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question-answering | transformers |
# Roberta-base-Squad2-NQ
## What is SQuAD?
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the ques... | {"license": "apache-2.0", "tags": ["qa"], "datasets": ["squad_v2", "natural_questions"], "model-index": [{"name": "nlpconnect/roberta-base-squad2-nq", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "va... | nlpconnect/roberta-base-squad2-nq | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"qa",
"dataset:squad_v2",
"dataset:natural_questions",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #question-answering #qa #dataset-squad_v2 #dataset-natural_questions #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Roberta-base-Squad2-NQ
## What is SQuAD?
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the ques... | [
"# Roberta-base-Squad2-NQ",
"## What is SQuAD?\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or... | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #qa #dataset-squad_v2 #dataset-natural_questions #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Roberta-base-Squad2-NQ",
"## What is SQuAD?\nStanford Question Answering Dataset (SQuAD) is a reading comprehensio... |
image-to-text | transformers |
# nlpconnect/vit-gpt2-image-captioning
This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts).
# The Illustrated Image Captioning u... | {"license": "apache-2.0", "tags": ["image-to-text", "image-captioning"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": "Foot... | nlpconnect/vit-gpt2-image-captioning | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"image-captioning",
"doi:10.57967/hf/0222",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #doi-10.57967/hf/0222 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# nlpconnect/vit-gpt2-image-captioning
This is an image captioning model trained by @ydshieh in flax this is pytorch version of this.
# The Illustrated Image Captioning using transformers
![](URL
* URL
# Sample running code
# Sample running code using transformers pipeline
# Contact for any help
* URL
*... | [
"# nlpconnect/vit-gpt2-image-captioning\n\nThis is an image captioning model trained by @ydshieh in flax this is pytorch version of this.",
"# The Illustrated Image Captioning using transformers\n\n![](URL\n\n* URL",
"# Sample running code",
"# Sample running code using transformers pipeline",
"# Contact f... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #doi-10.57967/hf/0222 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# nlpconnect/vit-gpt2-image-captioning\n\nThis is an image captioning model trained by @ydshieh in flax this is pytorch version of thi... |
text-classification | transformers |
# bert-base-multilingual-uncased-sentiment
This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
This model is intended ... | {"language": ["en", "nl", "de", "fr", "it", "es"], "license": "mit"} | nlptown/bert-base-multilingual-uncased-sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"en",
"nl",
"de",
"fr",
"it",
"es",
"doi:10.57967/hf/1515",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"nl",
"de",
"fr",
"it",
"es"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #en #nl #de #fr #it #es #doi-10.57967/hf/1515 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-multilingual-uncased-sentiment
========================================
This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (bet... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #en #nl #de #fr #it #es #doi-10.57967/hf/1515 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | 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-cased-IUChatbot-ontologyDts
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-IUChatbot-ontologyDts", "results": []}]} | nntadotzip/bert-base-cased-IUChatbot-ontologyDts | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased-IUChatbot-ontologyDts
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2446
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
question-answering | 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. -->
# xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast
This model is a fine-tuned version of [xlnet-base-cased](http... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast", "results": []}]} | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast
==================================================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3489
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
question-answering | 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. -->
# xlnet-base-cased-IUChatbot-ontologyDts-localParams
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-IUChatbot-ontologyDts-localParams", "results": []}]} | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-localParams | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-IUChatbot-ontologyDts-localParams
==================================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0238
Model description
-----------------
More information needed
... | [
"### 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: 32\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
question-answering | 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. -->
# xlnet-base-cased-IUChatbot-ontologyDts
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-IUChatbot-ontologyDts", "results": []}]} | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-IUChatbot-ontologyDts
======================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4965
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
text2text-generation | transformers | ## Model description
This model is a sequence-to-sequence question generator that takes an answer and context as an input and generates a question as an output. It is based on a pre-trained mt5-base by [Google](https://github.com/google-research/multilingual-t5) model.
## Training data
The model was fine-tuned on [XQu... | {} | noah-ai/mt5-base-question-generation-vi | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Model description
This model is a sequence-to-sequence question generator that takes an answer and context as an input and generates a question as an output. It is based on a pre-trained mt5-base by Google model.
## Training data
The model was fine-tuned on XQuAD
## Example usage
> Created by Duong Thanh Nguyen | [
"## Model description\nThis model is a sequence-to-sequence question generator that takes an answer and context as an input and generates a question as an output. It is based on a pre-trained mt5-base by Google model.",
"## Training data\nThe model was fine-tuned on XQuAD",
"## Example usage\n\n\n> Created by D... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\nThis model is a sequence-to-sequence question generator that takes an answer and context as an input and generates a question as an output. It is b... |
token-classification | transformers |
# Cause-Effect Detection for Software Requirements Based on Token Classification with BERT
This model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains.
The model outputs one of the f... | {"widget": [{"text": "If a user signs up, he will receive a confirmation email."}]} | noahjadallah/cause-effect-detection | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cause-Effect Detection for Software Requirements Based on Token Classification with BERT
This model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains.
The model outputs one of the f... | [
"# Cause-Effect Detection for Software Requirements Based on Token Classification with BERT\n\nThis model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains.\n\nThe model outputs one ... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cause-Effect Detection for Software Requirements Based on Token Classification with BERT\n\nThis model uses BERT to detect cause and effect from a single sentence. The focus of ... |
text-generation | transformers | ## About
`Distilgpt2` model finetuned on a dataset of inspirational/motivational quotes taken from the [Quotes-500K](https://github.com/ShivaliGoel/Quotes-500K) dataset. The model can generate inspirational quotes, many of which sound quite realistic.
## Code for Training
The code for fine-tuning the model can be foun... | {} | noelmathewisaac/inspirational-quotes-distilgpt2 | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## About
'Distilgpt2' model finetuned on a dataset of inspirational/motivational quotes taken from the Quotes-500K dataset. The model can generate inspirational quotes, many of which sound quite realistic.
## Code for Training
The code for fine-tuning the model can be found in this repo: URL
## Training Details
The m... | [
"## About\n'Distilgpt2' model finetuned on a dataset of inspirational/motivational quotes taken from the Quotes-500K dataset. The model can generate inspirational quotes, many of which sound quite realistic.",
"## Code for Training\nThe code for fine-tuning the model can be found in this repo: URL",
"## Trainin... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## About\n'Distilgpt2' model finetuned on a dataset of inspirational/motivational quotes taken from the Quotes-500K dataset. The model can generate i... |
token-classification | flair |
## Portuguese Name Identification
The [NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) paper contains Flair-based models for Portuguese Language, initialized with [Flair BBP](https://githu... | {"language": "pt", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "FISIOTERAPIA TRAUMATO - MANH\u00c3 Henrique Dias, 38 anos. Exerc\u00edcios metab\u00f3licos de extremidades inferiores. Realizo mobiliza\u00e7\u00e3o patelar e leve mobiliza\u00e7\u00e3o de fle... | noharm-ai/anony | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"pt",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #pt #license-mit #region-us
|
## Portuguese Name Identification
The NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier paper contains Flair-based models for Portuguese Language, initialized with Flair BBP & trained on clinical notes with names tagged.
### Demo: How to use in Flair... | [
"## Portuguese Name Identification\r\n\r\nThe NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier paper contains Flair-based models for Portuguese Language, initialized with Flair BBP & trained on clinical notes with names tagged.",
"### Demo: How to use ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pt #license-mit #region-us \n",
"## Portuguese Name Identification\r\n\r\nThe NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier paper contains Flair-based models for Portuguese Languag... |
text2text-generation | transformers | # Generate News in Thai language by keywords.
MODEL_NAME = 'nonamenlp/news_gen'
TOKENIZER_NAME = "nonamenlp/news_gen"
trained_model = MT5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict=True)
tokenizer = T5Tokenizer.from_pretrained(TOKENIZER_NAME) | {} | nonamenlp/thai_new_gen_from_kw | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Generate News in Thai language by keywords.
MODEL_NAME = 'nonamenlp/news_gen'
TOKENIZER_NAME = "nonamenlp/news_gen"
trained_model = MT5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict=True)
tokenizer = T5Tokenizer.from_pretrained(TOKENIZER_NAME) | [
"# Generate News in Thai language by keywords.\n\nMODEL_NAME = 'nonamenlp/news_gen' \n\nTOKENIZER_NAME = \"nonamenlp/news_gen\"\n\ntrained_model = MT5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict=True)\n\ntokenizer = T5Tokenizer.from_pretrained(TOKENIZER_NAME)"
] | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Generate News in Thai language by keywords.\n\nMODEL_NAME = 'nonamenlp/news_gen' \n\nTOKENIZER_NAME = \"nonamenlp/news_gen\"\n\ntrained_model = MT5ForConditionalG... |
text-generation | transformers |
# astley talks | {"tags": ["conversational"]} | noobed/DialoGPT-small-astley | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# astley talks | [
"# astley talks"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# astley talks"
] |
text-generation | transformers |
# mingbot DialoGPT Model | {"tags": ["conversational"]} | norie4/DialoGPT-small-kyutebot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mingbot DialoGPT Model | [
"# mingbot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mingbot DialoGPT Model"
] |
text-generation | transformers |
# mremoji DialoGPT Model | {"tags": ["conversational"]} | norie4/DialoGPT-small-memoji | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# mremoji DialoGPT Model | [
"# mremoji DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# mremoji DialoGPT Model"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [Vivos dataset](https://ailab.hcmus.edu.vn/vivos) and [FOSD dataset](https://data.mendeley.com/... | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "vivos"], "metrics": ["wer"], "model-index": [{"name": "Ted Vietnamese XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "na... | not-tanh/wav2vec2-large-xlsr-53-vietnamese | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"vi",
"dataset:common_voice",
"dataset:vivos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #dataset-common_voice #dataset-vivos #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Vivos dataset and FOSD 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:
## Eval... | [
"# Wav2Vec2-Large-XLSR-53-Vietnamese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Vivos dataset and FOSD dataset.\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... | [
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"# Wav2Vec2-Large-XLSR-53-Vietnamese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietname... |
text-generation | transformers |
# 7evenpool DialoGPT Model | {"tags": ["conversational"]} | not7even/DialoGPT-small-7evenpool | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 7evenpool DialoGPT Model | [
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"# 7evenpool DialoGPT Model"
] |
text2text-generation | 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. -->
# cover-letter-t5-base
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on cover letter samples scr... | {"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer", "t5-base"], "widget": [{"text": "coverletter name: Nouamane Tazi job: Machine Learning Engineer at HuggingFace background: Master's student in AI at the University of Paris Saclay experiences: I participated in the Digital Tech Year program,... | nouamanetazi/cover-letter-t5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"t5-base",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #t5-base #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# cover-letter-t5-base
This model is a fine-tuned version of t5-base on cover letter samples scraped from Indeed and JobHero.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# cover-letter-t5-base\n\nThis model is a fine-tuned version of t5-base on cover letter samples scraped from Indeed and JobHero.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #t5-base #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# cover-letter-t5-base\n\nThis model is a fine-tuned version of t5-base on cover letter samples scrape... |
automatic-speech-recognition | 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-xls-r-300m-ar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["ar"], "license": "apache-2.0", "tags": ["ar", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "XLS-R-300M - Arabic", "results": [{"task": {"type": "automatic-speech-recognition",... | nouamanetazi/wav2vec2-xls-r-300m-ar-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-ar
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch... | [
"# wav2vec2-xls-r-300m-ar\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - AR dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.0191\n- eval_wer: 1.0\n- eval_runtime: 252.2389\n- eval_samples_per_second: 30.217\n- eval_steps_per_second: 0.4... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-ar\n\nThis model is a fine-tuned version of f... |
automatic-speech-recognition | 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-xls-r-300m-ar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["ar"], "license": "apache-2.0", "tags": ["ar", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "XLS-R-300M - Arabic", "results": [{"task": {"type": "automatic-speech-recognition",... | nouamanetazi/wav2vec2-xls-r-300m-ar | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-ar
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch... | [
"# wav2vec2-xls-r-300m-ar\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - AR dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.0191\n- eval_wer: 1.0\n- eval_runtime: 252.2389\n- eval_samples_per_second: 30.217\n- eval_steps_per_second: 0.4... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-ar\n\nThis model is a fine-tuned version of f... |
token-classification | transformers |
# Hungarian named entity recognition model with OntoNotes5 + more entity types
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on NerKor+CARS-ONPP Corpus
## Limitations
- max_seq_length = 448
## Training data
The underlying corpus, [NerKor+CARS-OntoNotes++](https://github.com/ppke-nlpg/NYTK... | {"language": ["hu"], "license": "gpl", "tags": ["token-classification"], "metrics": ["F1"], "widget": [{"text": "A j\u00f3t\u00e9konys\u00e1gi szervezet \u00e1ltal id\u00e9zett Forbes-adatok szerint a vil\u00e1g t\u00edz leggazdagabb embere: Elon Musk (Tesla, SpaceX), Jeff Bezos (Amazon, Blue Origin), Bernard Arnault \... | novakat/nerkor-cars-onpp-hubert | null | [
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"safetensors",
"bert",
"token-classification",
"hu",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us
| Hungarian named entity recognition model with OntoNotes5 + more entity types
============================================================================
* Pretrained model used: SZTAKI-HLT/hubert-base-cc
* Finetuned on NerKor+CARS-ONPP Corpus
Limitations
-----------
* max\_seq\_length = 448
Training data
-----... | [
"### If you use this model, please cite:"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us \n",
"### If you use this model, please cite:"
] |
token-classification | transformers |
# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on NYTK-NerKor Corpus
## Limitations
- max_seq_length = 448
## See [https://huggingface.co/novakat/nerkor-cars-onpp-hubert](https://huggingface.co/novakat/nerko... | {"language": ["hu"], "license": "gpl", "tags": ["token-classification"], "metrics": ["F1"], "widget": [{"text": "A j\u00f3t\u00e9konys\u00e1gi szervezet \u00e1ltal id\u00e9zett Forbes-adatok szerint a vil\u00e1g t\u00edz leggazdagabb embere: Elon Musk (Tesla, SpaceX), Jeff Bezos (Amazon, Blue Origin), Bernard Arnault \... | novakat/nerkor-hubert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"hu",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us
|
# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on NYTK-NerKor Corpus
## Limitations
- max_seq_length = 448
## See URL for a much more elaborate Hungarian named entity model.
| [
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text-generation | null | # BART chatbot trained on [LIGHT](https://parl.ai/projects/light/) dataset with [Text Generative Adversarial Imitation Learning](https://arxiv.org/abs/2004.13796)
This model is intended to be used with [npc-engine](https://github.com/npc-engine/npc-engine).
It was based on [facebook/bart-large](https://huggingface.co... | {"language": "en", "license": "mit", "tags": ["conversational", "npc-engine"]} | npc-engine/exported-bart-light-gail-chatbot | null | [
"onnx",
"conversational",
"npc-engine",
"en",
"arxiv:2004.13796",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13796"
] | [
"en"
] | TAGS
#onnx #conversational #npc-engine #en #arxiv-2004.13796 #license-mit #region-us
| # BART chatbot trained on LIGHT dataset with Text Generative Adversarial Imitation Learning
This model is intended to be used with npc-engine.
It was based on facebook/bart-large. microsoft/deberta-base was used as an adversarial for GAIL stage.
| [
"# BART chatbot trained on LIGHT dataset with Text Generative Adversarial Imitation Learning\n\nThis model is intended to be used with npc-engine.\n\nIt was based on facebook/bart-large. microsoft/deberta-base was used as an adversarial for GAIL stage."
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text-to-speech | null | # Exported [FlowtronTTS](https://arxiv.org/abs/2005.05957) with [WaveGlow](https://arxiv.org/abs/1811.00002) vocoder
This model is intended to be used with [npc-engine](https://github.com/npc-engine/npc-engine).
Fork used for exporting https://github.com/npc-engine/flowtron
Original code https://github.com/NVIDIA/fl... | {"language": "en", "license": "mit", "tags": ["text-to-speech", "npc-engine"]} | npc-engine/exported-flowtron-waveglow-librispeech-tts | null | [
"onnx",
"text-to-speech",
"npc-engine",
"en",
"arxiv:2005.05957",
"arxiv:1811.00002",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05957",
"1811.00002"
] | [
"en"
] | TAGS
#onnx #text-to-speech #npc-engine #en #arxiv-2005.05957 #arxiv-1811.00002 #license-mit #region-us
| # Exported FlowtronTTS with WaveGlow vocoder
This model is intended to be used with npc-engine.
Fork used for exporting URL
Original code URL | [
"# Exported FlowtronTTS with WaveGlow vocoder\n\nThis model is intended to be used with npc-engine.\n\nFork used for exporting URL\n\nOriginal code URL"
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] |
null | null | # Exported [Nemo](https://github.com/NVIDIA/NeMo) models for Speech to Text with [OpenSLR 11](https://www.openslr.org/11/) librispeech 3-gram language model
This model is intended to be used with [npc-engine](https://github.com/npc-engine/npc-engine). | {"language": "en", "license": "mit", "tags": ["speech-to-text", "npc-engine"]} | npc-engine/exported-nemo-quartznet-ctc-stt | null | [
"onnx",
"speech-to-text",
"npc-engine",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#onnx #speech-to-text #npc-engine #en #license-mit #region-us
| # Exported Nemo models for Speech to Text with OpenSLR 11 librispeech 3-gram language model
This model is intended to be used with npc-engine. | [
"# Exported Nemo models for Speech to Text with OpenSLR 11 librispeech 3-gram language model\n\nThis model is intended to be used with npc-engine."
] | [
"TAGS\n#onnx #speech-to-text #npc-engine #en #license-mit #region-us \n",
"# Exported Nemo models for Speech to Text with OpenSLR 11 librispeech 3-gram language model\n\nThis model is intended to be used with npc-engine."
] |
sentence-similarity | null | # Export of [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
This model is intended to be used with [npc-engine](https://github.com/npc-engine/npc-engine).
| {"language": "en", "license": "mit", "tags": ["sentence-similarity", "npc-engine"]} | npc-engine/exported-paraphrase-MiniLM-L6-v2 | null | [
"onnx",
"sentence-similarity",
"npc-engine",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#onnx #sentence-similarity #npc-engine #en #license-mit #region-us
| # Export of sentence-transformers/paraphrase-MiniLM-L6-v2
This model is intended to be used with npc-engine.
| [
"# Export of sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis model is intended to be used with npc-engine."
] | [
"TAGS\n#onnx #sentence-similarity #npc-engine #en #license-mit #region-us \n",
"# Export of sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis model is intended to be used with npc-engine."
] |
feature-extraction | transformers | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 8 layers, 512 hidden unit size, and 8 attention heads. | {} | nreimers/BERT-Medium_L-8_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| This is the BERT-Medium model from Google: URL A BERT model with 8 layers, 512 hidden unit size, and 8 attention heads. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 4 layers, 256 hidden unit size, and 4 attention heads. | {} | nreimers/BERT-Mini_L-4_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| This is the BERT-Medium model from Google: URL A BERT model with 4 layers, 256 hidden unit size, and 4 attention heads. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # BERT-Small-L-4_H-512_A-8
This is a port of the [BERT-Small model](https://github.com/google-research/bert) to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads. | {} | nreimers/BERT-Small-L-4_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| # BERT-Small-L-4_H-512_A-8
This is a port of the BERT-Small model to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads. | [
"# BERT-Small-L-4_H-512_A-8\nThis is a port of the BERT-Small model to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads."
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] |
feature-extraction | transformers | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads. | {} | nreimers/BERT-Tiny_L-2_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us
| This is the BERT-Medium model from Google: URL A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
## MiniLM: 3 Layer Version
This is a 3 layer version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) by keeping only the layer [3, 7, 11]. | {"license": "mit"} | nreimers/MiniLM-L3-H384-uncased | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #license-mit #endpoints_compatible #region-us
|
## MiniLM: 3 Layer Version
This is a 3 layer version of microsoft/MiniLM-L12-H384-uncased by keeping only the layer [3, 7, 11]. | [
"## MiniLM: 3 Layer Version\r\n\r\nThis is a 3 layer version of microsoft/MiniLM-L12-H384-uncased by keeping only the layer [3, 7, 11]."
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"## MiniLM: 3 Layer Version\r\n\r\nThis is a 3 layer version of microsoft/MiniLM-L12-H384-uncased by keeping only the layer [3, 7, 11]."
] |
feature-extraction | transformers |
## MiniLM: 6 Layer Version
This is a 6 layer version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) by keeping only every second layer. | {"license": "mit"} | nreimers/MiniLM-L6-H384-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #license-mit #endpoints_compatible #has_space #region-us
|
## MiniLM: 6 Layer Version
This is a 6 layer version of microsoft/MiniLM-L12-H384-uncased by keeping only every second layer. | [
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] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
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fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Base | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
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fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
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] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Base | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
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] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] |
feature-extraction | transformers | This is the [General_TinyBERT_v2(4layer-312dim)](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT) ported to Huggingface transformers. | {} | nreimers/TinyBERT_L-4_H-312_v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| This is the General_TinyBERT_v2(4layer-312dim) ported to Huggingface transformers. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This is the [General_TinyBERT_v2(6layer-768dim)](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT) ported to Huggingface transformers. | {} | nreimers/TinyBERT_L-6_H-768_v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| This is the General_TinyBERT_v2(6layer-768dim) ported to Huggingface transformers. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # albert-small-v2
This is a 6 layer version of [albert-base-v2](https://huggingface.co/albert-base-v2). | {} | nreimers/albert-small-v2 | null | [
"transformers",
"pytorch",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #feature-extraction #endpoints_compatible #region-us
| # albert-small-v2
This is a 6 layer version of albert-base-v2. | [
"# albert-small-v2\n\nThis is a 6 layer version of albert-base-v2."
] | [
"TAGS\n#transformers #pytorch #albert #feature-extraction #endpoints_compatible #region-us \n",
"# albert-small-v2\n\nThis is a 6 layer version of albert-base-v2."
] |
fill-mask | transformers | # Multilingual MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual MiniLMv2
This is a MiniLMv2 model from: URL | [
"# Multilingual MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual MiniLMv2\nThis is a MiniLMv2 model from: URL"
] |
fill-mask | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | {} | nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLMv2
This is a MiniLMv2 model from: URL | [
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLMv2\nThis is a MiniLMv2 model from: URL"
] |
text-classification | transformers |
# Mobile App Classification
## Model description
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. The model can handle input sequence of length up to 4,096 tokens.
The [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-r... | {"language": "en", "license": "mit", "tags": ["big_bird", "pytorch", "text-classification", "mobile app descriptions", "playstore"], "thumbnail": "https://huggingface.co/nsi319", "inference": true} | nsi319/bigbird-roberta-base-finetuned-app | null | [
"transformers",
"pytorch",
"big_bird",
"text-classification",
"mobile app descriptions",
"playstore",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #big_bird #text-classification #mobile app descriptions #playstore #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Mobile App Classification
## Model description
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. The model can handle input sequence of length up to 4,096 tokens.
The google/bigbird-roberta-base model is fine-tuned to classify an mobile... | [
"# Mobile App Classification",
"## Model description\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. The model can handle input sequence of length up to 4,096 tokens.\n\nThe google/bigbird-roberta-base model is fine-tuned to classi... | [
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"# Mobile App Classification",
"## Model description\n\nBigBird, is a sparse-attention based transformer which extends Transformer based m... |
text-classification | transformers |
# Mobile App Classification
## Model description
DistilBERT is a transformer model, smaller and faster than BERT, which was pre-trained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher.
The [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model is fine... | {"language": "en", "license": "mit", "tags": ["distilbert", "pytorch", "text-classification", "mobile app descriptions", "playstore"], "thumbnail": "https://huggingface.co/nsi319", "inference": true} | nsi319/distilbert-base-uncased-finetuned-app | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"mobile app descriptions",
"playstore",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #mobile app descriptions #playstore #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Mobile App Classification
## Model description
DistilBERT is a transformer model, smaller and faster than BERT, which was pre-trained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher.
The distilbert-base-uncased model is fine-tuned to classify an mobile app description into ... | [
"# Mobile App Classification",
"## Model description\n\nDistilBERT is a transformer model, smaller and faster than BERT, which was pre-trained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher.\n\nThe distilbert-base-uncased model is fine-tuned to classify an mobile app descr... | [
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"# Mobile App Classification",
"## Model description\n\nDistilBERT is a transformer model, smaller and faster than BERT, which was pre-t... |
summarization | transformers |
## LED for legal summarization of documents
This is a Longformer Encoder Decoder ([led-base-16384](https://huggingface.co/allenai/led-base-16384)) model for the **legal domain**, trained for **long document abstractive summarization** task. The length of the document can be upto 16,384 tokens.
## Training data
The *... | {"language": "en", "license": "mit", "tags": "summarization", "metrics": ["rouge", "precision"], "inference": false} | nsi319/legal-led-base-16384 | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"summarization",
"en",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #led #text2text-generation #summarization #en #license-mit #autotrain_compatible #has_space #region-us
| LED for legal summarization of documents
----------------------------------------
This is a Longformer Encoder Decoder (led-base-16384) model for the legal domain, trained for long document abstractive summarization task. The length of the document can be upto 16,384 tokens.
Training data
-------------
The legal-... | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #summarization #en #license-mit #autotrain_compatible #has_space #region-us \n"
] |
summarization | transformers |
## PEGASUS for legal document summarization
**legal-pegasus** is a finetuned version of ([**google/pegasus-cnn_dailymail**](https://huggingface.co/google/pegasus-cnn_dailymail)) for the **legal domain**, trained to perform **abstractive summarization** task. The maximum length of input sequence is 1024 tokens.
## Tra... | {"language": "en", "license": "mit", "tags": "summarization", "metrics": ["rouge", "precision"], "inference": false} | nsi319/legal-pegasus | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"en",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #license-mit #autotrain_compatible #has_space #region-us
| PEGASUS for legal document summarization
----------------------------------------
legal-pegasus is a finetuned version of (google/pegasus-cnn\_dailymail) for the legal domain, trained to perform abstractive summarization task. The maximum length of input sequence is 1024 tokens.
Training data
-------------
This m... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #license-mit #autotrain_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# Mobile App Classification
## Model description
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving lo... | {"language": "en", "license": "mit", "tags": ["xlnet", "pytorch", "text-classification", "mobile app descriptions", "playstore"], "thumbnail": "https://huggingface.co/nsi319", "inference": true} | nsi319/xlnet-base-cased-finetuned-app | null | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"mobile app descriptions",
"playstore",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xlnet #text-classification #mobile app descriptions #playstore #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Mobile App Classification
## Model description
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving lo... | [
"# Mobile App Classification",
"## Model description\n\nXLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks in... | [
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"# Mobile App Classification",
"## Model description\n\nXLNet is a new unsupervised language representation learning method based on a novel ... |
null | null | kk | {} | ntest/mmm | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| kk | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | ntjrrvarma/DialoGPT-small-RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
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"# Rick DialoGPT Model"
] |
automatic-speech-recognition | transformers | pretrain | {} | ntp0102/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| pretrain | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# DistilHuBERT
[DistilHuBERT by NTU Speech Processing & Machine Learning Lab](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not ha... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | ntu-spml/distilhubert | null | [
"transformers",
"pytorch",
"safetensors",
"hubert",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #hubert #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DistilHuBERT
DistilHuBERT by NTU Speech Processing & Machine Learning Lab
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this mo... | [
"# DistilHuBERT\n\nDistilHuBERT by NTU Speech Processing & Machine Learning Lab\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to u... | [
"TAGS\n#transformers #pytorch #safetensors #hubert #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DistilHuBERT\n\nDistilHuBERT by NTU Speech Processing & Machine Learning Lab\n\nThe base model pretrained on 16kHz s... |
null | null | gt | {} | nurbek/nu | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| gt | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897
## Validation Metrics
- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.92266... | {"language": "en", "tags": "autonlp", "datasets": ["nurkayevaa/autonlp-data-bert-covid"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 9.72797586719897} | nurkayevaa/autonlp-bert-covid-407910458 | null | [
"transformers",
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"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:nurkayevaa/autonlp-data-bert-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897
## Validation Metrics
- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.92266... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 407910458\n- CO2 Emissions (in grams): 9.72797586719897",
"## Validation Metrics\n\n- Loss: 0.20907048881053925\n- Accuracy: 0.9119825708061002\n- Precision: 0.8912721893491125\n- Recall: 0.9563492063492064\n- AUC: 0.969845487309... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 407910458\n- CO2 Emissions (in ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 407910467
- CO2 Emissions (in grams): 10.719439124704492
## Validation Metrics
- Loss: 0.12029844522476196
- Accuracy: 0.9516339869281045
- Precision: 0.9477786438035853
- Recall: 0.9650793650793651
- AUC: 0.9907376734912967
- F1: 0.956... | {"language": "en", "tags": "autonlp", "datasets": ["nurkayevaa/autonlp-data-bert-covid"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.719439124704492} | nurkayevaa/autonlp-bert-covid-407910467 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:nurkayevaa/autonlp-data-bert-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-nurkayevaa/autonlp-data-bert-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 407910467
- CO2 Emissions (in grams): 10.719439124704492
## Validation Metrics
- Loss: 0.12029844522476196
- Accuracy: 0.9516339869281045
- Precision: 0.9477786438035853
- Recall: 0.9650793650793651
- AUC: 0.9907376734912967
- F1: 0.956... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 407910467\n- CO2 Emissions (in grams): 10.719439124704492",
"## Validation Metrics\n\n- Loss: 0.12029844522476196\n- Accuracy: 0.9516339869281045\n- Precision: 0.9477786438035853\n- Recall: 0.9650793650793651\n- AUC: 0.9907376734... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-nurkayevaa/autonlp-data-bert-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 407910467\n- CO2 Emissions (in gra... |
null | null | <!---
# ##############################################################################################
#
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... | {} | nvidia/megatron-bert-cased-345m | null | [
"arxiv:1909.08053",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.08053"
] | [] | TAGS
#arxiv-1909.08053 #has_space #region-us
|
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a bidirectional transformer in the style of BERT with text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. This model contains 345 million parameters... | [
"# How to run Megatron BERT using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands below, you'll have to clone 'Transformers'.",
"## Ge... | [
"TAGS\n#arxiv-1909.08053 #has_space #region-us \n",
"# How to run Megatron BERT using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands ... |
null | null | <!---
# ##############################################################################################
#
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... | {} | nvidia/megatron-bert-uncased-345m | null | [
"arxiv:1909.08053",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.08053"
] | [] | TAGS
#arxiv-1909.08053 #has_space #region-us
|
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a bidirectional transformer in the style of BERT with text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. This model contains 345 million parameters... | [
"# How to run Megatron BERT using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands below, you'll have to clone 'Transformers'.",
"## Ge... | [
"TAGS\n#arxiv-1909.08053 #has_space #region-us \n",
"# How to run Megatron BERT using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands ... |
null | null | <!---
# ##############################################################################################
#
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... | {} | nvidia/megatron-gpt2-345m | null | [
"arxiv:1909.08053",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.08053"
] | [] | TAGS
#arxiv-1909.08053 #region-us
|
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a generative, left-to-right transformer in the style of GPT-2. This model was trained on text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. It cont... | [
"# How to run Megatron GPT2 using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands below, you'll have to clone 'Transformers'.",
"## Ge... | [
"TAGS\n#arxiv-1909.08053 #region-us \n",
"# How to run Megatron GPT2 using Transformers",
"## Prerequisites \n\nIn that guide, we run all the commands from a folder called '$MYDIR' and defined as (in 'bash'):\n\n\n\nFeel free to change the location at your convenience.\n\nTo run some of the commands below, you'... |
image-classification | transformers |
# SegFormer (b0-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b0 | null | [
"transformers",
"pytorch",
"tf",
"segformer",
"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# SegFormer (b0-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b0-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b0-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced... |
image-classification | transformers |
# SegFormer (b1-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b1 | null | [
"transformers",
"pytorch",
"tf",
"segformer",
"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# SegFormer (b1-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b1-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b1-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced... |
image-classification | transformers |
# SegFormer (b2-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b2 | null | [
"transformers",
"pytorch",
"tf",
"segformer",
"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# SegFormer (b2-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b2-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b2-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced... |
image-classification | transformers |
# SegFormer (b3-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b3 | null | [
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"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# SegFormer (b3-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b3-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# SegFormer (b3-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the pap... |
image-classification | transformers |
# SegFormer (b4-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b4 | null | [
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"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# SegFormer (b4-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b4-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# SegFormer (b4-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the pap... |
image-classification | transformers |
# SegFormer (b5-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NV... | {"license": "other", "tags": ["vision"], "datasets": ["imagenet_1k"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_0... | nvidia/mit-b5 | null | [
"transformers",
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"tf",
"segformer",
"image-classification",
"vision",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# SegFormer (b5-sized) encoder pre-trained-only
SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegFormer did not write a... | [
"# SegFormer (b5-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing SegFormer did not... | [
"TAGS\n#transformers #pytorch #tf #segformer #image-classification #vision #dataset-imagenet_1k #arxiv-2105.15203 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b5-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced... |
null | null | <!---
Copyright 2021 NVIDIA Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | {} | nvidia/qdqbert-base-uncased | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# QDQBERT base model (uncased)
## Model description
QDQBERT model inserts fake quantization operations (pair of QuantizeLinear/DequantizeLinear operators) to (i) linear layer inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
QDQBERT model can be loaded from any checkpoint of HuggingF... | [
"# QDQBERT base model (uncased)",
"## Model description\nQDQBERT model inserts fake quantization operations (pair of QuantizeLinear/DequantizeLinear operators) to (i) linear layer inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.\n\nQDQBERT model can be loaded from any checkpoint o... | [
"TAGS\n#region-us \n",
"# QDQBERT base model (uncased)",
"## Model description\nQDQBERT model inserts fake quantization operations (pair of QuantizeLinear/DequantizeLinear operators) to (i) linear layer inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.\n\nQDQBERT model can be loa... |
image-segmentation | transformers |
# SegFormer (b0-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b0-finetuned-ade-512-512 | null | [
"transformers",
"pytorch",
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"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2105.15203",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b0-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b0-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b0-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was ... |
image-segmentation | transformers |
# SegFormer (b0-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b0-finetuned-cityscapes-1024-1024 | null | [
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"dataset:cityscapes",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
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|
# SegFormer (b0-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b0-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
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"# SegFormer (b0-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
image-segmentation | transformers |
# SegFormer (b4-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposit... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "road"}]} | nvidia/segformer-b0-finetuned-cityscapes-512-1024 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
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|
# SegFormer (b4-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing... | [
"# SegFormer (b4-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team r... | [
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"# SegFormer (b4-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduce... |
image-segmentation | transformers |
# SegFormer (b5-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 640x1280. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposit... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "road"}]} | nvidia/segformer-b0-finetuned-cityscapes-640-1280 | null | [
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
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|
# SegFormer (b5-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 640x1280. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing... | [
"# SegFormer (b5-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 640x1280. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team r... | [
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"# SegFormer (b5-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 640x1280. It was introduce... |
image-segmentation | transformers |
# SegFormer (b0-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 768x768. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposito... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b0-finetuned-cityscapes-768-768 | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b0-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 768x768. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing ... | [
"# SegFormer (b0-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 768x768. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team re... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b0-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 768x768. It was introduced... |
image-segmentation | transformers |
# SegFormer (b1-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b1-finetuned-ade-512-512 | null | [
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"arxiv:2105.15203",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #region-us
|
# SegFormer (b1-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b1-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #region-us \n",
"# SegFormer (b1-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper ... |
image-segmentation | transformers |
# SegFormer (b1-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b1-finetuned-cityscapes-1024-1024 | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b1-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b1-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b1-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
image-segmentation | transformers |
# SegFormer (b2-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b2-finetuned-ade-512-512 | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b2-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b2-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b2-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in... |
image-segmentation | transformers |
# SegFormer (b2-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b2-finetuned-cityscapes-1024-1024 | null | [
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"dataset:cityscapes",
"arxiv:2105.15203",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b2-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b2-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b2-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
image-segmentation | transformers |
# SegFormer (b3-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b3-finetuned-ade-512-512 | null | [
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"dataset:scene_parse_150",
"arxiv:2105.15203",
"license:other",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b3-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b3-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b3-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in... |
image-segmentation | transformers |
# SegFormer (b3-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b3-finetuned-cityscapes-1024-1024 | null | [
"transformers",
"pytorch",
"tf",
"segformer",
"vision",
"image-segmentation",
"dataset:cityscapes",
"arxiv:2105.15203",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b3-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b3-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b3-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
image-segmentation | transformers |
# SegFormer (b4-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b4-finetuned-ade-512-512 | null | [
"transformers",
"pytorch",
"tf",
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"vision",
"image-segmentation",
"dataset:scene_parse_150",
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"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b4-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b4-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b4-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in... |
image-segmentation | transformers |
# SegFormer (b4-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b4-finetuned-cityscapes-1024-1024 | null | [
"transformers",
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"dataset:cityscapes",
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] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
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#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b4-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b4-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b4-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
image-segmentation | transformers |
# SegFormer (b5-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](http... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | nvidia/segformer-b5-finetuned-ade-640-640 | null | [
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"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2105.15203",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b5-sized) model fine-tuned on ADE20k
SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasing SegForme... | [
"# SegFormer (b5-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team releasing ... | [
"TAGS\n#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us \n",
"# SegFormer (b5-sized) model fine-tuned on ADE20k\n\nSegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in... |
image-segmentation | transformers |
# SegFormer (b5-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposi... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["cityscapes"], "widget": [{"src": "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png", "example_title": "Road"}]} | nvidia/segformer-b5-finetuned-cityscapes-1024-1024 | null | [
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"vision",
"image-segmentation",
"dataset:cityscapes",
"arxiv:2105.15203",
"license:other",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #tf #segformer #vision #image-segmentation #dataset-cityscapes #arxiv-2105.15203 #license-other #endpoints_compatible #has_space #region-us
|
# SegFormer (b5-sized) model fine-tuned on CityScapes
SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
Disclaimer: The team releasin... | [
"# SegFormer (b5-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. \n\nDisclaimer: The team ... | [
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"# SegFormer (b5-sized) model fine-tuned on CityScapes\n\nSegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduc... |
question-answering | transformers | Suggest under 1k character | {} | nvkha/bert-qa-vi | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| Suggest under 1k character | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | 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-large-xlsr-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hindi-colab", "results": []}]} | nvshubhsharma/wav2vec2-large-xlsr-hindi-colab | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# wav2vec2-large-xlsr-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice... |
text-generation | transformers | # DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script d... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | nytestalkerq/DialoGPT-medium-joshua | null | [
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"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nChat with the model:"
] | [
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fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-100M-1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-100M-2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-100M-3 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-10M-1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-10M-2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-10M-3 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-1B-1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-1B-2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-base-1B-3 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-med-small-1M-1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-med-small-1M-2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes... |
fill-mask | transformers | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... | {} | nyu-mll/roberta-med-small-1M-3 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| RoBERTa Pretrained on Smaller Datasets
======================================
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: W... | [
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to model sizes mentioned above are as follows:\n\n\n\n(AH = number of attention heads; HS = hidden size; FFN = feedforward network ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Hyperparameters and Validation Perplexity\n\n\nThe hyperparameters and validation perplexities corresponding to each model are as follows:\n\n\n\nThe hyperparameters corresponding to ... |
question-answering | transformers |
# BERT DRCD 384
This model is a fine-tune checkpoint of [bert-base-chinese](https://huggingface.co/bert-base-chinese), fine-tuned on DRCD dataset.
This model reaches a F1 score of 86.
This model reaches a EM score of 83.
Training Arguments:
- length: 384
- stride: 128
- learning_rate: 3e-5
- batch_size: 10
- e... | {"language": "zh-tw", "datasets": "DRCD", "tasks": "Question Answering"} | nyust-eb210/braslab-bert-drcd-384 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"dataset:DRCD",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-tw"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #dataset-DRCD #endpoints_compatible #region-us
|
# BERT DRCD 384
This model is a fine-tune checkpoint of bert-base-chinese, fine-tuned on DRCD dataset.
This model reaches a F1 score of 86.
This model reaches a EM score of 83.
Training Arguments:
- length: 384
- stride: 128
- learning_rate: 3e-5
- batch_size: 10
- epoch: 3
Colab for detailed
## Deployment
... | [
"# BERT DRCD 384\n\nThis model is a fine-tune checkpoint of bert-base-chinese, fine-tuned on DRCD dataset.\nThis model reaches a F1 score of 86.\nThis model reaches a EM score of 83.\n\nTraining Arguments:\n\n- length: 384\n\n- stride: 128\n\n- learning_rate: 3e-5\n\n- batch_size: 10\n\n- epoch: 3\n\nColab for deta... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #dataset-DRCD #endpoints_compatible #region-us \n",
"# BERT DRCD 384\n\nThis model is a fine-tune checkpoint of bert-base-chinese, fine-tuned on DRCD dataset.\nThis model reaches a F1 score of 86.\nThis model reaches a EM score of 83.\n\nTraining Ar... |
text-generation | transformers |
# Harry Potter Dialogue GPT Oguz | {"tags": ["conversational"]} | oakkas/Dialge-small-harrypotter-oguz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 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 Dialogue GPT Oguz | [
"# Harry Potter Dialogue GPT Oguz"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter Dialogue GPT Oguz"
] |
text-classification | 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. -->
# financial_sentiment_model
This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/la... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["recall", "accuracy", "precision"], "model-index": [{"name": "financial_sentiment_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_ph... | oandreae/financial_sentiment_model | null | [
"transformers",
"pytorch",
"tensorboard",
"perceiver",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #perceiver #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| financial\_sentiment\_model
===========================
This model is a fine-tuned version of deepmind/language-perceiver on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3467
* Recall: 0.8840
* Accuracy: 0.8804
* Precision: 0.8604
Model description
--------... | [
"### 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* distributed\\_type: tpu\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\... | [
"TAGS\n#transformers #pytorch #tensorboard #perceiver #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
token-classification | transformers |
# Model Description
* A ClinicalBERT [[Alsentzer et al., 2019]](https://arxiv.org/pdf/1904.03323.pdf) model fine-tuned for de-identification of medical notes.
* Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected heal... | {"language": ["en"], "license": "mit", "tags": ["deidentification", "medical notes", "ehr", "phi"], "datasets": ["I2B2"], "metrics": ["F1", "Recall", "AUC"], "thumbnail": "https://www.onebraveidea.org/wp-content/uploads/2019/07/OBI-Logo-Website.png", "widget": [{"text": "Physician Discharge Summary Admit date: 10/12/19... | obi/deid_bert_i2b2 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"deidentification",
"medical notes",
"ehr",
"phi",
"en",
"dataset:I2B2",
"arxiv:1904.03323",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.03323"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #deidentification #medical notes #ehr #phi #en #dataset-I2B2 #arxiv-1904.03323 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Description
=================
* A ClinicalBERT [[Alsentzer et al., 2019]](URL model fine-tuned for de-identification of medical notes.
* Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information cate... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #deidentification #medical notes #ehr #phi #en #dataset-I2B2 #arxiv-1904.03323 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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