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fill-mask | transformers |
## KoRean based ELECTRA (KR-ELECTRA)
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such as review documents, while still... | {"language": ["ko"]} | snunlp/KR-ELECTRA-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us
| KoRean based ELECTRA (KR-ELECTRA)
---------------------------------
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such a... | [
"### Released Model\n\n\nWe pre-trained our KR-ELECTRA model following a base-scale model of ELECTRA. We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform.",
"#### Model Details\n\n\nWe followed the training parameters of the base-scale model of ELECTRA.",
"##### Hyperparameters... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### Released Model\n\n\nWe pre-trained our KR-ELECTRA model following a base-scale model of ELECTRA. We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform.",
"#### M... |
text-classification | transformers |
# KR-FinBert & KR-FinBert-SC
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for the financial domain by... | {"language": ["ko"]} | snunlp/KR-FinBert-SC | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #has_space #region-us
| KR-FinBert & KR-FinBert-SC
==========================
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for... | [
"### Sentimental Classification model\n\n\nDownstream task performances with 50,000 labeled data.",
"### Inference sample"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Sentimental Classification model\n\n\nDownstream task performances with 50,000 labeled data.",
"### Inference sample"
] |
fill-mask | transformers |
# KR-FinBert & KR-FinBert-SC
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for the financial domain by... | {"language": ["ko"]} | snunlp/KR-FinBert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us
|
# KR-FinBert & KR-FinBert-SC
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for the financial domain by... | [
"# KR-FinBert & KR-FinBert-SC\n\nMuch progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement. \nwe proposed KR-FinBert for the financial do... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# KR-FinBert & KR-FinBert-SC\n\nMuch progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning ... |
null | transformers |
# KR-BERT-MEDIUM
A pretrained Korean-specific BERT model developed by Computational Linguistics Lab at Seoul National University.
It is based on our character-level [KR-BERT](https://github.com/snunlp/KR-BERT) model which utilize WordPiece tokenizer.
Here, the model name has a suffix 'MEDIUM' since its training dat... | {"language": ["ko"]} | snunlp/KR-Medium | null | [
"transformers",
"pytorch",
"jax",
"bert",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #jax #bert #ko #endpoints_compatible #region-us
| KR-BERT-MEDIUM
==============
A pretrained Korean-specific BERT model developed by Computational Linguistics Lab at Seoul National University.
It is based on our character-level KR-BERT model which utilize WordPiece tokenizer.
Here, the model name has a suffix 'MEDIUM' since its training data grew from KR-BERT's ... | [
"### Vocab, Parameters and Data\n\n\n\n \n\nThe training data for this model is expanded from those of KR-BERT, texts from Korean Wikipedia, and news articles, by addition of legal texts crawled from the National Law Information Center and Korean Comments dataset. This data expansion is to collect texts from more ... | [
"TAGS\n#transformers #pytorch #jax #bert #ko #endpoints_compatible #region-us \n",
"### Vocab, Parameters and Data\n\n\n\n \n\nThe training data for this model is expanded from those of KR-BERT, texts from Korean Wikipedia, and news articles, by addition of legal texts crawled from the National Law Information C... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_ALL_A_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_ALL_B_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying
Our approach is described in our paper titled:
> Mishra, Sudhanshu, Shivangi Prasa... | {} | socialmediaie/TRAC2020_ALL_C_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying
Our approach is described in our paper titled:
> Mishra, Sudhanshu, Shivangi Prasa... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying\n\nOur approach is described in our paper titled: \n\n> Mishra, Sudhanshu, Shi... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_ENG_A_bert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_ENG_B_bert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_ENG_C_bert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_HIN_A_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_HIN_B_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_HIN_C_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_IBEN_A_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Works... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_IBEN_B_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-classification | transformers | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: ht... | {} | socialmediaie/TRAC2020_IBEN_C_bert-base-multilingual-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: UR... | [
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.\n\nOur trained models as well as evaluation metrics during traing are availab... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020\n\nModels and predictions for submission to TRAC - 2020... |
text-generation | transformers | The unexamined life is not worth living
| {} | socrates/socrates2.0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The unexamined life is not worth living
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
## Introduction
Download the model here:
* Catalan Roberta model: [julibert-2020-11-10.zip](https://www.softcatala.org/pub/softcatala/julibert/julibert-2020-11-10.zip)
## What's this?
Source code: https://github.com/Softcatala/julibert
* Corpus: Oscar Catalan Corpus (3,8G)
* Model type: Roberta
* Vocabulary size... | {"language": "ca"} | softcatala/julibert | null | [
"transformers",
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"jax",
"roberta",
"fill-mask",
"ca",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #ca #autotrain_compatible #endpoints_compatible #region-us
|
## Introduction
Download the model here:
* Catalan Roberta model: URL
## What's this?
Source code: URL
* Corpus: Oscar Catalan Corpus (3,8G)
* Model type: Roberta
* Vocabulary size: 50265
* Steps: 500000
| [
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"## What's this?\n\nSource code: URL\n\n* Corpus: Oscar Catalan Corpus (3,8G)\n* Model type: Roberta\n* Vocabulary size: 50265\n* Steps: 500000"
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"## What's this?\n\nSource code: URL\n\n* Corpus: Oscar Catalan Corpus (3,8G)\n* Model type: Roberta\n* Vocabula... |
translation | opennmt |
### Introduction
Catalan - German translation model for OpenNMT. These are the same models that we have in production at https://www.softcatala.org/traductor/.
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
```bash
pip3 install ctranslate2 pyonmttok
```
Simple tokenizat... | {"language": ["ca", "de"], "license": "mit", "library_name": "opennmt", "tags": ["translation"], "metrics": ["bleu"], "inference": false} | softcatala/opennmt-cat-deu | null | [
"opennmt",
"translation",
"ca",
"de",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca",
"de"
] | TAGS
#opennmt #translation #ca #de #license-mit #region-us
| ### Introduction
Catalan - German translation model for OpenNMT. These are the same models that we have in production at URL
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
Simple tokenization & translation using Python:
Benchmarks
----------
Additional information
... | [
"### Introduction\n\n\nCatalan - German translation model for OpenNMT. These are the same models that we have in production at URL\nThe models are quantified for low latency.",
"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple tokenization & translation using Python:\n\n\nBenchmarks\n----------\n\n\... | [
"TAGS\n#opennmt #translation #ca #de #license-mit #region-us \n",
"### Introduction\n\n\nCatalan - German translation model for OpenNMT. These are the same models that we have in production at URL\nThe models are quantified for low latency.",
"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple token... |
translation | opennmt |
### Introduction
Catalan - English translation model for OpenNMT. These are the same models that we have in production at https://www.softcatala.org/traductor/.
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
```bash
pip3 install ctranslate2 pyonmttok
```
Simple tokeniza... | {"language": ["ca", "en"], "license": "mit", "library_name": "opennmt", "tags": ["translation"], "metrics": ["bleu"], "inference": false} | softcatala/opennmt-cat-eng | null | [
"opennmt",
"translation",
"ca",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca",
"en"
] | TAGS
#opennmt #translation #ca #en #license-mit #region-us
| ### Introduction
Catalan - English translation model for OpenNMT. These are the same models that we have in production at URL
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
Simple tokenization & translation using Python:
Benchmarks
----------
Additional information... | [
"### Introduction\n\n\nCatalan - English translation model for OpenNMT. These are the same models that we have in production at URL\nThe models are quantified for low latency.",
"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple tokenization & translation using Python:\n\n\nBenchmarks\n----------\n\n... | [
"TAGS\n#opennmt #translation #ca #en #license-mit #region-us \n",
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"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple toke... |
translation | opennmt |
### Introduction
German - Catalan translation model for OpenNMT. These are the same models that we have in production at https://www.softcatala.org/traductor/.
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
```bash
pip3 install ctranslate2 pyonmttok
```
Simple tokenizat... | {"language": ["de", "ca"], "license": "mit", "library_name": "opennmt", "tags": ["translation"], "metrics": ["bleu"], "inference": false} | softcatala/opennmt-deu-cat | null | [
"opennmt",
"translation",
"de",
"ca",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"ca"
] | TAGS
#opennmt #translation #de #ca #license-mit #region-us
| ### Introduction
German - Catalan translation model for OpenNMT. These are the same models that we have in production at URL
The models are quantified for low latency.
### Usage
Install the necessary dependencies:
Simple tokenization & translation using Python:
Benchmarks
----------
Additional information
... | [
"### Introduction\n\n\nGerman - Catalan translation model for OpenNMT. These are the same models that we have in production at URL\nThe models are quantified for low latency.",
"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple tokenization & translation using Python:\n\n\nBenchmarks\n----------\n\n\... | [
"TAGS\n#opennmt #translation #de #ca #license-mit #region-us \n",
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"### Usage\n\n\nInstall the necessary dependencies:\n\n\nSimple token... |
translation | opennmt |
### Introduction
English - Catalan translation model based on OpenNMT. These are the same models that we have in production at https://www.softcatala.org/traductor/.
### Usage
```bash
pip3 install ctranslate2 pyonmttok
```
Simple translation using Python:
```python
import ctranslate2
from huggingface_hub impor... | {"language": ["ca", "en"], "license": "mit", "library_name": "opennmt", "tags": ["translation"], "metrics": ["bleu"], "inference": false} | softcatala/opennmt-eng-cat | null | [
"opennmt",
"translation",
"ca",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca",
"en"
] | TAGS
#opennmt #translation #ca #en #license-mit #region-us
| ### Introduction
English - Catalan translation model based on OpenNMT. These are the same models that we have in production at URL
### Usage
Simple translation using Python:
Simple tokenization & translation using Python:
Benchmarks
----------
| [
"### Introduction\n\n\nEnglish - Catalan translation model based on OpenNMT. These are the same models that we have in production at URL",
"### Usage\n\n\nSimple translation using Python:\n\n\nSimple tokenization & translation using Python:\n\n\nBenchmarks\n----------"
] | [
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"### Usage\n\n\nSimple translation using Python:\n\n\nSimple tokenization & translation using Python:\n\n\n... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-100k-VoxPopuli-Català
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
**Attention:... | {"language": "ca", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "speech-to-text"], "datasets": ["common_voice", "parlament_parla"], "metrics": ["wer"]} | softcatala/wav2vec2-large-100k-voxpopuli-catala | null | [
"transformers",
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
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"ca",
"dataset:common_voice",
"dataset:parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #speech-to-text #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-100k-VoxPopuli-Català
====================================
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli on Catalan language using the Common Voice and ParlamentParla datasets.
Attention: The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used co... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #speech-to-text #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Català
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
**Attention:** The split train/dev/t... | {"language": "ca", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "parlament_parla"], "metrics": ["wer"]} | softcatala/wav2vec2-large-xlsr-catala | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
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"dataset:common_voice",
"dataset:parlament_parla",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-XLSR-Català
==========================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Catalan language using the Common Voice and ParlamentParla datasets.
Attention: The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoic... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ca #dataset-common_voice #dataset-parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | # DPRContextEncoder for TriviaQA
## dpr-ctx_encoder-single-trivia-base
Dense Passage Retrieval (`DPR`)
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906), EMNLP 20... | {} | soheeyang/dpr-ctx_encoder-single-trivia-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906"
] | [] | TAGS
#transformers #pytorch #tf #dpr #arxiv-2004.04906 #endpoints_compatible #region-us
| DPRContextEncoder for TriviaQA
==============================
dpr-ctx\_encoder-single-trivia-base
-----------------------------------
Dense Passage Retrieval ('DPR')
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domai... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #arxiv-2004.04906 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # DPRQuestionEncoder for TriviaQA
## dpr-question_encoder-single-trivia-base
Dense Passage Retrieval (`DPR`)
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906), EM... | {} | soheeyang/dpr-question_encoder-single-trivia-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906"
] | [] | TAGS
#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2004.04906 #endpoints_compatible #region-us
| DPRQuestionEncoder for TriviaQA
===============================
dpr-question\_encoder-single-trivia-base
----------------------------------------
Dense Passage Retrieval ('DPR')
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval fo... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2004.04906 #endpoints_compatible #region-us \n"
] |
null | transformers | # rdr-ctx_encoder-single-nq-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while kee... | {} | soheeyang/rdr-ctx_encoder-single-nq-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"arxiv:2010.10999",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10999",
"2004.04906"
] | [] | TAGS
#transformers #pytorch #tf #dpr #arxiv-2010.10999 #arxiv-2004.04906 #endpoints_compatible #region-us
| rdr-ctx\_encoder-single-nq-base
===============================
Reader-Distilled Retriever ('RDR')
Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keepi... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #arxiv-2010.10999 #arxiv-2004.04906 #endpoints_compatible #region-us \n"
] |
null | transformers | # rdr-ctx_encoder-single-trivia-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while... | {} | soheeyang/rdr-ctx_encoder-single-trivia-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"arxiv:2010.10999",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10999"
] | [] | TAGS
#transformers #pytorch #tf #dpr #arxiv-2010.10999 #endpoints_compatible #region-us
| rdr-ctx\_encoder-single-trivia-base
===================================
Reader-Distilled Retriever ('RDR')
Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader whi... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #arxiv-2010.10999 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # rdr-question_encoder-single-nq-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader whil... | {} | soheeyang/rdr-question_encoder-single-nq-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"arxiv:2010.10999",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10999",
"2004.04906"
] | [] | TAGS
#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2010.10999 #arxiv-2004.04906 #endpoints_compatible #region-us
| rdr-question\_encoder-single-nq-base
====================================
Reader-Distilled Retriever ('RDR')
Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader w... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2010.10999 #arxiv-2004.04906 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # rdr-queston_encoder-single-nq-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while... | {} | soheeyang/rdr-question_encoder-single-trivia-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"arxiv:2010.10999",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10999"
] | [] | TAGS
#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2010.10999 #endpoints_compatible #region-us
| rdr-queston\_encoder-single-nq-base
===================================
Reader-Distilled Retriever ('RDR')
Sohee Yang and Minjoon Seo, Is Retriever Merely an Approximator of Reader?, arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader whi... | [] | [
"TAGS\n#transformers #pytorch #tf #dpr #feature-extraction #arxiv-2010.10999 #endpoints_compatible #region-us \n"
] |
text-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. -->
# chinese-bert-wwm-chinese_bert_wwm1
This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-chinese_bert_wwm1", "results": []}]} | soikit/chinese-bert-wwm-chinese_bert_wwm1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-bert-wwm-chinese\_bert\_wwm1
====================================
This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0009
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text-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. -->
# chinese-bert-wwm-chinese_bert_wwm3
This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-chinese_bert_wwm3", "results": []}]} | soikit/chinese-bert-wwm-chinese_bert_wwm3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-bert-wwm-chinese\_bert\_wwm3
====================================
This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text-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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | soikit/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
# Ryuji DialoGPT Model | {"tags": ["conversational"]} | solfer/DialoGPT-small-ryuji | 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
|
# Ryuji DialoGPT Model | [
"# Ryuji DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ryuji DialoGPT Model"
] |
null | null | # Darin AI | {} | sombochea/darin-ai | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Darin AI | [
"# Darin AI"
] | [
"TAGS\n#region-us \n",
"# Darin AI"
] |
fill-mask | transformers | Tensor-Flow Model using MASK token | {} | soniakris/Sonia_model | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Tensor-Flow Model using MASK token | [] | [
"TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# 日本語ByT5事前学習済みモデル
This is a [ByT5 (a tokenizer-free extension of the Text-to-Text Transfer Transformer)](https://github.com/google-research/byt5/) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったByT5 (a tokenizer-free extension of the Text-to-Text Transfer Transformer) モデルです。
* [Wikipedia](http... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["byt5", "t5", "text2text-generation", "seq2seq"], "datasets": ["wikipedia", "oscar", "cc100"]} | sonoisa/byt5-small-japanese | null | [
"transformers",
"pytorch",
"mt5",
"byt5",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #byt5 #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #endpoints_compatible #text-generation-inference #region-us
| 日本語ByT5事前学習済みモデル
================
This is a ByT5 (a tokenizer-free extension of the Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったByT5 (a tokenizer-free extension of the Text-to-Text Transfer Transformer) モデルです。
* Wikipediaの日本語ダンプデータ (2020年7月6日時点のもの)
* OSCARの... | [] | [
"TAGS\n#transformers #pytorch #mt5 #byt5 #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers |
# 日本語版[CLIP](https://github.com/openai/CLIP)モデル
This is a [CLIP](https://github.com/openai/CLIP) text/image encoder model for Japanese.
英語版CLIPモデルのテキストエンコーダーを一種の蒸留を用いて日本語化したモデルです。
作り方や精度、使い方、サンプルコードは下記の解説記事をご参照ください。
- 解説記事:
- 概要: [【日本語モデル付き】2022年にマルチモーダル処理をする人にお勧めしたい事前学習済みモデル](https://qiita.com/sonoisa/items/00e8... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["clip", "feature-extraction", "sentence-similarity"]} | sonoisa/clip-vit-b-32-japanese-v1 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"clip",
"sentence-similarity",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #feature-extraction #clip #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
# 日本語版CLIPモデル
This is a CLIP text/image encoder model for Japanese.
英語版CLIPモデルのテキストエンコーダーを一種の蒸留を用いて日本語化したモデルです。
作り方や精度、使い方、サンプルコードは下記の解説記事をご参照ください。
- 解説記事:
- 概要: 【日本語モデル付き】2022年にマルチモーダル処理をする人にお勧めしたい事前学習済みモデル
- 使い方の解説: 【日本語CLIP】画像とテキストの類似度計算、画像やテキストの埋め込み計算、類似画像検索
- (公開準備中) 応用解説: いらすとや画像のマルチモーダル検索（ゼロショット編）
- ... | [
"# 日本語版CLIPモデル\n\nThis is a CLIP text/image encoder model for Japanese.\n\n英語版CLIPモデルのテキストエンコーダーを一種の蒸留を用いて日本語化したモデルです。\n作り方や精度、使い方、サンプルコードは下記の解説記事をご参照ください。\n\n- 解説記事:\n - 概要: 【日本語モデル付き】2022年にマルチモーダル処理をする人にお勧めしたい事前学習済みモデル\n - 使い方の解説: 【日本語CLIP】画像とテキストの類似度計算、画像やテキストの埋め込み計算、類似画像検索\n - (公開準備中) 応用解説: いらすとや画像のマルチモーダル検索... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #clip #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# 日本語版CLIPモデル\n\nThis is a CLIP text/image encoder model for Japanese.\n\n英語版CLIPモデルのテキストエンコーダーを一種の蒸留を用いて日本語化したモデルです。\n作り方や精度、使い方、サンプルコードは下記の解説記事をご参照ください。\... |
feature-extraction | sentence-transformers |
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン2）です。
[バージョン1](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens)よりも良いロス関数である[MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)を用いて学習した改良版です。
手元の非公開データセットでは、バージョン1よりも1... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["sentence-transformers", "sentence-bert", "feature-extraction", "sentence-similarity"]} | sonoisa/sentence-bert-base-ja-mean-tokens-v2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"sentence-bert",
"feature-extraction",
"sentence-similarity",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#sentence-transformers #pytorch #bert #sentence-bert #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン2）です。
バージョン1よりも良いロス関数であるMultipleNegativesRankingLossを用いて学習した改良版です。
手元の非公開データセットでは、バージョン1よりも1.5〜2ポイントほど精度が高い結果が得られました。
事前学習済みモデルとしてcl-tohoku/bert-base-japanese-whole-word-maskingを利用しました。
従って、推論の実行にはfugashiとipadicが必要です（pip install fugashi ipadic）。
... | [
"# 旧バージョンの解説\n\nURL\n\nモデル名を\"sonoisa/sentence-bert-base-ja-mean-tokens-v2\"に書き換えれば、本モデルを利用した挙動になります。",
"# 使い方"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #sentence-bert #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# 旧バージョンの解説\n\nURL\n\nモデル名を\"sonoisa/sentence-bert-base-ja-mean-tokens-v2\"に書き換えれば、本モデルを利用した挙動になります。",
"# 使い方"
] |
feature-extraction | sentence-transformers |
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン1）です。
※: 精度が1.5ポイントほど向上した[バージョン2モデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)もあります。
# 解説
https://qiita.com/sonoisa/items/1df94d0a98cd4f209051
# 使い方
```python
from transformers import BertJapaneseTokenizer, BertModel
i... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["sentence-transformers", "sentence-bert", "feature-extraction", "sentence-similarity"]} | sonoisa/sentence-bert-base-ja-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"sentence-bert",
"feature-extraction",
"sentence-similarity",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#sentence-transformers #pytorch #sentence-bert #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン1）です。
※: 精度が1.5ポイントほど向上したバージョン2モデルもあります。
# 解説
URL
# 使い方
| [
"# 解説\n\nURL",
"# 使い方"
] | [
"TAGS\n#sentence-transformers #pytorch #sentence-bert #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# 解説\n\nURL",
"# 使い方"
] |
feature-extraction | sentence-transformers |
This is a Japanese sentence-T5 model.
日本語用Sentence-T5モデルです。
事前学習済みモデルとして[sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese)を利用しました。
推論の実行にはsentencepieceが必要です（pip install sentencepiece）。
手元の非公開データセットでは、精度は[sonoisa/sentence-bert-base-ja-mean-tokens](https://huggingface.co/sonoisa/sentence-b... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["sentence-transformers", "sentence-t5", "feature-extraction", "sentence-similarity"]} | sonoisa/sentence-t5-base-ja-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"t5",
"sentence-t5",
"feature-extraction",
"sentence-similarity",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#sentence-transformers #pytorch #t5 #sentence-t5 #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
This is a Japanese sentence-T5 model.
日本語用Sentence-T5モデルです。
事前学習済みモデルとしてsonoisa/t5-base-japaneseを利用しました。
推論の実行にはsentencepieceが必要です（pip install sentencepiece）。
手元の非公開データセットでは、精度はsonoisa/sentence-bert-base-ja-mean-tokensと同程度です。
# 使い方
| [
"# 使い方"
] | [
"TAGS\n#sentence-transformers #pytorch #t5 #sentence-t5 #feature-extraction #sentence-similarity #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# 使い方"
] |
text2text-generation | transformers |
# タイトルから記事本文を生成するモデル
SEE: https://qiita.com/sonoisa/items/a9af64ff641f0bbfed44 | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"]} | sonoisa/t5-base-japanese-article-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# タイトルから記事本文を生成するモデル
SEE: URL | [
"# タイトルから記事本文を生成するモデル\n\nSEE: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# タイトルから記事本文を生成するモデル\n\nSEE: URL"
] |
text2text-generation | transformers |
# 日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約890GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* [Wikipedia](https://ja.wikipedia.org)の日本語ダンプデータ (2020年7月6日時点のもの)
* [mC4](https://github.com/allenai/allennlp/discussions/5056)の日本語コーパス... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["wikipedia", "c4"]} | sonoisa/t5-base-japanese-mC4-Wikipedia | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:wikipedia",
"dataset:c4",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-c4 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| 日本語T5事前学習済みモデル
==============
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約890GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* Wikipediaの日本語ダンプデータ (2020年7月6日時点のもの)
* mC4の日本語コーパス（正確にはc4/multilingualのjaスプリット）
このモデルは事前学習のみを行なったものであり、特定のタスクに利用するにはファイ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-c4 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# 回答と回答が出てくるパラグラフを与えると質問文を生成するモデル
SEE: https://github.com/sonoisa/deep-question-generation
## 本モデルの作成ステップ概要
1. [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/)を日本語に機械翻訳し、不正なデータをクレンジング（有効なデータは約半分）。
回答が含まれるコンテキスト、質問文、解答の3つ組ができる。
2. [日本語T5モデル](https://huggingface.co/sonoisa/t5-base-japanese)を次の設定でファインチューニング... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "widget": [{"text": "answer: \u30a2\u30de\u30d3\u30a8 context: \u30a2\u30de\u30d3\u30a8\uff08\u6b74\u53f2\u7684\u4eee\u540d\u9063\uff1a\u30a2\u30de\u30d3\u30f1\uff09\u306f\u3001\u65e5\u672c\u306b\u4f1d\u308f\u308b\u534a\u4e... | sonoisa/t5-base-japanese-question-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# 回答と回答が出てくるパラグラフを与えると質問文を生成するモデル
SEE: URL
## 本モデルの作成ステップ概要
1. SQuAD 1.1を日本語に機械翻訳し、不正なデータをクレンジング（有効なデータは約半分）。
回答が含まれるコンテキスト、質問文、解答の3つ組ができる。
2. 日本語T5モデルを次の設定でファインチューニング
* 入力: "answer: {解答} content: {回答が含まれるコンテキスト}"
* 出力: "{質問文}"
* 各種ハイパーパラメータ
* 最大入力トークン数: 512
* 最大出力トークン数: 64
* 最適化アルゴリズム: AdaFact... | [
"# 回答と回答が出てくるパラグラフを与えると質問文を生成するモデル\n\nSEE: URL",
"## 本モデルの作成ステップ概要\n\n1. SQuAD 1.1を日本語に機械翻訳し、不正なデータをクレンジング（有効なデータは約半分）。 \n回答が含まれるコンテキスト、質問文、解答の3つ組ができる。\n2. 日本語T5モデルを次の設定でファインチューニング\n * 入力: \"answer: {解答} content: {回答が含まれるコンテキスト}\"\n * 出力: \"{質問文}\"\n * 各種ハイパーパラメータ\n * 最大入力トークン数: 512\n * 最大出力トークン数: 64\n ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# 回答と回答が出てくるパラグラフを与えると質問文を生成するモデル\n\nSEE: URL",
"## 本モデルの作成ステップ概要\n\n1. SQuAD 1.1を日本語に機械翻訳し、不正なデータをクレンジング（有効なデータは約半分）。 \n... |
text2text-generation | transformers |
# 記事本文からタイトルを生成するモデル
SEE: https://qiita.com/sonoisa/items/a9af64ff641f0bbfed44 | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"]} | sonoisa/t5-base-japanese-title-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 記事本文からタイトルを生成するモデル
SEE: URL | [
"# 記事本文からタイトルを生成するモデル\n\nSEE: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 記事本文からタイトルを生成するモデル\n\nSEE: URL"
] |
text2text-generation | transformers |
# 日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* [Wikipedia](https://ja.wikipedia.org)の日本語ダンプデータ (2020年7月6日時点のもの)
* [OSCAR](https://oscar-corpus.com)の日本語コーパス
* [CC-100](http://data.st... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["wikipedia", "oscar", "cc100"]} | sonoisa/t5-base-japanese | null | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"text2text-generation",
"seq2seq",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #t5 #feature-extraction #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #text-generation-inference #region-us
| 日本語T5事前学習済みモデル
==============
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* Wikipediaの日本語ダンプデータ (2020年7月6日時点のもの)
* OSCARの日本語コーパス
* CC-100の日本語コーパス
このモデルは事前学習のみを行なったものであり、特定のタスクに利用するにはファインチューニングする必... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #feature-extraction #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# 記事本文からタイトルを生成するモデル
SEE: https://qiita.com/sonoisa/items/30876467ad5a8a81821f | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"]} | sonoisa/t5-qiita-title-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# 記事本文からタイトルを生成するモデル
SEE: URL | [
"# 記事本文からタイトルを生成するモデル\n\nSEE: URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# 記事本文からタイトルを生成するモデル\n\nSEE: URL"
] |
null | transformers |
# 日本語VL-T5事前学習済みモデル
This is a VL-T5 (Unifying Vision-and-Language Tasks via Text Generation) model pretrained on Japanese corpus.
日本語コーパスを用いて事前学習を行ったVL-T5 (Unifying Vision-and-Language Tasks via Text Generation) モデルです。
- VL-T5の論文: https://arxiv.org/abs/2102.02779
- 推論例 (要Google Colab): https://colab.research.googl... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["vl-t5"], "datasets": ["wikipedia", "oscar", "cc100", "ms_coco", "visual_genome", "coco_captions", "vqa", "gqa"]} | sonoisa/vl-t5-base-japanese | null | [
"transformers",
"pytorch",
"t5",
"vl-t5",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"dataset:ms_coco",
"dataset:visual_genome",
"dataset:coco_captions",
"dataset:vqa",
"dataset:gqa",
"arxiv:2102.02779",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"text-generati... | null | 2022-03-02T23:29:05+00:00 | [
"2102.02779"
] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #vl-t5 #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #dataset-ms_coco #dataset-visual_genome #dataset-coco_captions #dataset-vqa #dataset-gqa #arxiv-2102.02779 #license-cc-by-sa-4.0 #endpoints_compatible #text-generation-inference #region-us
|
# 日本語VL-T5事前学習済みモデル
This is a VL-T5 (Unifying Vision-and-Language Tasks via Text Generation) model pretrained on Japanese corpus.
日本語コーパスを用いて事前学習を行ったVL-T5 (Unifying Vision-and-Language Tasks via Text Generation) モデルです。
- VL-T5の論文: URL
- 推論例 (要Google Colab): URL/日本語VL-T5推論.ipynb
| [
"# 日本語VL-T5事前学習済みモデル\n\nThis is a VL-T5 (Unifying Vision-and-Language Tasks via Text Generation) model pretrained on Japanese corpus.\n\n日本語コーパスを用いて事前学習を行ったVL-T5 (Unifying Vision-and-Language Tasks via Text Generation) モデルです。 \n\n- VL-T5の論文: URL\n- 推論例 (要Google Colab): URL/日本語VL-T5推論.ipynb"
] | [
"TAGS\n#transformers #pytorch #t5 #vl-t5 #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #dataset-ms_coco #dataset-visual_genome #dataset-coco_captions #dataset-vqa #dataset-gqa #arxiv-2102.02779 #license-cc-by-sa-4.0 #endpoints_compatible #text-generation-inference #region-us \n",
"# 日本語VL-T5事前学習済みモデル\n\nTh... |
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. -->
# xlm-roberta-large-finetuned-squad-v2
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlm-roberta-large-finetuned-squad-v2", "results": []}]} | sontn122/xlm-roberta-large-finetuned-squad-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| xlm-roberta-large-finetuned-squad-v2
====================================
This model is a fine-tuned version of xlm-roberta-large on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4627
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #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... |
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. -->
# xlm-roberta-large-finetuned-squad-v2_15102021
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/x... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlm-roberta-large-finetuned-squad-v2_15102021", "results": []}]} | sontn122/xlm-roberta-large-finetuned-squad-v2_15102021 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
|
# xlm-roberta-large-finetuned-squad-v2_15102021
This model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step... | [
"# xlm-roberta-large-finetuned-squad-v2_15102021\n\nThis model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 17.5548\n- eval_runtime: 168.7788\n- eval_samples_per_second: 23.368\n- eval_steps_per_second: 5.842\n- epoch: ... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us \n",
"# xlm-roberta-large-finetuned-squad-v2_15102021\n\nThis model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.\nIt achieves... |
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. -->
# xlm-roberta-large-finetuned-squad
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-l... | {"tags": ["generated_from_trainer"], "datasets": ["squad"]} | sontn122/xlm-roberta-large-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
| xlm-roberta-large-finetuned-squad
=================================
This model is a fine-tuned version of xlm-roberta-large on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0350
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad #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: 4\n* eval\\_ba... |
null | null | export enum PipelineType {
"text-generation"} | {} | soskok1288/Sas | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| export enum PipelineType {
"text-generation"} | [] | [
"TAGS\n#region-us \n"
] |
null | null | Aboba | {} | soskok1288/sberbank-hh | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Aboba | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers | hello
| {} | spacemanidol/neuralmagic-bert-squad-12layer-0sparse | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n"
] |
token-classification | spacy | ### Details: https://spacy.io/models/ca#ca_core_news_lg
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ca_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ca"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/ca_core_news_lg | null | [
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (317 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/ca#ca_core_news_md
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ca_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ca"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/ca_core_news_md | null | [
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (317 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/ca#ca_core_news_sm
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ca_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ca"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/ca_core_news_sm | null | [
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (317 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (317 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/ca#ca_core_news_trf
Catalan transformer pipeline (Transformer(name='projecte-aina/roberta-base-ca-v2', piece_encoder='byte-bpe', stride=112, type='roberta', width=768, window=144, vocab_size=50262)). Components: transformer, morphologizer, parser, ner, attribute_ruler, lemmatizer.
... | {"language": ["ca"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/ca_core_news_trf | null | [
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Catalan transformer pipeline (Transformer(name='projecte-aina/roberta-base-ca-v2', piece\_encoder='byte-bpe', stride=112, type='roberta', width=768, window=144, vocab\_size=50262)). Components: transformer, morphologizer, parser, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label sc... | [
"### Details: URL\n\n\nCatalan transformer pipeline (Transformer(name='projecte-aina/roberta-base-ca-v2', piece\\_encoder='byte-bpe', stride=112, type='roberta', width=768, window=144, vocab\\_size=50262)). Components: transformer, morphologizer, parser, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n... | [
"TAGS\n#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nCatalan transformer pipeline (Transformer(name='projecte-aina/roberta-base-ca-v2', piece\\_encoder='byte-bpe', stride=112, type='roberta', width=768, window=144, vocab\\_size=50262)). Components: transform... |
token-classification | spacy | ### Details: https://spacy.io/models/da#da_core_news_lg
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `da_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/da_core_news_lg | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (194 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 ... |
token-classification | spacy | ### Details: https://spacy.io/models/da#da_core_news_md
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `da_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/da_core_news_md | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (194 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 ... |
token-classification | spacy | ### Details: https://spacy.io/models/da#da_core_news_sm
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `da_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/da_core_news_sm | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (194 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (194 ... |
token-classification | spacy | ### Details: https://spacy.io/models/da#da_core_news_trf
Danish transformer pipeline (Transformer(name='vesteinn/DanskBERT', piece_encoder='xlm-roberta-sentencepiece', stride=120, type='xlm-roberta', width=768, window=152, vocab_size=50005)). Components: transformer, morphologizer, parser, lemmatizer (trainable_lemmat... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/da_core_news_trf | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Danish transformer pipeline (Transformer(name='vesteinn/DanskBERT', piece\_encoder='xlm-roberta-sentencepiece', stride=120, type='xlm-roberta', width=768, window=152, vocab\_size=50005)). Components: transformer, morphologizer, parser, lemmatizer (trainable\_lemmatizer), ner, attribute\_ruler.
###... | [
"### Details: URL\n\n\nDanish transformer pipeline (Transformer(name='vesteinn/DanskBERT', piece\\_encoder='xlm-roberta-sentencepiece', stride=120, type='xlm-roberta', width=768, window=152, vocab\\_size=50005)). Components: transformer, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), ner, attribute\\_r... | [
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDanish transformer pipeline (Transformer(name='vesteinn/DanskBERT', piece\\_encoder='xlm-roberta-sentencepiece', stride=120, type='xlm-roberta', width=768, window=152, vocab\\_size=50005)). Components... |
token-classification | spacy | ### Details: https://spacy.io/models/de#de_core_news_lg
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `de_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["de"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/de_core_news_lg | null | [
"spacy",
"token-classification",
"de",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-mit #model-index #region-us
| ### Details: URL
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (772 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels for 4 compone... |
token-classification | spacy | ### Details: https://spacy.io/models/de#de_core_news_md
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `de_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["de"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/de_core_news_md | null | [
"spacy",
"token-classification",
"de",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-mit #model-index #has_space #region-us
| ### Details: URL
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (772 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels fo... |
token-classification | spacy | ### Details: https://spacy.io/models/de#de_core_news_sm
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `de_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["de"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/de_core_news_sm | null | [
"spacy",
"token-classification",
"de",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-mit #model-index #region-us
| ### Details: URL
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (772 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nGerman pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (772 labels for 4 compone... |
token-classification | spacy | ### Details: https://spacy.io/models/de#de_dep_news_trf
German transformer pipeline (Transformer(name='bert-base-german-cased', piece_encoder='bert-wordpiece', stride=136, type='bert', width=768, window=176, vocab_size=30000)). Components: transformer, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer).
... | {"language": ["de"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/de_dep_news_trf | null | [
"spacy",
"token-classification",
"de",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-mit #model-index #region-us
| ### Details: URL
German transformer pipeline (Transformer(name='bert-base-german-cased', piece\_encoder='bert-wordpiece', stride=136, type='bert', width=768, window=176, vocab\_size=30000)). Components: transformer, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer).
### Label Scheme
View label s... | [
"### Details: URL\n\n\nGerman transformer pipeline (Transformer(name='bert-base-german-cased', piece\\_encoder='bert-wordpiece', stride=136, type='bert', width=768, window=176, vocab\\_size=30000)). Components: transformer, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer).",
"### Label Scheme\n\... | [
"TAGS\n#spacy #token-classification #de #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nGerman transformer pipeline (Transformer(name='bert-base-german-cased', piece\\_encoder='bert-wordpiece', stride=136, type='bert', width=768, window=176, vocab\\_size=30000)). Components: transformer, tagger, ... |
token-classification | spacy | ### Details: https://spacy.io/models/el#el_core_news_lg
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7... | {"language": ["el"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/el_core_news_lg | null | [
"spacy",
"token-classification",
"el",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (395 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (395 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (39... |
token-classification | spacy | ### Details: https://spacy.io/models/el#el_core_news_md
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7... | {"language": ["el"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/el_core_news_md | null | [
"spacy",
"token-classification",
"el",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (395 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (395 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (39... |
token-classification | spacy | ### Details: https://spacy.io/models/el#el_core_news_sm
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7... | {"language": ["el"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/el_core_news_sm | null | [
"spacy",
"token-classification",
"el",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (395 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (395 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #el #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nGreek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (39... |
token-classification | spacy | ### Details: https://spacy.io/models/en#en_core_web_lg
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_lg` |
| **Version** | `3.7.1` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipel... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/en_core_web_lg | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
| ### Details: URL
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (113 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/en#en_core_web_md
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_md` |
| **Version** | `3.7.1` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipel... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/en_core_web_md | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
| ### Details: URL
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (113 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/en#en_core_web_sm
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_sm` |
| **Version** | `3.7.1` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipel... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/en_core_web_sm | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
| ### Details: URL
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (113 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (113 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/en#en_core_web_trf
English transformer pipeline (Transformer(name='roberta-base', piece_encoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocab_size=50265)). Components: transformer, tagger, parser, ner, attribute_ruler, lemmatizer.
| Feature | Description |
|... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/en_core_web_trf | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
| ### Details: URL
English transformer pipeline (Transformer(name='roberta-base', piece\_encoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocab\_size=50265)). Components: transformer, tagger, parser, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (112 labels for 3 comp... | [
"### Details: URL\n\n\nEnglish transformer pipeline (Transformer(name='roberta-base', piece\\_encoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocab\\_size=50265)). Components: transformer, tagger, parser, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (112 ... | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nEnglish transformer pipeline (Transformer(name='roberta-base', piece\\_encoder='byte-bpe', stride=104, type='roberta', width=768, window=144, vocab\\_size=50265)). Components: transformer, tagger, p... |
token-classification | spacy | ### Details: https://spacy.io/models/es#es_core_news_lg
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `es_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/es_core_news_lg | null | [
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (468 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/es#es_core_news_md
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `es_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/es_core_news_md | null | [
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #has_space #region-us
| ### Details: URL
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (468 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #has_space #region-us \n",
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 compo... |
token-classification | spacy | ### Details: https://spacy.io/models/es#es_core_news_sm
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `es_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/es_core_news_sm | null | [
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #has_space #region-us
| ### Details: URL
Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (468 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #has_space #region-us \n",
"### Details: URL\n\n\nSpanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (468 labels for 3 compo... |
token-classification | spacy | ### Details: https://spacy.io/models/es#es_dep_news_trf
Spanish transformer pipeline (Transformer(name='dccuchile/bert-base-spanish-wwm-cased', piece_encoder='bert-wordpiece', stride=112, type='bert', width=768, window=158, vocab_size=31002)). Components: transformer, morphologizer, parser, attribute_ruler, lemmatizer... | {"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/es_dep_news_trf | null | [
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Spanish transformer pipeline (Transformer(name='dccuchile/bert-base-spanish-wwm-cased', piece\_encoder='bert-wordpiece', stride=112, type='bert', width=768, window=158, vocab\_size=31002)). Components: transformer, morphologizer, parser, attribute\_ruler, lemmatizer.
### Label Scheme
View label... | [
"### Details: URL\n\n\nSpanish transformer pipeline (Transformer(name='dccuchile/bert-base-spanish-wwm-cased', piece\\_encoder='bert-wordpiece', stride=112, type='bert', width=768, window=158, vocab\\_size=31002)). Components: transformer, morphologizer, parser, attribute\\_ruler, lemmatizer.",
"### Label Scheme\... | [
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nSpanish transformer pipeline (Transformer(name='dccuchile/bert-base-spanish-wwm-cased', piece\\_encoder='bert-wordpiece', stride=112, type='bert', width=768, window=158, vocab\\_size=31002)). Components: t... |
token-classification | spacy | ### Details: https://spacy.io/models/fr#fr_core_news_lg
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `fr_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defau... | {"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]} | spacy/fr_core_news_lg | null | [
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us
| ### Details: URL
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (237 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n",
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 components)",
"... |
token-classification | spacy | ### Details: https://spacy.io/models/fr#fr_core_news_md
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `fr_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defau... | {"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]} | spacy/fr_core_news_md | null | [
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us
| ### Details: URL
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (237 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n",
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 components)",
"... |
token-classification | spacy | ### Details: https://spacy.io/models/fr#fr_core_news_sm
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `fr_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defau... | {"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]} | spacy/fr_core_news_sm | null | [
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #has_space #region-us
| ### Details: URL
French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (237 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #has_space #region-us \n",
"### Details: URL\n\n\nFrench pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (237 labels for 3 compon... |
token-classification | spacy | ### Details: https://spacy.io/models/fr#fr_dep_news_trf
French transformer pipeline (Transformer(name='camembert-base', piece_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab_size=32005)). Components: transformer, morphologizer, parser, attribute_ruler, lemmatizer.
| Featu... | {"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]} | spacy/fr_dep_news_trf | null | [
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us
| ### Details: URL
French transformer pipeline (Transformer(name='camembert-base', piece\_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab\_size=32005)). Components: transformer, morphologizer, parser, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (2... | [
"### Details: URL\n\n\nFrench transformer pipeline (Transformer(name='camembert-base', piece\\_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab\\_size=32005)). Components: transformer, morphologizer, parser, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nVie... | [
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n",
"### Details: URL\n\n\nFrench transformer pipeline (Transformer(name='camembert-base', piece\\_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab\\_size=32005)). Components: transformer... |
token-classification | spacy | ### Details: https://spacy.io/models/it#it_core_news_lg
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `it_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["it"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/it_core_news_lg | null | [
"spacy",
"token-classification",
"it",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (443 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels ... |
token-classification | spacy | ### Details: https://spacy.io/models/it#it_core_news_md
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `it_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["it"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/it_core_news_md | null | [
"spacy",
"token-classification",
"it",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (443 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels ... |
token-classification | spacy | ### Details: https://spacy.io/models/it#it_core_news_sm
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `it_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["it"], "license": "cc-by-nc-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/it_core_news_sm | null | [
"spacy",
"token-classification",
"it",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us
| ### Details: URL
Italian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (443 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #it #license-cc-by-nc-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nItalian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (443 labels ... |
token-classification | spacy | ### Details: https://spacy.io/models/ja#ja_core_news_lg
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ja_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipelin... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ja_core_news_lg | null | [
"spacy",
"token-classification",
"ja",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (65 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)",
"### Ac... |
token-classification | spacy | ### Details: https://spacy.io/models/ja#ja_core_news_md
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ja_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipelin... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ja_core_news_md | null | [
"spacy",
"token-classification",
"ja",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (65 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)",
"### Ac... |
token-classification | spacy | ### Details: https://spacy.io/models/ja#ja_core_news_sm
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ja_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipelin... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ja_core_news_sm | null | [
"spacy",
"token-classification",
"ja",
"license:cc-by-sa-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #has_space #region-us
| ### Details: URL
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (65 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ja #license-cc-by-sa-4.0 #model-index #has_space #region-us \n",
"### Details: URL\n\n\nJapanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (65 labels for 3 components)"... |
token-classification | spacy | ### Details: https://spacy.io/models/ja#ja_core_news_trf
Japanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece_encoder='char', stride=160, type='bert', width=768, window=216, vocab_size=6144)). Components: transformer, morphologizer, parser, ner.
| Feature | Description |
| ---... | {"language": ["ja"], "license": "cc-by-sa-3.0", "tags": ["spacy", "token-classification"]} | spacy/ja_core_news_trf | null | [
"spacy",
"token-classification",
"ja",
"license:cc-by-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#spacy #token-classification #ja #license-cc-by-sa-3.0 #model-index #region-us
| ### Details: URL
Japanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece\_encoder='char', stride=160, type='bert', width=768, window=216, vocab\_size=6144)). Components: transformer, morphologizer, parser, ner.
### Label Scheme
View label scheme (64 labels for 3 components)... | [
"### Details: URL\n\n\nJapanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece\\_encoder='char', stride=160, type='bert', width=768, window=216, vocab\\_size=6144)). Components: transformer, morphologizer, parser, ner.",
"### Label Scheme\n\n\n\nView label scheme (64 labels f... | [
"TAGS\n#spacy #token-classification #ja #license-cc-by-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nJapanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece\\_encoder='char', stride=160, type='bert', width=768, window=216, vocab\\_size=6144)). Components: transfo... |
token-classification | spacy | ### Details: https://spacy.io/models/lt#lt_core_news_lg
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `lt_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["lt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/lt_core_news_lg | null | [
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1669 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels... |
token-classification | spacy | ### Details: https://spacy.io/models/lt#lt_core_news_md
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `lt_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["lt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/lt_core_news_md | null | [
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1669 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels... |
token-classification | spacy | ### Details: https://spacy.io/models/lt#lt_core_news_sm
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `lt_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["lt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/lt_core_news_sm | null | [
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1669 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nLithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1669 labels... |
token-classification | spacy | ### Details: https://spacy.io/models/mk#mk_core_news_lg
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `mk_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **D... | {"language": ["mk"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/mk_core_news_lg | null | [
"spacy",
"token-classification",
"mk",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (54 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 component... |
token-classification | spacy | ### Details: https://spacy.io/models/mk#mk_core_news_md
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `mk_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **D... | {"language": ["mk"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/mk_core_news_md | null | [
"spacy",
"token-classification",
"mk",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (54 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 component... |
token-classification | spacy | ### Details: https://spacy.io/models/mk#mk_core_news_sm
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `mk_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **D... | {"language": ["mk"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/mk_core_news_sm | null | [
"spacy",
"token-classification",
"mk",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (54 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #mk #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nMacedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (54 labels for 3 component... |
token-classification | spacy | ### Details: https://spacy.io/models/nb#nb_core_news_lg
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `nb_core_news_lg` |
| **Version** | `3.7.0` |
| **spa... | {"language": ["nb"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/nb_core_news_lg | null | [
"spacy",
"token-classification",
"nb",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #license-mit #model-index #region-us
| ### Details: URL
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (249 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (249 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (2... |
token-classification | spacy | ### Details: https://spacy.io/models/nb#nb_core_news_md
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `nb_core_news_md` |
| **Version** | `3.7.0` |
| **spa... | {"language": ["nb"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/nb_core_news_md | null | [
"spacy",
"token-classification",
"nb",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #license-mit #model-index #region-us
| ### Details: URL
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (249 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (249 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (2... |
token-classification | spacy | ### Details: https://spacy.io/models/nb#nb_core_news_sm
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `nb_core_news_sm` |
| **Version** | `3.7.0` |
| **spa... | {"language": ["nb"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/nb_core_news_sm | null | [
"spacy",
"token-classification",
"nb",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #license-mit #model-index #region-us
| ### Details: URL
Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (249 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (249 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nNorwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (2... |
token-classification | spacy | ### Details: https://spacy.io/models/nl#nl_core_news_lg
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `nl_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0... | {"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/nl_core_news_lg | null | [
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (323 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 labels for 4... |
token-classification | spacy | ### Details: https://spacy.io/models/nl#nl_core_news_md
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `nl_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0... | {"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/nl_core_news_md | null | [
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #has_space #region-us
| ### Details: URL
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (323 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #has_space #region-us \n",
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 l... |
token-classification | spacy | ### Details: https://spacy.io/models/nl#nl_core_news_sm
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `nl_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0... | {"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/nl_core_news_sm | null | [
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (323 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nDutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (323 labels for 4... |
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