event in Polish history, the battle of Grunwald.", "## BibTeX entry and citation info" ]
[ 56, 139, 31, 112, 56, 92, 367, 3, 43, 238, 54, 65, 177, 136, 126, 56, 10 ]
[ "passage: TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #pl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# papuGaPT2 - Polish GPT2 language model\nGPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research. \n\nOur model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens).## Datasets\nWe used the Polish subset of the multilingual Oscar corpus to train the model in a self-supervised fashion.## Intended uses & limitations\nThe raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research.## Bias Analysis\nThere are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in this notebook.### Gender Bias\nAs an example, we generated 50 texts starting with prompts \"She/He works as\". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress.\n\n!gender bias", "passage: ### Ethnicity/Nationality/Gender Bias\nWe generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme: \n\n* Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts (\"He/She\"). \n* Topic - we used 5 different topics: \n * random act: *entered home*\n * said: *said*\n * works as: *works as*\n * intent: Polish *niech* which combined with *he* would roughly translate to *let him ...*\n * define: *is*\n\nEach combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts. \n\nWe used a model trained on Polish Hate Speech corpus to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector.\n \nThe following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline. \n\n!hate score by ethnicity\n\nLooking at the gender dimension we see higher hate score associated with males vs. females. \n\n!hate score by gender\n\nWe don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided.## Training procedure### Training scripts\nWe used the causal language modeling script for Flax. We would like to thank the authors of that script as it allowed us to complete this training in a very short time!### Preprocessing and Training Details\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.\n\nWe have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state: \n1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68\n2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55\n3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79## Evaluation results\nWe trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in: \n* Evaluation loss: 3.082\n* Perplexity: 21.79## How to use\nYou can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples here including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations." ]
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null
null
transformers
# A certain person's AI
{"tags": ["conversational"]}
text-generation
dkminer81/Tromm
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# A certain person's AI
[ "# A certain person's AI" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# A certain person's AI" ]
[ 51, 7 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# A certain person's AI" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4171 - Wer: 0.3452 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0054 | 4.0 | 500 | 1.5456 | 0.9005 | | 0.8183 | 8.0 | 1000 | 0.4738 | 0.4839 | | 0.3019 | 12.0 | 1500 | 0.4280 | 0.4047 | | 0.1738 | 16.0 | 2000 | 0.4584 | 0.3738 | | 0.1285 | 20.0 | 2500 | 0.4418 | 0.3593 | | 0.1104 | 24.0 | 3000 | 0.4110 | 0.3479 | | 0.0828 | 28.0 | 3500 | 0.4171 | 0.3452 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.14.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-demo-colab", "results": []}]}
automatic-speech-recognition
dkssud/wav2vec2-base-demo-colab
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
wav2vec2-base-demo-colab ======================== This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4171 * Wer: 0.3452 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu102 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### 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: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\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: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 56, 130, 4, 33 ]
[ "passage: 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: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001 This is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805). The model has been further quantized to INT8 precision using quantization-aware fine-tuning with [NNCF](https://github.com/openvinotoolkit/nncf). Model source: [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001)
{}
question-answering
dkurt/bert-large-uncased-whole-word-masking-squad-int8-0001
[ "transformers", "bert", "question-answering", "arxiv:1810.04805", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1810.04805" ]
[]
TAGS #transformers #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us
# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001 This is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. The model has been further quantized to INT8 precision using quantization-aware fine-tuning with NNCF. Model source: Open Model Zoo
[ "# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001\n\nThis is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.\n\nThe model has been further quantized to INT8 precision using quantization-aware fine-tuning with NNCF.\n\nModel source: Open Model Zoo" ]
[ "TAGS\n#transformers #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us \n", "# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001\n\nThis is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.\n\nThe model has been further quantized to INT8 precision using quantization-aware fine-tuning with NNCF.\n\nModel source: Open Model Zoo" ]
[ 34, 184 ]
[ "passage: TAGS\n#transformers #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us \n# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001\n\nThis is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.\n\nThe model has been further quantized to INT8 precision using quantization-aware fine-tuning with NNCF.\n\nModel source: Open Model Zoo" ]
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null
null
transformers
[anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) model quantized with [Optimum OpenVINO](https://github.com/dkurt/optimum-openvino/). | Accuracy on eval (baseline) | Accuracy on eval (quantized) | |-----------------------------|----------------------------------------| | 0.9828 | 0.9553 (-0.0274) |
{}
audio-classification
dkurt/wav2vec2-base-ft-keyword-spotting-int8
[ "transformers", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #wav2vec2 #audio-classification #endpoints_compatible #region-us
anton-l/wav2vec2-base-ft-keyword-spotting model quantized with Optimum OpenVINO.
[]
[ "TAGS\n#transformers #wav2vec2 #audio-classification #endpoints_compatible #region-us \n" ]
[ 29 ]
[ "passage: TAGS\n#transformers #wav2vec2 #audio-classification #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.926 - F1: 0.9261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8436 | 1.0 | 250 | 0.3175 | 0.9105 | 0.9081 | | 0.2492 | 2.0 | 500 | 0.2161 | 0.926 | 0.9261 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.7.1 - Datasets 1.17.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}, {"type": "f1", "value": 0.9261144741040841, "name": "F1"}]}]}]}
text-classification
dmiller1/distilbert-base-uncased-finetuned-emotion
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2161 * Accuracy: 0.926 * F1: 0.9261 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.7.1 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### 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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.7.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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: 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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.7.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 63, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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: 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: 2### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.7.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
NER Model of BERN2 system
{}
null
dmis-lab/bern2-ner
[ "transformers", "pytorch", "roberta", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #roberta #endpoints_compatible #region-us
NER Model of BERN2 system
[]
[ "TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n" ]
[ 24 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Model Card for biobert-large-cased-v1.1-squad # Model Details ## Model Description More information needed - **Developed by:** DMIS-lab (Data Mining and Information Systems Lab, Korea University) - **Shared by [Optional]:** DMIS-lab (Data Mining and Information Systems Lab, Korea University) - **Model type:** Question Answering - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** [gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) - **Resources for more information:** - [GitHub Repo](https://github.com/jhyuklee/biobert) - [Associated Paper](https://arxiv.org/abs/1901.08746) # Uses ## Direct Use This model can be used for the task of question answering. ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf): > We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC)) ## Training Procedure ### Preprocessing The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf): > We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs ### Speeds, Sizes, Times The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf): > The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Training**: Eight NVIDIA V100 (32GB) GPUs [ for training], - **Fine-tuning:** a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed. # Citation **BibTeX:** ```bibtex @misc{mesh-transformer-jax, @article{lee2019biobert, title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining}, author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo}, journal={arXiv preprint arXiv:1901.08746}, year={2019} } ``` # Glossary [optional] More information needed # More Information [optional] For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee(`lee.jnhk (at) gmail.com`), or Wonjin Yoon (`wonjin.info (at) gmail.com`) for communication related to BioBERT. # Model Card Authors [optional] DMIS-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad") model = AutoModelForQuestionAnswering.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad") ``` </details>
{"tags": ["question-answering", "bert"]}
question-answering
dmis-lab/biobert-large-cased-v1.1-squad
[ "transformers", "pytorch", "jax", "bert", "question-answering", "arxiv:1901.08746", "arxiv:1910.09700", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1901.08746", "1910.09700" ]
[]
TAGS #transformers #pytorch #jax #bert #question-answering #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
# Model Card for biobert-large-cased-v1.1-squad # Model Details ## Model Description More information needed - Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University) - Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University) - Model type: Question Answering - Language(s) (NLP): More information needed - License: More information needed - Parent Model: gpt-neo-2.7B - Resources for more information: - GitHub Repo - Associated Paper # Uses ## Direct Use This model can be used for the task of question answering. ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model creators note in the associated paper: > We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC)) ## Training Procedure ### Preprocessing The model creators note in the associated paper: > We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs ### Speeds, Sizes, Times The model creators note in the associated paper: > The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: More information needed - Training: Eight NVIDIA V100 (32GB) GPUs [ for training], - Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed. BibTeX: # Glossary [optional] More information needed # More Information [optional] For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT. # Model Card Authors [optional] DMIS-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> </details>
[ "# Model Card for biobert-large-cased-v1.1-squad", "# Model Details", "## Model Description\n \nMore information needed\n \n- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n\n- Model type: Question Answering\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Parent Model: gpt-neo-2.7B\n- Resources for more information:\n \t- GitHub Repo\n \t - Associated Paper", "# Uses", "## Direct Use\nThis model can be used for the task of question answering.", "## Downstream Use [Optional]\n \nMore information needed.", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\n \n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "# Training Details", "## Training Data\n \nThe model creators note in the associated paper:\n> We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC))", "## Training Procedure", "### Preprocessing\n \n The model creators note in the associated paper:\n> We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs", "### Speeds, Sizes, Times\n \n The model creators note in the associated paper:\n> The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors\nMore information needed", "### Metrics\n \nMore information needed", "## Results \n \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n\t- Training: Eight NVIDIA V100 (32GB) GPUs [ for training], \n - Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \n \nMore information needed", "### Software\n \nMore information needed.\n \nBibTeX:", "# Glossary [optional]\n \nMore information needed", "# More Information [optional]\n \nFor help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT.", "# Model Card Authors [optional]\n \n DMIS-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]
[ "TAGS\n#transformers #pytorch #jax #bert #question-answering #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n", "# Model Card for biobert-large-cased-v1.1-squad", "# Model Details", "## Model Description\n \nMore information needed\n \n- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n\n- Model type: Question Answering\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Parent Model: gpt-neo-2.7B\n- Resources for more information:\n \t- GitHub Repo\n \t - Associated Paper", "# Uses", "## Direct Use\nThis model can be used for the task of question answering.", "## Downstream Use [Optional]\n \nMore information needed.", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\n \n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "# Training Details", "## Training Data\n \nThe model creators note in the associated paper:\n> We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC))", "## Training Procedure", "### Preprocessing\n \n The model creators note in the associated paper:\n> We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs", "### Speeds, Sizes, Times\n \n The model creators note in the associated paper:\n> The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors\nMore information needed", "### Metrics\n \nMore information needed", "## Results \n \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n\t- Training: Eight NVIDIA V100 (32GB) GPUs [ for training], \n - Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \n \nMore information needed", "### Software\n \nMore information needed.\n \nBibTeX:", "# Glossary [optional]\n \nMore information needed", "# More Information [optional]\n \nFor help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT.", "# Model Card Authors [optional]\n \n DMIS-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]
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[ "passage: TAGS\n#transformers #pytorch #jax #bert #question-answering #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n# Model Card for biobert-large-cased-v1.1-squad# Model Details## Model Description\n \nMore information needed\n \n- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n\n- Model type: Question Answering\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Parent Model: gpt-neo-2.7B\n- Resources for more information:\n \t- GitHub Repo\n \t - Associated Paper# Uses## Direct Use\nThis model can be used for the task of question answering.## Downstream Use [Optional]\n \nMore information needed.## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.# Bias, Risks, and Limitations\n \n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.## Recommendations\n \n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.# Training Details" ]
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null
null
transformers
hello
{}
feature-extraction
dmis-lab/biosyn-biobert-bc2gn
[ "transformers", "pytorch", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
hello
[]
[ "TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n" ]
[ 29 ]
[ "passage: TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n" ]
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null
null
transformers
hello
{}
feature-extraction
dmis-lab/biosyn-sapbert-bc2gn
[ "transformers", "pytorch", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
hello
[]
[ "TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n" ]
[ 29 ]
[ "passage: TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Model Card for biosyn-sapbert-ncbi-disease # Model Details ## Model Description More information needed - **Developed by:** Dmis-lab (Data Mining and Information Systems Lab, Korea University) - **Shared by [Optional]:** Hugging Face - **Model type:** Feature Extraction - **Language(s) (NLP):** More information needed - **License:** More information needed - **Related Models:** - **Parent Model:** BERT - **Resources for more information:** - [GitHub Repo](https://github.com/jhyuklee/biobert) - [Associated Paper](https://arxiv.org/abs/1901.08746) # Uses ## Direct Use This model can be used for the task of Feature Extraction ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) > We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC)) ## Training Procedure ### Preprocessing The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) > We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs ### Speeds, Sizes, Times The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) > The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** - **Training:** Eight NVIDIA V100 (32GB) GPUs [ for training], - **Fine-tuning:** a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation **BibTeX:** ``` @article{lee2019biobert, title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining}, author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo}, journal={arXiv preprint arXiv:1901.08746}, year={2019} } ``` # Glossary [optional] More information needed # More Information [optional] For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee(`lee.jnhk (at) gmail.com`), or Wonjin Yoon (`wonjin.info (at) gmail.com`) for communication related to BioBERT. # Model Card Authors [optional] Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease") model = AutoModel.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease") ``` </details>
{"tags": ["bert"]}
feature-extraction
dmis-lab/biosyn-sapbert-ncbi-disease
[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:1901.08746", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1901.08746", "1910.09700" ]
[]
TAGS #transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us
# Model Card for biosyn-sapbert-ncbi-disease # Model Details ## Model Description More information needed - Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University) - Shared by [Optional]: Hugging Face - Model type: Feature Extraction - Language(s) (NLP): More information needed - License: More information needed - Related Models: - Parent Model: BERT - Resources for more information: - GitHub Repo - Associated Paper # Uses ## Direct Use This model can be used for the task of Feature Extraction ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model creators note in the associated paper > We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC)) ## Training Procedure ### Preprocessing The model creators note in the associated paper > We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs ### Speeds, Sizes, Times The model creators note in the associated paper > The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Training: Eight NVIDIA V100 (32GB) GPUs [ for training], - Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed BibTeX: # Glossary [optional] More information needed # More Information [optional] For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT. # Model Card Authors [optional] Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> </details>
[ "# Model Card for biosyn-sapbert-ncbi-disease", "# Model Details", "## Model Description\n \nMore information needed\n \n- Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: Hugging Face\n- Model type: Feature Extraction\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: BERT\n- Resources for more information: \n - GitHub Repo\n - Associated Paper", "# Uses", "## Direct Use\n \nThis model can be used for the task of Feature Extraction", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "# Training Details", "## Training Data\nThe model creators note in the associated paper\n> We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC))", "## Training Procedure", "### Preprocessing\n The model creators note in the associated paper\n> We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs", "### Speeds, Sizes, Times\n The model creators note in the associated paper\n> The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors\n \nMore information needed", "### Metrics\n \n \n \nMore information needed", "## Results \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: \n- Training: Eight NVIDIA V100 (32GB) GPUs [ for training], \n- Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\n \nMore information needed\n \nBibTeX:", "# Glossary [optional]\n \nMore information needed", "# More Information [optional]\n \nFor help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT.", "# Model Card Authors [optional]\n \n \n Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]
[ "TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for biosyn-sapbert-ncbi-disease", "# Model Details", "## Model Description\n \nMore information needed\n \n- Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: Hugging Face\n- Model type: Feature Extraction\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: BERT\n- Resources for more information: \n - GitHub Repo\n - Associated Paper", "# Uses", "## Direct Use\n \nThis model can be used for the task of Feature Extraction", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "# Training Details", "## Training Data\nThe model creators note in the associated paper\n> We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC))", "## Training Procedure", "### Preprocessing\n The model creators note in the associated paper\n> We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs", "### Speeds, Sizes, Times\n The model creators note in the associated paper\n> The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors\n \nMore information needed", "### Metrics\n \n \n \nMore information needed", "## Results \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: \n- Training: Eight NVIDIA V100 (32GB) GPUs [ for training], \n- Fine-tuning: a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\n \nMore information needed\n \nBibTeX:", "# Glossary [optional]\n \nMore information needed", "# More Information [optional]\n \nFor help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') for communication related to BioBERT.", "# Model Card Authors [optional]\n \n \n Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]
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[ "passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for biosyn-sapbert-ncbi-disease# Model Details## Model Description\n \nMore information needed\n \n- Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: Hugging Face\n- Model type: Feature Extraction\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: BERT\n- Resources for more information: \n - GitHub Repo\n - Associated Paper# Uses## Direct Use\n \nThis model can be used for the task of Feature Extraction## Downstream Use [Optional]\n \nMore information needed## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.## Recommendations\n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.# Training Details" ]
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null
null
transformers
# rubert_ria_headlines ## Description *bert2bert* model, initialized with the `DeepPavlov/rubert-base-cased` pretrained weights and fine-tuned on the first 99% of ["Rossiya Segodnya" news dataset](https://github.com/RossiyaSegodnya/ria_news_dataset) for 2 epochs. ## Usage example ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM MODEL_NAME = "dmitry-vorobiev/rubert_ria_headlines" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) text = "Скопируйте текст статьи / новости" encoded_batch = tokenizer.prepare_seq2seq_batch( [text], return_tensors="pt", padding="max_length", truncation=True, max_length=512) output_ids = model.generate( input_ids=encoded_batch["input_ids"], max_length=36, no_repeat_ngram_size=3, num_beams=5, top_k=0 ) headline = tokenizer.decode(output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) print(headline) ``` ## Datasets - [ria_news](https://github.com/RossiyaSegodnya/ria_news_dataset) ## How it was trained? I used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start). - [1 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53254694) - [2 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53269040) - [3 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53280797) Common train params: ```shell export XLA_USE_BF16=1 export XLA_TENSOR_ALLOCATOR_MAXSIZE=100000000 python nlp_headline_rus/src/train_seq2seq.py \ --do_train \ --tie_encoder_decoder \ --max_source_length 512 \ --max_target_length 32 \ --val_max_target_length 48 \ --tpu_num_cores 8 \ --per_device_train_batch_size 24 \ --gradient_accumulation_steps 1 \ --learning_rate 5e-4 \ --adam_epsilon 1e-6 \ --weight_decay 1e-5 \ ``` ## Validation results - Using [last 1% of ria](https://drive.google.com/drive/folders/1ztAeyb1BiLMgXwOgOJS7WMR4PGiI1q92) dataset - Using [gazeta_ru test](https://drive.google.com/drive/folders/1CyowuRpecsLTcDbqEfmAvkCWOod58g_e) split - Using [gazeta_ru val](https://drive.google.com/drive/folders/1XZFOXHSXLKdhzm61ceVLw3aautrdskIu) split
{"language": ["ru"], "license": "mit", "tags": ["summarization", "bert", "rubert"]}
summarization
dmitry-vorobiev/rubert_ria_headlines
[ "transformers", "pytorch", "safetensors", "encoder-decoder", "text2text-generation", "summarization", "bert", "rubert", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ru" ]
TAGS #transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #bert #rubert #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us
# rubert_ria_headlines ## Description *bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and fine-tuned on the first 99% of "Rossiya Segodnya" news dataset for 2 epochs. ## Usage example ## Datasets - ria_news ## How it was trained? I used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start). - 1 epoch notebook - 2 epoch notebook - 3 epoch notebook Common train params: ## Validation results - Using last 1% of ria dataset - Using gazeta_ru test split - Using gazeta_ru val split
[ "# rubert_ria_headlines", "## Description\n*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and \n fine-tuned on the first 99% of \"Rossiya Segodnya\" news dataset for 2 epochs.", "## Usage example", "## Datasets\n- ria_news", "## How it was trained?\n\nI used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start).\n\n- 1 epoch notebook\n- 2 epoch notebook\n- 3 epoch notebook\n\nCommon train params:", "## Validation results\n\n- Using last 1% of ria dataset\n- Using gazeta_ru test split\n- Using gazeta_ru val split" ]
[ "TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #bert #rubert #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# rubert_ria_headlines", "## Description\n*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and \n fine-tuned on the first 99% of \"Rossiya Segodnya\" news dataset for 2 epochs.", "## Usage example", "## Datasets\n- ria_news", "## How it was trained?\n\nI used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start).\n\n- 1 epoch notebook\n- 2 epoch notebook\n- 3 epoch notebook\n\nCommon train params:", "## Validation results\n\n- Using last 1% of ria dataset\n- Using gazeta_ru test split\n- Using gazeta_ru val split" ]
[ 63, 8, 60, 4, 9, 83, 31 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #bert #rubert #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# rubert_ria_headlines## Description\n*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and \n fine-tuned on the first 99% of \"Rossiya Segodnya\" news dataset for 2 epochs.## Usage example## Datasets\n- ria_news## How it was trained?\n\nI used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start).\n\n- 1 epoch notebook\n- 2 epoch notebook\n- 3 epoch notebook\n\nCommon train params:## Validation results\n\n- Using last 1% of ria dataset\n- Using gazeta_ru test split\n- Using gazeta_ru val split" ]
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null
null
transformers
# doc2query/S2ORC-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/S2ORC-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 156k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, abstract) pairs from [S2ORC](https://github.com/allenai/s2orc).
{"language": "en", "license": "apache-2.0", "datasets": ["S2ORC"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/S2ORC-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:S2ORC", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-S2ORC #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/S2ORC-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 156k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, abstract) pairs from S2ORC.
[ "# doc2query/S2ORC-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 156k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, abstract) pairs from S2ORC." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-S2ORC #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/S2ORC-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 156k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, abstract) pairs from S2ORC." ]
[ 83, 261, 32, 89 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-S2ORC #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/S2ORC-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 156k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, abstract) pairs from S2ORC." ]
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null
null
transformers
# doc2query/all-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/all-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 570k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a large collection of datasets. For the exact datasets names and weights see the `data_config.json` in this repository. Most of the datasets are available at [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). The datasets include besides others: - (title, body) pairs from [Reddit](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) - (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers! - (title, review) pairs from Amazon reviews - (query, paragraph) pairs from MS MARCO, NQ, and GooAQ - (question, duplicate_question) from Quora and WikiAnswers - (title, abstract) pairs from S2ORC ## Prefix This model was trained **without a prefix**. In contrast to [doc2query/all-with_prefix-t5-base-v1](https://huggingface.co/doc2query/all-with_prefix-t5-base-v1) you cannot specify what type of transformation (answer2question, review2title) etc. you will have. This can lead to a mixture of output values.
{"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/reddit-title-body", "sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/all-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:sentence-transformers/reddit-title-body", "dataset:sentence-transformers/embedding-training-data", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/all-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 570k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a large collection of datasets. For the exact datasets names and weights see the 'data_config.json' in this repository. Most of the datasets are available at URL The datasets include besides others: - (title, body) pairs from Reddit - (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers! - (title, review) pairs from Amazon reviews - (query, paragraph) pairs from MS MARCO, NQ, and GooAQ - (question, duplicate_question) from Quora and WikiAnswers - (title, abstract) pairs from S2ORC ## Prefix This model was trained without a prefix. In contrast to doc2query/all-with_prefix-t5-base-v1 you cannot specify what type of transformation (answer2question, review2title) etc. you will have. This can lead to a mixture of output values.
[ "# doc2query/all-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 570k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a large collection of datasets. For the exact datasets names and weights see the 'data_config.json' in this repository. Most of the datasets are available at URL\r\n\r\nThe datasets include besides others:\r\n- (title, body) pairs from Reddit\r\n- (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers!\r\n- (title, review) pairs from Amazon reviews\r\n- (query, paragraph) pairs from MS MARCO, NQ, and GooAQ \r\n- (question, duplicate_question) from Quora and WikiAnswers\r\n- (title, abstract) pairs from S2ORC", "## Prefix\r\n\r\nThis model was trained without a prefix. In contrast to doc2query/all-with_prefix-t5-base-v1 you cannot specify what type of transformation (answer2question, review2title) etc. you will have. This can lead to a mixture of output values." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/all-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 570k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a large collection of datasets. For the exact datasets names and weights see the 'data_config.json' in this repository. Most of the datasets are available at URL\r\n\r\nThe datasets include besides others:\r\n- (title, body) pairs from Reddit\r\n- (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers!\r\n- (title, review) pairs from Amazon reviews\r\n- (query, paragraph) pairs from MS MARCO, NQ, and GooAQ \r\n- (question, duplicate_question) from Quora and WikiAnswers\r\n- (title, abstract) pairs from S2ORC", "## Prefix\r\n\r\nThis model was trained without a prefix. In contrast to doc2query/all-with_prefix-t5-base-v1 you cannot specify what type of transformation (answer2question, review2title) etc. you will have. This can lead to a mixture of output values." ]
[ 107, 258, 32, 229, 69 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/all-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it." ]
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null
null
transformers
# doc2query/all-with_prefix-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/all-with_prefix-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) prefix = "answer2question" text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." text = prefix+": "+text input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 575k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a large collection of datasets. For the exact datasets names and weights see the `data_config.json` in this repository. Most of the datasets are available at [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). The datasets include besides others: - (title, body) pairs from [Reddit](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) - (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers! - (title, review) pairs from Amazon reviews - (query, paragraph) pairs from MS MARCO, NQ, and GooAQ - (question, duplicate_question) from Quora and WikiAnswers - (title, abstract) pairs from S2ORC ## Prefix This model was trained **with a prefix**: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different. E.g. the above text about Python produces the following output: | Prefix | Output | | --- | --- | | answer2question | Why should I use python in my business? ; What is the difference between Python and.NET? ; what is the python design philosophy? | | review2title | Python a powerful and useful language ; A new and improved programming language ; Object-oriented, practical and accessibl | | abstract2title | Python: A Software Development Platform ; A Research Guide for Python X: Conceptual Approach to Programming ; Python : Language and Approach | | text2query | is python a low level language? ; what is the primary idea of python? ; is python a programming language? | These are all available pre-fixes: - text2reddit - question2title - answer2question - abstract2title - review2title - news2title - text2query - question2question For the datasets and weights for the different pre-fixes see `data_config.json` in this repository.
{"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/reddit-title-body", "sentence-transformers/embedding-training-data"], "widget": [{"text": "text2reddit: Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/all-with_prefix-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:sentence-transformers/reddit-title-body", "dataset:sentence-transformers/embedding-training-data", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
doc2query/all-with\_prefix-t5-base-v1 ===================================== This is a doc2query model based on T5 (also known as docT5query). It can be used for: * Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. * Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. Usage ----- Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. Training -------- This model fine-tuned google/t5-v1\_1-base for 575k training steps. For the training script, see the 'train\_script.py' in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a large collection of datasets. For the exact datasets names and weights see the 'data\_config.json' in this repository. Most of the datasets are available at URL The datasets include besides others: * (title, body) pairs from Reddit * (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers! * (title, review) pairs from Amazon reviews * (query, paragraph) pairs from MS MARCO, NQ, and GooAQ * (question, duplicate\_question) from Quora and WikiAnswers * (title, abstract) pairs from S2ORC Prefix ------ This model was trained with a prefix: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different. E.g. the above text about Python produces the following output: These are all available pre-fixes: * text2reddit * question2title * answer2question * abstract2title * review2title * news2title * text2query * question2question For the datasets and weights for the different pre-fixes see 'data\_config.json' in this repository.
[]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]
[ 111 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]
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transformers
# doc2query/msmarco-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/msmarco-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [MS MARCO Passage-Ranking dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking).
{"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/msmarco-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:sentence-transformers/embedding-training-data", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/msmarco-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset.
[ "# doc2query/msmarco-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/msmarco-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
[ 91, 260, 32, 113 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/msmarco-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
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null
null
transformers
# doc2query/msmarco-t5-small-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/msmarco-t5-small-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [MS MARCO Passage-Ranking dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking).
{"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/msmarco-t5-small-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:sentence-transformers/embedding-training-data", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/msmarco-t5-small-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-small for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset.
[ "# doc2query/msmarco-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/msmarco-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
[ 91, 261, 32, 114 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/msmarco-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-small for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (query, passage) from the MS MARCO Passage-Ranking dataset." ]
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null
null
transformers
# doc2query/reddit-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/reddit-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 533k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, body) from Reddit.
{"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/reddit-title-body"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/reddit-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/reddit-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 533k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, body) from Reddit.
[ "# doc2query/reddit-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 533k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/reddit-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 533k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
[ 75, 259, 32, 85 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/reddit-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 533k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
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null
null
transformers
# doc2query/reddit-t5-small-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/reddit-t5-small-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) for 547k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, body) from Reddit.
{"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/reddit-title-body"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/reddit-t5-small-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/reddit-t5-small-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-small for 547k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, body) from Reddit.
[ "# doc2query/reddit-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 547k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/reddit-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 547k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
[ 75, 260, 32, 87 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/reddit-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-small for 547k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, body) from Reddit." ]
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null
null
transformers
# doc2query/stackexchange-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/stackexchange-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 449k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, best_answer_pairs) from StackExchange.
{"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/stackexchange-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/stackexchange-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 449k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, best_answer_pairs) from StackExchange.
[ "# doc2query/stackexchange-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 449k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, best_answer_pairs) from StackExchange." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/stackexchange-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 449k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, best_answer_pairs) from StackExchange." ]
[ 107, 261, 32, 94 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/stackexchange-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 449k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, best_answer_pairs) from StackExchange." ]
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null
null
transformers
# doc2query/stackexchange-title-body-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/stackexchange-title-body-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 550k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, question_body) from StackExchange.
{"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_body_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/stackexchange-title-body-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:flax-sentence-embeddings/stackexchange_title_body_jsonl", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/stackexchange-title-body-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 550k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, question_body) from StackExchange.
[ "# doc2query/stackexchange-title-body-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 550k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/stackexchange-title-body-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 550k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
[ 101, 265, 32, 89 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/stackexchange-title-body-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 550k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
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null
null
transformers
# doc2query/stackexchange-title-body-t5-small-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/stackexchange-title-body-t5-small-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) for 321k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, question_body) from StackExchange.
{"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_body_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/stackexchange-title-body-t5-small-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:flax-sentence-embeddings/stackexchange_title_body_jsonl", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# doc2query/stackexchange-title-body-t5-small-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-small for 321k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, question_body) from StackExchange.
[ "# doc2query/stackexchange-title-body-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 321k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# doc2query/stackexchange-title-body-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-small for 321k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
[ 101, 266, 32, 92 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# doc2query/stackexchange-title-body-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-small for 321k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, question_body) from StackExchange." ]
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null
transformers
# doc2query/yahoo_answers-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/yahoo_answers-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 111k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, answer) pairs from [Yahoo Answers](https://huggingface.co/datasets/sentence-transformers/embedding-training-data).
{"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."}]}
text2text-generation
doc2query/yahoo_answers-t5-base-v1
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "arxiv:1904.08375", "arxiv:2104.08663", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1904.08375", "2104.08663" ]
[ "en" ]
TAGS #transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
# doc2query/yahoo_answers-t5-base-v1 This is a doc2query model based on T5 (also known as docT5query). It can be used for: - Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini. - Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage Note: 'model.generate()' is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned google/t5-v1_1-base for 111k training steps. For the training script, see the 'train_script.py' in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, answer) pairs from Yahoo Answers.
[ "# doc2query/yahoo_answers-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 111k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, answer) pairs from Yahoo Answers." ]
[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# doc2query/yahoo_answers-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.", "## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.", "## Training\r\nThis model fine-tuned google/t5-v1_1-base for 111k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, answer) pairs from Yahoo Answers." ]
[ 79, 261, 32, 88 ]
[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# doc2query/yahoo_answers-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our BEIR paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.\r\n- Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. On URL we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.## Usage\r\n\r\n\r\nNote: 'model.generate()' is non-deterministic. It produces different queries each time you run it.## Training\r\nThis model fine-tuned google/t5-v1_1-base for 111k training steps. For the training script, see the 'train_script.py' in this repository.\r\n\r\nThe input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. \r\n\r\nThis model was trained on a (title, answer) pairs from Yahoo Answers." ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.6045 - Accuracy: 0.7960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7494 | 1.0 | 4597 | 0.5942 | 0.7716 | | 0.3499 | 2.0 | 9194 | 0.6045 | 0.7960 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]}
multiple-choice
domdomreloaded/bert-base-uncased-finetuned-swag
[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
bert-base-uncased-finetuned-swag ================================ This model is a fine-tuned version of bert-base-uncased on the swag dataset. It achieves the following results on the evaluation set: * Loss: 0.6045 * Accuracy: 0.7960 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 54, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0492 - Precision: 0.9530 - Recall: 0.9604 - F1: 0.9567 - Accuracy: 0.9889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2031 | 1.0 | 878 | 0.0560 | 0.9381 | 0.9445 | 0.9413 | 0.9858 | | 0.0446 | 2.0 | 1756 | 0.0480 | 0.9510 | 0.9578 | 0.9544 | 0.9887 | | 0.0263 | 3.0 | 2634 | 0.0492 | 0.9530 | 0.9604 | 0.9567 | 0.9889 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.0 - Tokenizers 0.10.3
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9529566113766282, "name": "Precision"}, {"type": "recall", "value": 0.9604268983755194, "name": "Recall"}, {"type": "f1", "value": 0.9566771720212616, "name": "F1"}, {"type": "accuracy", "value": 0.988938664048357, "name": "Accuracy"}]}]}]}
token-classification
dominiqueblok/roberta-base-finetuned-ner
[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
roberta-base-finetuned-ner ========================== This model is a fine-tuned version of roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set: * Loss: 0.0492 * Precision: 0.9530 * Recall: 0.9604 * F1: 0.9567 * Accuracy: 0.9889 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.0 * Tokenizers 0.10.3
[ "### 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
[ 61, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #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: 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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3" ]
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null
null
null
# this is a shit model
{}
null
douglas0204/shitmodel
[ "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #region-us
# this is a shit model
[ "# this is a shit model" ]
[ "TAGS\n#region-us \n", "# this is a shit model" ]
[ 6, 6 ]
[ "passage: TAGS\n#region-us \n# this is a shit model" ]
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