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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers |
# Model description
**mbert-base-uncased-ner-pcm** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizati... | {"language": ["pcm"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."}]} | arnolfokam/mbert-base-uncased-ner-pcm | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pcm"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-ner-pcm is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**mbert-base-uncased-ner-swa** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizati... | {"language": ["swa"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."}]} | arnolfokam/mbert-base-uncased-ner-swa | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"swa",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"swa"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-ner-swa is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (swa) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**mbert-base-uncased-pcm** is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes conce... | {"language": ["pcm"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."}]} | arnolfokam/mbert-base-uncased-pcm | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pcm"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-pcm is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**mbert-base-uncased-swa** is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes conce... | {"language": ["swa"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."}]} | arnolfokam/mbert-base-uncased-swa | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"swa",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"swa"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
mbert-base-uncased-swa is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (swa) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===========... |
token-classification | transformers |
# Model description
**roberta-base-kin** is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named Entity Recog... | {"language": ["kin"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n\u2019u Rwanda, bushingiye nanone ku bufatanye hagati y\u2019imigabane ya Afurika n\u2019u B... | arnolfokam/roberta-base-kin | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"kin",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kin"
] | TAGS
#transformers #pytorch #roberta #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
roberta-base-kin is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research purposes c... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Kinyarwandan corpus (kin) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precisio... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #NER #kin #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n========... |
token-classification | transformers |
# Model description
**roberta-base-pcm** is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named Entity Recogn... | {"language": ["pcm"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi, Ultimate Fighting Championship, UFC don decide say dem go enta back di octagon on Saturday, 9 May, for Jacksonville, Florida."}]} | arnolfokam/roberta-base-pcm | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pcm"
] | TAGS
#transformers #pytorch #roberta #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
roberta-base-pcm is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research purposes c... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (pcm) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #NER #pcm #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n========... |
token-classification | transformers |
# Model description
**roberta-base-swa** is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
- dates & time (DATE)
- Location (LOC)
- Organizations (ORG)
- Person (PER)
# Intended Use
- Intended to be used for research purposes concerning Named Entity Recog... | {"language": ["swa"], "license": "apache-2.0", "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."}]} | arnolfokam/roberta-base-swa | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"swa",
"dataset:masakhaner",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"swa"
] | TAGS
#transformers #pytorch #roberta #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
roberta-base-swa is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities:
* dates & time (DATE)
* Location (LOC)
* Organizations (ORG)
* Person (PER)
Intended Use
============
* Intended to be used for research purposes c... | [
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n===============\n\n\nWe evaluated this model on the test split of the Swahili corpus (swa) present in the MasakhaNER with no thresholding.\n\n\nMetrics\n=======\n\n\n* Precision\n* ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #NER #swa #dataset-masakhaner #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Hyperparameters\n\n\n* Learning Rate: 5e-5\n* Batch Size: 32\n* Maximum Sequence Length: 164\n* Epochs: 30\n\n\nEvaluation Data\n========... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kobert-finetuned-squad_kor_v1
This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on... | {"tags": ["generated_from_trainer"], "datasets": ["squad_kor_v1"]} | arogyaGurkha/kobert-finetuned-squad_kor_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_kor_v1",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us
| kobert-finetuned-squad\_kor\_v1
===============================
This model is a fine-tuned version of monologg/kobert on the squad\_kor\_v1 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0928
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_b... |
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. -->
# koelectra-base-discriminator-finetuned-squad_kor_v1
This model is a fine-tuned version of [monologg/koelectra-base-discriminator... | {"tags": ["generated_from_trainer"], "datasets": ["squad_kor_v1"]} | arogyaGurkha/koelectra-base-discriminator-finetuned-squad_kor_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad_kor_v1",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us
| koelectra-base-discriminator-finetuned-squad\_kor\_v1
=====================================================
This model is a fine-tuned version of monologg/koelectra-base-discriminator on the squad\_kor\_v1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5589
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\... |
text-classification | transformers |
Connect me on LinkedIn
- [linkedin.com/in/arpanghoshal](https://www.linkedin.com/in/arpanghoshal)
## What is GoEmotions
Dataset labelled 58000 Reddit comments with 28 emotions
- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassm... | {"language": "en", "license": "mit", "tags": ["text-classification", "tensorflow", "roberta"], "datasets": ["go_emotions"]} | arpanghoshal/EmoRoBERTa | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"tensorflow",
"en",
"dataset:go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #roberta #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| Connect me on LinkedIn
* URL
What is GoEmotions
------------------
Dataset labelled 58000 Reddit comments with 28 emotions
* admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love... | [] | [
"TAGS\n#transformers #tf #roberta #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | arredondos/my_sentence_transformer | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us
| all-MiniLM-L6-v2
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncase... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | artemis13fowl/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4725
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-lv-v05
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-lv-v05", "results": []}]} | artursz/wav2vec2-large-xls-r-300m-lv-v05 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-lv-v05
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3862
* Wer: 0.2588
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | arvalinno/albert-base-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| albert-base-v2-finetuned-squad
==============================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3222
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_b... |
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. -->
# distilbert-base-uncased-finetuned-indosquad-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-indosquad-v2", "results": []}]} | arvalinno/distilbert-base-uncased-finetuned-indosquad-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-indosquad-v2
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6650
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | arvalinno/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4232
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | aryanbhosale/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Harry porter DialoGPT model | {"tags": ["conversational"]} | asad/DialoGPT-small-harryporter_bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry porter DialoGPT model | [
"# Harry porter DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry porter DialoGPT model"
] |
fill-mask | transformers |
# Arabic-ALBERT Base
Arabic edition of ALBERT Base pretrained language model
_If you use any of these models in your work, please cite this work as:_
```
@software{ali_safaya_2020_4718724,
author = {Ali Safaya},
title = {Arabic-ALBERT},
month = aug,
year = 2020,
publisher =... | {"language": "ar", "tags": ["ar", "masked-lm"], "datasets": ["oscar", "wikipedia"]} | asafaya/albert-base-arabic | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"ar",
"masked-lm",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us
| Arabic-ALBERT Base
==================
Arabic edition of ALBERT Base pretrained language model
*If you use any of these models in your work, please cite this work as:*
Pretraining data
----------------
The models were pretrained on ~4.4 Billion words:
* Arabic version of OSCAR (unshuffled version of the corpus... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic-ALBERT Large
Arabic edition of ALBERT Large pretrained language model
_If you use any of these models in your work, please cite this work as:_
```
@software{ali_safaya_2020_4718724,
author = {Ali Safaya},
title = {Arabic-ALBERT},
month = aug,
year = 2020,
publisher ... | {"language": "ar", "tags": ["ar", "masked-lm"], "datasets": ["oscar", "wikipedia"]} | asafaya/albert-large-arabic | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"ar",
"masked-lm",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us
| Arabic-ALBERT Large
===================
Arabic edition of ALBERT Large pretrained language model
*If you use any of these models in your work, please cite this work as:*
Pretraining data
----------------
The models were pretrained on ~4.4 Billion words:
* Arabic version of OSCAR (unshuffled version of the cor... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic-ALBERT Xlarge
Arabic edition of ALBERT Xlarge pretrained language model
_If you use any of these models in your work, please cite this work as:_
```
@software{ali_safaya_2020_4718724,
author = {Ali Safaya},
title = {Arabic-ALBERT},
month = aug,
year = 2020,
publisher ... | {"language": "ar", "tags": ["ar", "masked-lm"], "datasets": ["oscar", "wikipedia"]} | asafaya/albert-xlarge-arabic | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"ar",
"masked-lm",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us
| Arabic-ALBERT Xlarge
====================
Arabic edition of ALBERT Xlarge pretrained language model
*If you use any of these models in your work, please cite this work as:*
Pretraining data
----------------
The models were pretrained on ~4.4 Billion words:
* Arabic version of OSCAR (unshuffled version of the ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #ar #masked-lm #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic BERT Model
Pretrained BERT base language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media",
author = "Safaya, ... | {"language": "ar", "datasets": ["oscar", "wikipedia"]} | asafaya/bert-base-arabic | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Arabic BERT Model
Pretrained BERT base language model for Arabic
_If you use this model in your work, please cite this paper:_
## Pretraining Corpus
'arabic-bert-base' model was pretrained on ~8.2 Billion words:
- Arabic version of OSCAR - filtered from Common Crawl
- Recent dump of Arabic Wikipedia
and oth... | [
"# Arabic BERT Model\n\nPretrained BERT base language model for Arabic\n\n\n_If you use this model in your work, please cite this paper:_",
"## Pretraining Corpus\n\n'arabic-bert-base' model was pretrained on ~8.2 Billion words:\n\n- Arabic version of OSCAR - filtered from Common Crawl\n- Recent dump of Arabic Wi... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Arabic BERT Model\n\nPretrained BERT base language model for Arabic\n\n\n_If you use this model in your work, please cite this paper:... |
fill-mask | transformers |
# Arabic BERT Large Model
Pretrained BERT Large language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media",
author = "... | {"language": "ar", "datasets": ["oscar", "wikipedia"]} | asafaya/bert-large-arabic | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Arabic BERT Large Model
Pretrained BERT Large language model for Arabic
_If you use this model in your work, please cite this paper:_
## Pretraining Corpus
'arabic-bert-large' model was pretrained on ~8.2 Billion words:
- Arabic version of OSCAR - filtered from Common Crawl
- Recent dump of Arabic Wikipedia... | [
"# Arabic BERT Large Model\n\nPretrained BERT Large language model for Arabic\n\n_If you use this model in your work, please cite this paper:_",
"## Pretraining Corpus\n\n'arabic-bert-large' model was pretrained on ~8.2 Billion words:\n\n- Arabic version of OSCAR - filtered from Common Crawl\n- Recent dump of Ara... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Arabic BERT Large Model\n\nPretrained BERT Large language model for Arabic\n\n_If you use this model in your work, please cite this p... |
fill-mask | transformers |
# Arabic BERT Medium Model
Pretrained BERT Medium language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media",
author = "... | {"language": "ar", "datasets": ["oscar", "wikipedia"]} | asafaya/bert-medium-arabic | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Arabic BERT Medium Model
Pretrained BERT Medium language model for Arabic
_If you use this model in your work, please cite this paper:_
## Pretraining Corpus
'arabic-bert-medium' model was pretrained on ~8.2 Billion words:
- Arabic version of OSCAR - filtered from Common Crawl
- Recent dump of Arabic Wikipedi... | [
"# Arabic BERT Medium Model\n\nPretrained BERT Medium language model for Arabic\n\n_If you use this model in your work, please cite this paper:_",
"## Pretraining Corpus\n\n'arabic-bert-medium' model was pretrained on ~8.2 Billion words:\n\n- Arabic version of OSCAR - filtered from Common Crawl\n- Recent dump of ... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Arabic BERT Medium Model\n\nPretrained BERT Medium language model for Arabic\n\n_If you use this model in your work, please cite this... |
fill-mask | transformers |
# Arabic BERT Mini Model
Pretrained BERT Mini language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media",
author = "Safa... | {"language": "ar", "datasets": ["oscar", "wikipedia"]} | asafaya/bert-mini-arabic | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us
|
# Arabic BERT Mini Model
Pretrained BERT Mini language model for Arabic
_If you use this model in your work, please cite this paper:_
## Pretraining Corpus
'arabic-bert-mini' model was pretrained on ~8.2 Billion words:
- Arabic version of OSCAR - filtered from Common Crawl
- Recent dump of Arabic Wikipedia
and... | [
"# Arabic BERT Mini Model\n\nPretrained BERT Mini language model for Arabic\n\n_If you use this model in your work, please cite this paper:_",
"## Pretraining Corpus\n\n'arabic-bert-mini' model was pretrained on ~8.2 Billion words:\n\n- Arabic version of OSCAR - filtered from Common Crawl\n- Recent dump of Arabic... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-oscar #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us \n",
"# Arabic BERT Mini Model\n\nPretrained BERT Mini language model for Arabic\n\n_If you use this model in your work, please cite this paper:_",
"#... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squad-qg-default`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asa... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | research-backup/bart-base-squad-qg-default | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2001.11314",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.11314",
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squad-qg-default'
==========================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without parameter search (defaul... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Train... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squad-qg-no-answer`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/a... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce further expanded her acting career, starring as blues singer Etta Ja... | research-backup/bart-base-squad-qg-no-answer | null | [
"transformers",
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"bart",
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"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squad-qg-no-answer'
============================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without answer information, ... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squad-qg-no-paragraph`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | research-backup/bart-base-squad-qg-no-paragraph | null | [
"transformers",
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"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squad-qg-no-paragraph'
===============================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without pargraph infor... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-squad-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-g... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | lmqg/bart-base-squad-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-squad-qg'
=======================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/bart-base
* Language: en
* Training data: lmqg/qg\_... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squad-qg-default`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | research-backup/bart-large-squad-qg-default | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2001.11314",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.11314",
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squad-qg-default'
===========================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without parameter search (def... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Trai... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squad-qg-no-answer`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce further expanded her acting career, starring as blues singer Etta Ja... | research-backup/bart-large-squad-qg-no-answer | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squad-qg-no-answer'
=============================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without answer informatio... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squad-qg-no-paragraph`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | research-backup/bart-large-squad-qg-no-paragraph | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squad-qg-no-paragraph'
================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without pargraph in... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-squad-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-questio... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Ett... | lmqg/bart-large-squad-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-squad-qg'
========================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/bart-large
* Language: en
* Training data: lmqg/... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-jaquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github... | {"language": "ja", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_jaquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: \u30be\u30d5\u30a3\u30fc\u306f\u8cb4\u65c... | lmqg/mt5-small-jaquad-qg-ae | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"answer extraction",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-jaquad-qg-ae'
===========================================
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg\_jaquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language:... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ja\n* Training data: lmqg/qg\\_jaquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-smal... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-jaquad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "ja", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_jaquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u30be\u30d5\u30a3\u30fc\u306f\u8cb4\u65cf\u51fa\u8eab\u3067\u306f\u3042\u3063\u3... | lmqg/mt5-small-jaquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ja",
"dataset:lmqg/qg_jaquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-jaquad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_jaquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: ja
* Training data: lmqg/qg\_j... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ja\n* Training data: lmqg/qg\\_jaquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ja\n... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-base-squad-qg-default`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-genera... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-base-squad-qg-default | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2001.11314",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.11314",
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-base-squad-qg-default'
========================================================
This model is fine-tuned version of t5-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without parameter search (default configuration... | [
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining hy... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-base\n* Languag... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squad-qg-ae`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-questi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her act... | lmqg/t5-base-squad-qg-ae | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"answer extraction",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squad-qg-ae'
========================================
This model is fine-tuned version of t5-base for question generation and answer extraction jointly on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: t5-base
* Language: en
* Training data: lmqg... | [
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* *Metric (... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-base\n* Langua... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-base-squad-qg-no-answer`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gene... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce further expanded her acting career, starring as b... | research-backup/t5-base-squad-qg-no-answer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-base-squad-qg-no-answer'
==========================================================
This model is fine-tuned version of t5-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without answer information, i.e. generate a... | [
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining hy... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training ... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-base-squad-qg-no-paragraph`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-g... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-base-squad-qg-no-paragraph | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-base-squad-qg-no-paragraph'
=============================================================
This model is fine-tuned version of t5-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without pargraph information but only... | [
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining hy... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training ... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squad-qg`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overvi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | lmqg/t5-base-squad-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squad-qg'
=====================================
This model is fine-tuned version of t5-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: t5-base
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online D... | [
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* *Metric (... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-base\n* Language: en\n... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-squad-qg-default`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gen... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-large-squad-qg-default | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2001.11314",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.11314",
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-squad-qg-default'
=========================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without parameter search (default configurat... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Langua... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-squad-qg-no-answer`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-g... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce further expanded her acting career, starring as b... | research-backup/t5-large-squad-qg-no-answer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-squad-qg-no-answer'
===========================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without answer information, i.e. generat... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-squad-qg-no-paragraph`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-questio... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-large-squad-qg-no-paragraph | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-squad-qg-no-paragraph'
==============================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without pargraph information but o... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-squad-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Ove... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | lmqg/t5-large-squad-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-squad-qg'
======================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Onli... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* *Metric ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-small-squad-qg-default`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gen... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-small-squad-qg-default | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2001.11314",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.11314",
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-small-squad-qg-default'
=========================================================
This model is fine-tuned version of t5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without parameter search (default configurat... | [
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2001.11314 #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-small\n* Langua... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squad-qg-ae`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-que... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her act... | lmqg/t5-small-squad-qg-ae | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"answer extraction",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squad-qg-ae'
=========================================
This model is fine-tuned version of t5-small for question generation and answer extraction jointly on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: t5-small
* Language: en
* Training data: ... | [
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* *Metric ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #answer extraction #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-small\n* Langu... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-small-squad-qg-no-answer`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-g... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce further expanded her acting career, starring as b... | research-backup/t5-small-squad-qg-no-answer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-small-squad-qg-no-answer'
===========================================================
This model is fine-tuned version of t5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without answer information, i.e. generat... | [
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-small-squad-qg-no-paragraph`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-questio... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | research-backup/t5-small-squad-qg-no-paragraph | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-small-squad-qg-no-paragraph'
==============================================================
This model is fine-tuned version of t5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
This model is fine-tuned without pargraph information but o... | [
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-squad-qg`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Ove... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring ... | lmqg/t5-small-squad-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-squad-qg'
======================================
This model is fine-tuned version of t5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: t5-small
* Language: en
* Training data: lmqg/qg\_squad (default)
* Onli... | [
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training data: lmqg/qg\\_squad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n* *Metric ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-small\n* Language: en\n* Training... |
null | adapter-transformers |
# Adapter `asahi417/tner-roberta-large-multiconer-en-adapter` for roberta-large
An [adapter](https://adapterhub.ml) for the `roberta-large` model that was trained on the [named-entity-recognition/multiconer](https://adapterhub.ml/explore/named-entity-recognition/multiconer/) dataset and includes a prediction head for... | {"tags": ["adapter-transformers", "adapterhub:named-entity-recognition/multiconer", "roberta"], "datasets": ["multiconer"]} | asahi417/tner-roberta-large-multiconer-en-adapter | null | [
"adapter-transformers",
"roberta",
"adapterhub:named-entity-recognition/multiconer",
"dataset:multiconer",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us
|
# Adapter 'asahi417/tner-roberta-large-multiconer-en-adapter' for roberta-large
An adapter for the 'roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
... | [
"# Adapter 'asahi417/tner-roberta-large-multiconer-en-adapter' for roberta-large\n\nAn adapter for the 'roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
... | [
"TAGS\n#adapter-transformers #roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us \n",
"# Adapter 'asahi417/tner-roberta-large-multiconer-en-adapter' for roberta-large\n\nAn adapter for the 'roberta-large' model that was trained on the named-entity-recognition/multiconer dataset... |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-all-english")
model = ... | {} | asahi417/tner-xlm-roberta-base-all-english | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-bc5cdr")
model = AutoM... | {} | tner/xlm-roberta-base-bc5cdr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-bionlp2004")
model = A... | {} | tner/xlm-roberta-base-bionlp2004 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-conll2003")
model = Au... | {} | tner/xlm-roberta-base-conll2003 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-fin")
model = AutoMode... | {} | tner/xlm-roberta-base-fin | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # Model Card for XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER.
# Model Details
## Model Description
XLM-RoBERTa finetuned on NER.
- **Developed by:** Asahi Ushio
- **Shared by [Optional]:** Hugging Face
- **Model type:** Token Classification
- **Language(s) (NLP):** en
- **License:** More information needed... | {"language": ["en"]} | asahi417/tner-xlm-roberta-base-ontonotes5 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"en",
"arxiv:2209.12616",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2209.12616",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #en #arxiv-2209.12616 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Model Card for XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER.
# Model Details
## Model Description
XLM-RoBERTa finetuned on NER.
- Developed by: Asahi Ushio
- Shared by [Optional]: Hugging Face
- Model type: Token Classification
- Language(s) (NLP): en
- License: More information needed
- Related Models: X... | [
"# Model Card for XLM-RoBERTa for NER\n \nXLM-RoBERTa finetuned on NER.",
"# Model Details",
"## Model Description\n \nXLM-RoBERTa finetuned on NER.\n- Developed by: Asahi Ushio\n- Shared by [Optional]: Hugging Face\n- Model type: Token Classification\n- Language(s) (NLP): en\n- License: More information needed... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #en #arxiv-2209.12616 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for XLM-RoBERTa for NER\n \nXLM-RoBERTa finetuned on NER.",
"# Model Details",
"## Model Description\n \nXLM-RoBERTa fin... |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ar")
mode... | {} | tner/xlm-roberta-base-panx-dataset-ar | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-en")
mode... | {} | tner/xlm-roberta-base-panx-dataset-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-es")
mod... | {} | tner/xlm-roberta-base-panx-dataset-es | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ja")
mode... | {} | tner/xlm-roberta-base-panx-dataset-ja | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ko")
mod... | {} | tner/xlm-roberta-base-panx-dataset-ko | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ru")
mode... | {} | tner/xlm-roberta-base-panx-dataset-ru | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-all-english")
... | {} | tner/xlm-roberta-base-uncased-all-english | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-bc5cdr")
model... | {} | tner/xlm-roberta-base-uncased-bc5cdr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-bionlp2004")
... | {} | tner/xlm-roberta-base-uncased-bionlp2004 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-conll2003")
mo... | {} | tner/xlm-roberta-base-uncased-conll2003 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-fin")
model = ... | {} | tner/xlm-roberta-base-uncased-fin | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-mit-movie-trivia")
... | {} | tner/xlm-roberta-base-uncased-mit-movie-trivia | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-mit-restaurant")
... | {} | tner/xlm-roberta-base-uncased-mit-restaurant | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-ontonotes5")
m... | {} | asahi417/tner-xlm-roberta-base-uncased-ontonotes5 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-panx-dataset-en")
... | {} | tner/xlm-roberta-base-uncased-panx-dataset-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-wnut2017")
mod... | {} | tner/xlm-roberta-base-uncased-wnut2017 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-wnut2017")
model = Aut... | {} | tner/xlm-roberta-base-wnut2017 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-all-english")
model =... | {} | asahi417/tner-xlm-roberta-large-all-english | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-bc5cdr")
model = Auto... | {} | asahi417/tner-xlm-roberta-large-bc5cdr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-bionlp2004")
model = ... | {} | tner/xlm-roberta-large-bionlp2004 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-conll2003")
model = A... | {} | tner/xlm-roberta-large-conll2003 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-fin")
model = AutoMod... | {} | tner/xlm-roberta-large-fin | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
null | adapter-transformers |
# Adapter `asahi417/tner-xlm-roberta-large-multiconer-mix-adapter` for xlm-roberta-large
An [adapter](https://adapterhub.ml) for the `xlm-roberta-large` model that was trained on the [named-entity-recognition/multiconer](https://adapterhub.ml/explore/named-entity-recognition/multiconer/) dataset and includes a predic... | {"tags": ["adapter-transformers", "adapterhub:named-entity-recognition/multiconer", "xlm-roberta"], "datasets": ["multiconer"]} | asahi417/tner-xlm-roberta-large-multiconer-mix-adapter | null | [
"adapter-transformers",
"xlm-roberta",
"adapterhub:named-entity-recognition/multiconer",
"dataset:multiconer",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #xlm-roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us
|
# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-mix-adapter' for xlm-roberta-large
An adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers librar... | [
"# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-mix-adapter' for xlm-roberta-large\n\nAn adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformer... | [
"TAGS\n#adapter-transformers #xlm-roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us \n",
"# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-mix-adapter' for xlm-roberta-large\n\nAn adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition/m... |
null | adapter-transformers |
# Adapter `asahi417/tner-xlm-roberta-large-multiconer-multi-adapter` for xlm-roberta-large
An [adapter](https://adapterhub.ml) for the `xlm-roberta-large` model that was trained on the [named-entity-recognition/multiconer](https://adapterhub.ml/explore/named-entity-recognition/multiconer/) dataset and includes a pred... | {"tags": ["adapter-transformers", "adapterhub:named-entity-recognition/multiconer", "xlm-roberta"], "datasets": ["multiconer"]} | asahi417/tner-xlm-roberta-large-multiconer-multi-adapter | null | [
"adapter-transformers",
"xlm-roberta",
"adapterhub:named-entity-recognition/multiconer",
"dataset:multiconer",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #xlm-roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us
|
# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-multi-adapter' for xlm-roberta-large
An adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers libr... | [
"# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-multi-adapter' for xlm-roberta-large\n\nAn adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition/multiconer dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transform... | [
"TAGS\n#adapter-transformers #xlm-roberta #adapterhub-named-entity-recognition/multiconer #dataset-multiconer #region-us \n",
"# Adapter 'asahi417/tner-xlm-roberta-large-multiconer-multi-adapter' for xlm-roberta-large\n\nAn adapter for the 'xlm-roberta-large' model that was trained on the named-entity-recognition... |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-ontonotes5")
model = ... | {} | asahi417/tner-xlm-roberta-large-ontonotes5 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ar")
mod... | {} | tner/xlm-roberta-large-panx-dataset-ar | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-en")
mod... | {} | tner/xlm-roberta-large-panx-dataset-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-es")
mod... | {} | tner/xlm-roberta-large-panx-dataset-es | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ja")
mod... | {} | tner/xlm-roberta-large-panx-dataset-ja | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ko")
mod... | {} | tner/xlm-roberta-large-panx-dataset-ko | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ru")
mod... | {} | tner/xlm-roberta-large-panx-dataset-ru | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-all-english")
... | {} | tner/xlm-roberta-large-uncased-all-english | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-bc5cdr")
mode... | {} | tner/xlm-roberta-large-uncased-bc5cdr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-bionlp2004")
... | {} | tner/xlm-roberta-large-uncased-bionlp2004 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-conll2003")
m... | {} | tner/xlm-roberta-large-uncased-conll2003 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-fin")
model =... | {} | tner/xlm-roberta-large-uncased-fin | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-movie-trivia")... | {} | tner/xlm-roberta-large-uncased-mit-movie-trivia | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-restaurant")
... | {} | tner/xlm-roberta-large-uncased-mit-restaurant | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-ontonotes5")
... | {} | asahi417/tner-xlm-roberta-large-uncased-ontonotes5 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-panx-dataset-en")
... | {} | tner/xlm-roberta-large-uncased-panx-dataset-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-wnut2017")
mo... | {} | tner/xlm-roberta-large-uncased-wnut2017 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
token-classification | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-wnut2017")
model = Au... | {} | tner/xlm-roberta-large-wnut2017 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at TNER repository.
## Usage
| [
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for NER\nXLM-RoBERTa finetuned on NER. Check more detail at TNER repository.",
"## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-demo-colab", "results": []}]} | asakawa/wav2vec2-base-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-demo-colab
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4500
* Wer: 0.3391
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | asalics/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2207
* Accuracy: 0.924
* F1: 0.9244
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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