pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for questi... | {"license": "apache-2.0"} | gaetangate/bart-large_genrl_lcquad1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.07337"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for questi... | {"license": "apache-2.0"} | gaetangate/bart-large_genrl_lcquad2 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.07337"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for questi... | {"license": "apache-2.0"} | gaetangate/bart-large_genrl_qald9 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.07337"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for questi... | {"license": "apache-2.0"} | gaetangate/bart-large_genrl_simpleq | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.07337"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | test 123 | {} | gaga42gaga42/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| test 123 | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | # Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the [BenchmarkTest](https://github.com/gagan3012/Fox-News-Generator/blob/master/BenchmarkT... | {} | gagan3012/Fox-News-Generator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the BenchmarkTest notebook for results
Find the model at gagan3012/Fox-News-Generator
| [
"# Generating Right Wing News Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data from FOX news",
"### My model can be accessed at gagan3012/Fox-News-Generator\n\nCheck the BenchmarkTest notebook for results\n\nFind the model at gagan3012/Fox... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Generating Right Wing News Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data from FOX news",... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTGPT2I2A
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patc... | {"license": "apache-2.0", "tags": ["image-captioning", "generated_from_trainer"], "model-index": [{"name": "ViTGPT2I2A", "results": []}]} | gagan3012/ViTGPT2I2A | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-captioning",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-captioning #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ViTGPT2I2A
==========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the vizwiz dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0708
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* opti... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-captioning #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #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: 2\... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTGPT2_VW
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following re... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ViTGPT2_VW", "results": []}]} | gagan3012/ViTGPT2_VW | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us
| ViTGPT2\_VW
===========
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0771
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Tr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* opti... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #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: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distribu... |
image-to-text | 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. -->
# ViTGPT2_vizwiz
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the followin... | {"tags": ["generated_from_trainer", "image-to-text"], "model-index": [{"name": "ViTGPT2_vizwiz", "results": []}]} | gagan3012/ViTGPT2_vizwiz | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"generated_from_trainer",
"image-to-text",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #endpoints_compatible #has_space #region-us
| ViTGPT2\_vizwiz
===============
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0719
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size:... |
token-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. -->
# bert-tiny-finetuned-ner
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) ... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "... | gagan3012/bert-tiny-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-tiny-finetuned-ner
=======================
This model is a fine-tuned version of prajjwal1/bert-tiny on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1689
* Precision: 0.8083
* Recall: 0.8274
* F1: 0.8177
* Accuracy: 0.9598
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
token-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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | gagan3012/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"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 #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9274
* Recall: 0.9363
* F1: 0.9319
* Accuracy: 0.9840
Model des... | [
"### 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 #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text2text-generation | transformers |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[![pypi Version](https://img.shields.io/pypi... | {"language": "en", "license": "mit", "tags": ["keytotext", "k2t-base", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t-base",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-base #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
## Model:
Keytotext is based on the Amazing T5 Model:
- 'k2t': Model... | [
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 M... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-base #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywo... |
text2text-generation | transformers |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[![pypi Version](https://img.shields.io/pyp... | {"language": "en", "license": "mit", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["common_gen"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t-new | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:common_gen",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-common_gen #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
## Model:
Keytotext is based on the Amazing T5 Model:
- 'k2t': Mode... | [
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 M... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-common_gen #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inp... |
text2text-generation | transformers |
<h1 align="center">keytotext</h1>
[](https://pypi.org/project/keytotext/)
[![Downloads](https://static.pepy.tech/personalized-badge/keytotext?period=total&units=none&left_color=grey&right_color=orange&left_text=Pip%2... | {"language": "en", "license": "MIT", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t-test | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<h1 align="center">keytotext</h1>
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
![API Call](URL
![Docker Call](URL
![HuggingFace](URL
![Documentation Status](URL
![Code style: black](URL
!keytotext
Idea is to build a model which will take keywords as inputs and ge... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
#keytotext
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/... | {"language": "en", "license": "MIT", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t-test3 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#keytotext
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
![API Call](URL
![Docker Call](URL
![HuggingFace](URL
![Documentation Status](URL
![Code style: black](URL
!keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
P... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[![pypi Version](https://img.shields.io/pypi... | {"language": "en", "license": "mit", "tags": ["keytotext", "k2t-tiny", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t-tiny | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t-tiny",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-tiny #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
## Model:
Keytotext is based on the Amazing T5 Model:
- 'k2t': Model... | [
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 M... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-tiny #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywo... |
text2text-generation | transformers |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[![pypi Version](https://img.shields.io/pyp... | {"language": "en", "license": "mit", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | gagan3012/k2t | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
## Model:
Keytotext is based on the Amazing T5 Model:
- 'k2t': Mode... | [
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 M... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take... |
text2text-generation | transformers | # keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gag... | {} | gagan3012/keytotext-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: URL
- Using T5-small size = 230 MB can be found here: URL
#### Usage:
### Demo:
![Streamlit App](URL
URL
!image
... | [
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n![Streamlit... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T... |
text2text-generation | transformers | # keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gag... | {} | gagan3012/keytotext | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: URL
- Using T5-small size = 230 MB can be found here: URL
#### Usage:
### Demo:
![Streamlit App](URL
URL
!image
... | [
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n on an unknown dataset.
It achieves t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "model", "results": []}]} | gagan3012/model | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# model
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.6250",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following result... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pickuplines
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the fol... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "pickuplines", "results": []}]} | gagan3012/pickuplines | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pickuplines
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# pickuplines\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.7873",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pickuplines\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on th... |
text-generation | transformers | # Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
**Note**: the Answers might not make sense in some cases because of the bias in GPT-2
**Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code... | {} | gagan3012/project-code-py-small | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
Note: the Answers might not make sense in some cases because of the bias in GPT-2
Contribtuions: If you would like to make the model better contributions are welcome Check out URL
### Favour:
It would be highly motivating, if you can STAR⭐ t... | [
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT... |
text-generation | transformers | # Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
**Note**: the Answers might not make sense in some cases because of the bias in GPT-2
**Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code... | {} | gagan3012/project-code-py | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
Note: the Answers might not make sense in some cases because of the bias in GPT-2
Contribtuions: If you would like to make the model better contributions are welcome Check out URL
### Favour:
It would be highly motivating, if you can STAR⭐ t... | [
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContr... |
text-generation | transformers | # Generating Rap song Lyrics like Eminem Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data lyrics from leading hip-hop stars
### My model can be accessed at: gagan3012/rap-writer
```
from transformers import AutoTokenizer, AutoModelWithLMHead
token... | {} | gagan3012/rap-writer | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Generating Rap song Lyrics like Eminem Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data lyrics from leading hip-hop stars
### My model can be accessed at: gagan3012/rap-writer
| [
"# Generating Rap song Lyrics like Eminem Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data lyrics from leading hip-hop stars",
"### My model can be accessed at: gagan3012/rap-writer"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Generating Rap song Lyrics like Eminem Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model usi... |
text2text-generation | transformers | ---
Summarisation model summarsiation | {} | gagan3012/summarsiation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ---
Summarisation model summarsiation | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]} | gagan3012/wav2vec2-large-xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"#... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ca... | {"language": "cv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-chuvash by Gagan Bhatia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | gagan3012/wav2vec2-xlsr-chuvash | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"cv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"cv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Results:
Prediction: ['проектпа килӗшӳ... | [
"# Wav2Vec2-Large-XLSR-53-Chuvash \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction:... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Chuvash \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-khmer
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Khmer using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR Kh](http://www.openslr.org/42/).
When using this model, make sure that your speech input ... | {"language": "km", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR", "common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-Khmer by Gagan Bhatia", "results": [{"task": {"type": "automatic-speech-recognition", "na... | gagan3012/wav2vec2-xlsr-khmer | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"km",
"dataset:OpenSLR",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"km"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #km #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-khmer
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Result
Prediction: ['पारा... | [
"# Wav2Vec2-Large-XLSR-53-khmer \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #km #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-khmer \n\nFine-tuned facebook/wav2vec2-large-xlsr-... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Nepali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Nepali using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR ne](http://www.openslr.org/43/).
When using this model, make sure that your speech inpu... | {"language": "ne", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR", "common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-nepali", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Re... | gagan3012/wav2vec2-xlsr-nepali | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ne",
"dataset:OpenSLR",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ne"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ne #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Nepali
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Result
Prediction: ['पा... | [
"# Wav2Vec2-Large-XLSR-53-Nepali \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ne #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Nepali \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nepa... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ca... | {"language": "pa-IN", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-punjabi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogniti... | gagan3012/wav2vec2-xlsr-punjabi | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Results:
Prediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰ... | [
"# Wav2Vec2-Large-XLSR-53-Punjabi \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction:... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Punjabi \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common... |
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. -->
# xls-r-300m-hi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "xls-r-300m-hi", "results": []}]} | gagan3012/xls-r-300m-hi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-hi
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7522
* Wer: 1.0091
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #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* ... |
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. -->
# xls-r-300m-pa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "xls-r-300m-pa", "results": []}]} | gagan3012/xls-r-300m-pa | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa-IN"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-pa
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0443
* Wer: 0.5715
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #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* lear... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 19984005
- CO2 Emissions (in grams): 20.790169878009916
## Validation Metrics
- Loss: 0.06693269312381744
- Accuracy: 0.9789
- Precision: 0.9843244336569579
- Recall: 0.9733
- AUC: 0.99695552
- F1: 0.9787811745776348
## Usage
You can ... | {"language": "es", "tags": "autonlp", "datasets": ["gagandeepkundi/autonlp-data-text-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 20.790169878009916} | gagandeepkundi/latam-question-quality | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"es",
"dataset:gagandeepkundi/autonlp-data-text-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #es #dataset-gagandeepkundi/autonlp-data-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 19984005
- CO2 Emissions (in grams): 20.790169878009916
## Validation Metrics
- Loss: 0.06693269312381744
- Accuracy: 0.9789
- Precision: 0.9843244336569579
- Recall: 0.9733
- AUC: 0.99695552
- F1: 0.9787811745776348
## Usage
You can ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 19984005\n- CO2 Emissions (in grams): 20.790169878009916",
"## Validation Metrics\n\n- Loss: 0.06693269312381744\n- Accuracy: 0.9789\n- Precision: 0.9843244336569579\n- Recall: 0.9733\n- AUC: 0.99695552\n- F1: 0.9787811745776348"... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #es #dataset-gagandeepkundi/autonlp-data-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 19984005\n- CO2 Emiss... |
text-classification | transformers |
# Sentiment Classification for hinglish text: `gk-hinglish-sentiment`
## Model description
Trained small amount of reviews dataset
## Intended uses & limitations
I wanted something to work well with hinglish data as it is being used in India mostly.
The training data was not much as expected
#### How to use
```p... | {"license": "apache-2.0", "tags": ["sentiment", "multilingual", "hindi codemix", "hinglish"], "datasets": ["sail"], "language_bcp47": ["hi-en"]} | ganeshkharad/gk-hinglish-sentiment | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"multilingual",
"hindi codemix",
"hinglish",
"dataset:sail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #sentiment #multilingual #hindi codemix #hinglish #dataset-sail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'
## Model description
Trained small amount of reviews dataset
## Intended uses & limitations
I wanted something to work well with hinglish data as it is being used in India mostly.
The training data was not much as expected
#### How to use
##... | [
"# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'",
"## Model description\n\nTrained small amount of reviews dataset",
"## Intended uses & limitations\n\nI wanted something to work well with hinglish data as it is being used in India mostly.\nThe training data was not much as expected",
"... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #sentiment #multilingual #hindi codemix #hinglish #dataset-sail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'",
"## Model... |
text-generation | transformers | ## Generating Chinese poetry by topic.
```python
from transformers import *
tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
prompt= '''<s>田园躬耕'''
length= 84
stop_token='</s>'
temperature = 1.2
repetition_pen... | {} | gaochangkuan/model_dir | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Generating Chinese poetry by topic.
| [
"## Generating Chinese poetry by topic."
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Generating Chinese poetry by topic."
] |
image-classification | transformers |
### What style is that?
This model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training.
Upload a photograph of a building to the ... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | gatecitypreservation/architectural_styles | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### What style is that?
This model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training.
Upload a photograph of a building to the ... | [
"### What style is that?\n\nThis model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training. \n\nUpload a photograph of a building... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### What style is that?\n\nThis model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | gauravtripathy/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7550
* Matthews Correlation: 0.5265
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
sentence-similarity | sentence-transformers |
# gaussfer/test_simcse_new
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model become... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gaussfer/test_simcse_new | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gaussfer/test_simcse_new
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 installed:
Th... | [
"# gaussfer/test_simcse_new\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers instal... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gaussfer/test_simcse_new\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clus... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-finetuned-pubmed", "results": []}]} | gayanin/bart-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-finetuned-pubmed
=====================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5363
* Rouge2 Precision: 0.3459
* Rouge2 Recall: 0.2455
* Rouge2 Fmeasure: 0.2731
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-15
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-15", "results": []}]} | gayanin/bart-mlm-pubmed-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm-pubmed-15
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4822
* Rouge2 Precision: 0.7578
* Rouge2 Recall: 0.5933
* Rouge2 Fmeasure: 0.6511
Model description
-----------------
More info... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-35
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-35", "results": []}]} | gayanin/bart-mlm-pubmed-35 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm-pubmed-35
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9359
* Rouge2 Precision: 0.5451
* Rouge2 Recall: 0.4232
* Rouge2 Fmeasure: 0.4666
Model description
-----------------
More info... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-45
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-45", "results": []}]} | gayanin/bart-mlm-pubmed-45 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm-pubmed-45
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1797
* Rouge2 Precision: 0.4333
* Rouge2 Recall: 0.3331
* Rouge2 Fmeasure: 0.3684
Model description
-----------------
More info... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-medterm
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-medterm", "results": []}]} | gayanin/bart-mlm-pubmed-medterm | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm-pubmed-medterm
=======================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Rouge2 Precision: 0.985
* Rouge2 Recall: 0.7208
* Rouge2 Fmeasure: 0.8088
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed", "results": []}]} | gayanin/bart-mlm-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-mlm-pubmed
===============
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7223
* Rouge2 Precision: 0.6572
* Rouge2 Recall: 0.5164
* Rouge2 Fmeasure: 0.5662
Model description
-----------------
More informatio... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-pubmed-1.1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-pubmed-1.1", "results": []}]} | gayanin/bart-paraphrase-pubmed-1.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-pubmed-1.1
==========================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4236
* Rouge2 Precision: 0.8482
* Rouge2 Recall: 0.673
* Rouge2 Fmeasure: 0.7347
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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-pubmed", "results": []}]} | gayanin/bart-paraphrase-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-pubmed
======================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6340
* Rouge2 Precision: 0.83
* Rouge2 Recall: 0.6526
* Rouge2 Fmeasure: 0.7144
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: 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: 40\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-pubmed", "results": []}]} | gayanin/t5-small-finetuned-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-pubmed
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6131
* Rouge2 Precision: 0.3
* Rouge2 Recall: 0.2152
* Rouge2 Fmeasure: 0.2379
Model description
-----------------
More inf... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-15
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-15", "results": []}]} | gayanin/t5-small-mlm-pubmed-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-mlm-pubmed-15
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5389
* Rouge2 Precision: 0.7165
* Rouge2 Recall: 0.5375
* Rouge2 Fmeasure: 0.5981
Model description
-----------------
More inform... | [
"### 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: 40\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-35
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-35", "results": []}]} | gayanin/t5-small-mlm-pubmed-35 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-mlm-pubmed-35
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1101
* Rouge2 Precision: 0.4758
* Rouge2 Recall: 0.3498
* Rouge2 Fmeasure: 0.3927
Model description
-----------------
More inform... | [
"### 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: 40\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-45
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-45", "results": []}]} | gayanin/t5-small-mlm-pubmed-45 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-mlm-pubmed-45
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6395
* Rouge2 Precision: 0.3383
* Rouge2 Recall: 0.2424
* Rouge2 Fmeasure: 0.2753
Model description
-----------------
More inform... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed", "results": []}]} | gayanin/t5-small-mlm-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-mlm-pubmed
===================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8008
* Rouge2 Precision: 0.6071
* Rouge2 Recall: 0.4566
* Rouge2 Fmeasure: 0.5079
Model description
-----------------
More information ... | [
"### 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: 40\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-paraphrase-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-paraphrase-pubmed", "results": []}]} | gayanin/t5-small-paraphrase-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-paraphrase-pubmed
==========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4032
* Rouge2 Precision: 0.8281
* Rouge2 Recall: 0.6346
* Rouge2 Fmeasure: 0.6996
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: 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: 40\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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... | gbade786/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: 0.2180
* Accuracy: 0.923
* F1: 0.9233
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... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 483413089
- CO2 Emissions (in grams): 210.6348731063569
## Validation Metrics
- Loss: 1.8478657007217407
- Rouge1: 50.5981
- Rouge2: 26.2167
- RougeL: 46.0513
- RougeLsum: 46.061
- Gen Len: 13.5987
## Usage
You can use cURL to access this mod... | {"language": "en", "tags": "autonlp", "datasets": ["gborn/autonlp-data-news-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 210.6348731063569} | gborn/autonlp-news-summarization-483413089 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:gborn/autonlp-data-news-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-gborn/autonlp-data-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 483413089
- CO2 Emissions (in grams): 210.6348731063569
## Validation Metrics
- Loss: 1.8478657007217407
- Rouge1: 50.5981
- Rouge2: 26.2167
- RougeL: 46.0513
- RougeLsum: 46.061
- Gen Len: 13.5987
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 483413089\n- CO2 Emissions (in grams): 210.6348731063569",
"## Validation Metrics\n\n- Loss: 1.8478657007217407\n- Rouge1: 50.5981\n- Rouge2: 26.2167\n- RougeL: 46.0513\n- RougeLsum: 46.061\n- Gen Len: 13.5987",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-gborn/autonlp-data-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 483413089\n- CO2 Emissions (in grams):... |
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. -->
# bert-base-cased-finetuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-cased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | gchhablani/bert-base-cased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-cola
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6747
* Matthews Correlation: 0.5957
The model was fine-tuned to compare google/fnet-base as introduced in... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-mnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | gchhablani/bert-base-cased-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-mnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5721
* Accuracy: 0.8410
The model was fine-tuned to compare google/fnet-base as introduced in this paper ... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-cased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {... | gchhablani/bert-base-cased-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-mrpc
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7132
* Accuracy: 0.8603
* F1: 0.9026
* Combined Score: 0.8814
The model was fine-tuned to compare google/... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-qnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | gchhablani/bert-base-cased-finetuned-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-qnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3986
* Accuracy: 0.9099
The model was fine-tuned to compare google/fnet-base as introduced in this paper ... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-qqp
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-cased-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"... | gchhablani/bert-base-cased-finetuned-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-qqp
=============================
This model is a fine-tuned version of bert-base-cased on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3752
* Accuracy: 0.9084
* F1: 0.8768
* Combined Score: 0.8926
The model was fine-tuned to compare google/fne... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-rte
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name":... | gchhablani/bert-base-cased-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-rte
=============================
This model is a fine-tuned version of bert-base-cased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7260
* Accuracy: 0.6715
The model was fine-tuned to compare google/fnet-base as introduced in this paper aga... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-sst2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | gchhablani/bert-base-cased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-sst2
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3649
* Accuracy: 0.9232
The model was fine-tuned to compare google/fnet-base as introduced in this paper ... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-stsb
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "bert-base-cased-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name... | gchhablani/bert-base-cased-finetuned-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-stsb
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4861
* Pearson: 0.8926
* Spearmanr: 0.8898
* Combined Score: 0.8912
The model was fine-tuned to compare g... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-base-cased-finetuned-wnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | gchhablani/bert-base-cased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-wnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6996
* Accuracy: 0.4648
The model was fine-tuned to compare google/fnet-base as introduced in this paper ... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# bert-large-cased-finetuned-cola
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-large-cased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "... | gchhablani/bert-large-cased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-large-cased-finetuned-cola
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8385
* Matthews Correlation: 0.5957
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# bert-large-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-large-cased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type":... | gchhablani/bert-large-cased-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-large-cased-finetuned-mrpc
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6274
* Accuracy: 0.6838
* F1: 0.8122
* Combined Score: 0.7480
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# bert-large-cased-finetuned-rte
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased)... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue",... | gchhablani/bert-large-cased-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-large-cased-finetuned-rte
==============================
This model is a fine-tuned version of bert-large-cased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5187
* Accuracy: 0.6643
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# bert-large-cased-finetuned-wnli
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue... | gchhablani/bert-large-cased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-large-cased-finetuned-wnli
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7087
* Accuracy: 0.3521
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: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-base-finetuned-cola
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-base-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {... | gchhablani/fnet-base-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-cola
========================
This model is a fine-tuned version of google/fnet-base on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5929
* Matthews Correlation: 0.3594
The model was fine-tuned to compare google/fnet-base as introduced in this paper... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-mnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLU... | gchhablani/fnet-base-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-mnli
========================
This model is a fine-tuned version of google/fnet-base on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6443
* Accuracy: 0.7675
The model was fine-tuned to compare google/fnet-base as introduced in this paper against ber... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-mrpc
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-base-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | gchhablani/fnet-base-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-mrpc
========================
This model is a fine-tuned version of google/fnet-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9653
* Accuracy: 0.7721
* F1: 0.8502
* Combined Score: 0.8112
The model was fine-tuned to compare google/fnet-base a... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-qnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLU... | gchhablani/fnet-base-finetuned-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-qnli
========================
This model is a fine-tuned version of google/fnet-base on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4746
* Accuracy: 0.8439
The model was fine-tuned to compare google/fnet-base as introduced in this paper against ber... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-qqp
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-base-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name":... | gchhablani/fnet-base-finetuned-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-qqp
=======================
This model is a fine-tuned version of google/fnet-base on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3686
* Accuracy: 0.8847
* F1: 0.8466
* Combined Score: 0.8657
The model was fine-tuned to compare google/fnet-base as i... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-rte
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE... | gchhablani/fnet-base-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-rte
=======================
This model is a fine-tuned version of google/fnet-base on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6978
* Accuracy: 0.6282
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-b... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-sst2
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLU... | gchhablani/fnet-base-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"rust",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #rust #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-sst2
========================
This model is a fine-tuned version of google/fnet-base on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4674
* Accuracy: 0.8945
The model was fine-tuned to compare google/fnet-base as introduced in this paper against ber... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #rust #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpara... |
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. -->
# fnet-base-finetuned-stsb
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "fnet-base-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GL... | gchhablani/fnet-base-finetuned-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-stsb
========================
This model is a fine-tuned version of google/fnet-base on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7894
* Pearson: 0.8256
* Spearmanr: 0.8219
* Combined Score: 0.8238
The model was fine-tuned to compare google/fnet-... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-base-finetuned-wnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLU... | gchhablani/fnet-base-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03824"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-base-finetuned-wnli
========================
This model is a fine-tuned version of google/fnet-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6887
* Accuracy: 0.5493
The model was fine-tuned to compare google/fnet-base as introduced in this paper against ber... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
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. -->
# fnet-large-finetuned-cola-copy
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-larg... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-large-finetuned-cola-copy", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "t... | gchhablani/fnet-large-finetuned-cola-copy | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-cola-copy
==============================
This model is a fine-tuned version of google/fnet-large on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6243
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-cola-copy2
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-lar... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-large-finetuned-cola-copy2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "... | gchhablani/fnet-large-finetuned-cola-copy2 | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-cola-copy2
===============================
This model is a fine-tuned version of google/fnet-large on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6173
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-cola-copy3
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-lar... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-large-finetuned-cola-copy3", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "... | gchhablani/fnet-large-finetuned-cola-copy3 | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-cola-copy3
===============================
This model is a fine-tuned version of google/fnet-large on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6554
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-cola-copy4
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-lar... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-large-finetuned-cola-copy4", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "... | gchhablani/fnet-large-finetuned-cola-copy4 | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-cola-copy4
===============================
This model is a fine-tuned version of google/fnet-large on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6500
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* num\\_epochs: 3.0",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-cola
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-large-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type":... | gchhablani/fnet-large-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-cola
=========================
This model is a fine-tuned version of google/fnet-large on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6243
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-mrpc
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-large-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue... | gchhablani/fnet-large-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-mrpc
=========================
This model is a fine-tuned version of google/fnet-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0872
* Accuracy: 0.8260
* F1: 0.8799
* Combined Score: 0.8529
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-qqp
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-large-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue",... | gchhablani/fnet-large-finetuned-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-qqp
========================
This model is a fine-tuned version of google/fnet-large on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5515
* Accuracy: 0.8943
* F1: 0.8557
* Combined Score: 0.8750
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-rte
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-large-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args... | gchhablani/fnet-large-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-rte
========================
This model is a fine-tuned version of google/fnet-large on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7528
* Accuracy: 0.6426
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-sst2
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-large-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "ar... | gchhablani/fnet-large-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-sst2
=========================
This model is a fine-tuned version of google/fnet-large on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5240
* Accuracy: 0.9048
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-stsb
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "fnet-large-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "a... | gchhablani/fnet-large-finetuned-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-stsb
=========================
This model is a fine-tuned version of google/fnet-large on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6250
* Pearson: 0.8554
* Spearmanr: 0.8533
* Combined Score: 0.8543
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# fnet-large-finetuned-wnli
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-large-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "ar... | gchhablani/fnet-large-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| fnet-large-finetuned-wnli
=========================
This model is a fine-tuned version of google/fnet-large on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6953
* Accuracy: 0.3803
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hakha-Chin
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hakha Chin using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage... | {"language": "cnh", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Hakha Chin by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", ... | gchhablani/wav2vec2-large-xlsr-cnh | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"cnh",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"cnh"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cnh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Hakha-Chin
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hakha Chin using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can b... | [
"# Wav2Vec2-Large-XLSR-53-Hakha-Chin\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hakha Chin using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cnh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Hakha-Chin\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hakha Chin using ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Esperanto
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
... | {"language": "eo", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Esperanto by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "n... | gchhablani/wav2vec2-large-xlsr-eo | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eo",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Esperanto
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Esperanto using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be ... | [
"# Wav2Vec2-Large-XLSR-53-Esperanto\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Esperanto using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nT... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Esperanto\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Esperanto using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Gujarati
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Gujarati using the [OpenSLR SLR78](http://openslr.org/78/) dataset. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used d... | {"language": "gu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Gujarati by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "na... | gchhablani/wav2vec2-large-xlsr-gu | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"gu",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gu"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #gu #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Gujarati
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Gujarati using the OpenSLR SLR78 dataset. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows, assuming you have a dataset with Guja... | [
"# Wav2Vec2-Large-XLSR-53-Gujarati\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Gujarati using the OpenSLR SLR78 dataset. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows, assuming you have a datase... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #gu #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Gujarati\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Gujarati using the OpenSL... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
... | {"language": "hu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Hungarian by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "n... | gchhablani/wav2vec2-large-xlsr-hu | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be ... | [
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nT... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Interlingua
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Interlingua using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mode... | {"language": "ia", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Interlingua by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recogniti... | gchhablani/wav2vec2-large-xlsr-ia | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ia",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ia"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ia #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Interlingua
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as... | [
"# Wav2Vec2-Large-XLSR-53-Interlingua\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model ca... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ia #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Interlingua\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Interlingua using t... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Italian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "it", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Italian by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | gchhablani/wav2vec2-large-xlsr-it | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"it",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #it #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Italian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Italian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-Italian\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Italian using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mod... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #it #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Italian\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Italian using the Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using a part of the [InterSpeech 2021 Marathi](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) dataset. When using this model, make sure that your speech i... | {"language": "mr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["interspeech_2021_asr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Marathi 2 by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-rec... | gchhablani/wav2vec2-large-xlsr-mr-2 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mr",
"dataset:interspeech_2021_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-interspeech_2021_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows, assuming you have ... | [
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows, assumin... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-interspeech_2021_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset and [InterSpeech 2021](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) Marathi datasets. Note tha... | {"language": "mr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr", "interspeech_2021_asr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani", "results": [{"task": {"type": "automatic-s... | gchhablani/wav2vec2-large-xlsr-mr-3 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mr",
"dataset:openslr",
"dataset:interspeech_2021_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #dataset-interspeech_2021_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the OpenSLR SLR64 dataset and InterSpeech 2021 Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. When using this model, make sure that yo... | [
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the OpenSLR SLR64 dataset and InterSpeech 2021 Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. When using this model, make sure t... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #dataset-interspeech_2021_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi u... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, al... | {"language": "mr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | gchhablani/wav2vec2-large-xlsr-mr | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mr",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the OpenSLR SLR64 dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that ... | [
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the OpenSLR SLR64 dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Marathi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the OpenSLR ... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | {"language": "or", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Odia by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "n... | gchhablani/wav2vec2-large-xlsr-or | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"or",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"or"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on th... | [
"# Wav2Vec2-Large-XLSR-53-Odia\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Odia\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Odia using the Common Voic... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Portugese by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "n... | gchhablani/wav2vec2-large-xlsr-pt | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"pt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can b... | [
"# Wav2Vec2-Large-XLSR-53-Portuguese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Portuguese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using t... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilvan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16... | {"language": "rm-sursilv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Large 53 Romansh Sursilvan by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-... | gchhablani/wav2vec2-large-xlsr-rm-sursilv | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"rm-sursilv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilvan using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
T... | [
"# Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilvan using the Common Voice dataset. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"##... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romansh Sursilv... |
fill-mask | transformers |
# GreekSocialBERT
## Model description
A Greek language model based on [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1)
## Training data
The training data is a corpus of 458,293 documents collected from Greek social media accounts.
The training corpus has been collected and provided by [Pal... | {"language": "el"} | gealexandri/greeksocialbert-base-greek-uncased-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"el",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #el #autotrain_compatible #endpoints_compatible #region-us
|
# GreekSocialBERT
## Model description
A Greek language model based on GreekBERT
## Training data
The training data is a corpus of 458,293 documents collected from Greek social media accounts.
The training corpus has been collected and provided by Palo LTD
## Eval results
### BibTeX entry and citation info
| [
"# GreekSocialBERT",
"## Model description\n\nA Greek language model based on GreekBERT",
"## Training data\n\nThe training data is a corpus of 458,293 documents collected from Greek social media accounts. \n\nThe training corpus has been collected and provided by Palo LTD",
"## Eval results",
"### BibTeX e... | [
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #el #autotrain_compatible #endpoints_compatible #region-us \n",
"# GreekSocialBERT",
"## Model description\n\nA Greek language model based on GreekBERT",
"## Training data\n\nThe training data is a corpus of 458,293 documents collected from Greek social media... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.