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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-binary | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous... | [
"# ALBERT Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\nALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles fro... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALBERT Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\nALBERT-Persian is ... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-binary | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized ... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classe... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-multi | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized ... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### DeepSentiPers\n\n\nwhich is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classe... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-digikala | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### Digikala\n\n\nDigikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained as compared to other models and... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Digikala\n\n\nDigikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:\n\n\n\nDownload\nY... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-multi | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous... | [
"# ALBERT Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\nALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles fro... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALBERT Persian\n\nA Lite BERT for Self-supervised Learning of Language Representations for the Persian Language\n\n> میتونی بهش بگی برت_کوچولو\n\nALBERT-Persian is ... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-sentiment-snappfood | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### SnappFood\n\n\nSnappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):\n\n\n1. Happy\n2. Sad\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score obtained as co... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### SnappFood\n\n\nSnappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):\n\n\n1. Happy\... |
fill-mask | transformers |
# ALBERT-Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
## Introduction
ALBERT-Persian trained on a massive amount of public corpora ([Persian Wikidumps](https://dumps.wikimedia.org/fawiki/), [MirasText](https://github.com/miras-tech... | {"language": "fa", "license": "apache-2.0", "tags": ["albert-persian", "persian-lm"]} | m3hrdadfi/albert-fa-base-v2 | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"albert-persian",
"persian-lm",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #albert #fill-mask #albert-persian #persian-lm #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT-Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
Introduction
------------
ALBERT-Persian trained on a massive amount of public corpora (Persian Wikidumps, MirasText) and six other manually crawl... | [
"### How to use\n\n\n* for using any type of Albert you have to install sentencepiece\n* run this in your notebook",
"#### TensorFlow 2.0",
"#### Pytorch\n\n\nTraining\n--------\n\n\nALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versi... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #albert-persian #persian-lm #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\n* for using any type of Albert you have to install sentencepiece\n* run this in your notebook",
"#### TensorFlow 2.0",
"#### Pyt... |
feature-extraction | sentence-transformers |
# Sentence Embeddings with `albert-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
pip install -U sentencepiece
```
Then you can use the model like this:
```python
... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | m3hrdadfi/albert-zwnj-wnli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Sentence Embeddings with 'albert-zwnj-wnli-mean-tokens'
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First,... | [
"# Sentence Embeddings with 'albert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model l... | [
"TAGS\n#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sentence Embeddings with 'albert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:... |
feature-extraction | transformers |
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo.
```bibtex
@article{amirkhan... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/bert-fa-base-uncased-farstail-mean-tokens | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"fa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us
|
# FarsTail + ParsBERT
Please follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the Sentence-Transformer repo.
| [
"# FarsTail + ParsBERT\n\nPlease follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the Sentence-Transformer repo."
] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# FarsTail + ParsBERT\n\nPlease follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the Sentence-Transfor... |
text-classification | transformers |
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo
```bibtex
@article{amirkhan... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/bert-fa-base-uncased-farstail | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FarsTail + ParsBERT
Please follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the Sentence-Transformer repo
| [
"# FarsTail + ParsBERT\n\nPlease follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the Sentence-Transformer repo"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FarsTail + ParsBERT\n\nPlease follow the FarsTail repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check ou... |
feature-extraction | transformers |
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title = {Sentence Embeddings with ParsBERT},
y... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/bert-fa-base-uncased-wikinli-mean-tokens | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"fa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us
|
# ParsBERT + Sentence Transformers
Please follow the Sentence-Transformer repo for the latest information about previous and current models.
| [
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] |
text-classification | transformers |
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title = {Sentence Embeddings with ParsBERT},
y... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/bert-fa-base-uncased-wikinli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ParsBERT + Sentence Transformers
Please follow the Sentence-Transformer repo for the latest information about previous and current models.
| [
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] |
feature-extraction | transformers |
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title = {Sentence Embeddings with ParsBERT},
y... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/bert-fa-base-uncased-wikitriplet-mean-tokens | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"fa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us
|
# ParsBERT + Sentence Transformers
Please follow the Sentence-Transformer repo for the latest information about previous and current models.
| [
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #fa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ParsBERT + Sentence Transformers\n\nPlease follow the Sentence-Transformer repo for the latest information about previous and current models."
] |
feature-extraction | sentence-transformers |
# Sentence Embeddings with `bert-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers impo... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | m3hrdadfi/bert-zwnj-wnli-mean-tokens | 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
|
# Sentence Embeddings with 'bert-zwnj-wnli-mean-tokens'
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, y... | [
"# Sentence Embeddings with 'bert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model lik... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sentence Embeddings with 'bert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\n... |
summarization | transformers |
A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.
For more detail, please follow the [News Headline Generation](https://github.com/m3hrdadfi/news-headline-generation) repo.
## Eval results
The following t... | {"language": "fa", "license": "apache-2.0", "tags": ["summarization"]} | m3hrdadfi/bert2bert-fa-news-headline | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.
For more detail, please follow the News Headline Generation repo.
Eval results
------------
The following table summarizes the ROUGE scores obtained by the... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
A Bert2Bert model on the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score.
For more detail, please follow the [Wiki Summary](https://github.com/m3hrdadfi/wiki-summary) repo.
## Eval results
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.
| ... | {"language": "fa", "license": "apache-2.0", "tags": ["summarization"]} | m3hrdadfi/bert2bert-fa-wiki-summary | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| A Bert2Bert model on the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score.
For more detail, please follow the Wiki Summary repo.
Eval results
------------
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.
Questions?
----------
Post a Github i... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# Sentence Embeddings with `distilbert-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformer... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "\u0645\u0631\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062e\u0648\u0631\u062f\u0646 \u067e\u0627\u0633\u062a\u0627 \u0627\u0633\u062a.", "sentences":... | m3hrdadfi/distilbert-zwnj-wnli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens'
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: Fi... | [
"# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the mod... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers in... |
text-generation | transformers |
# GPT2 QA
Using GPT2 in other downstream NLP tasks like QA. The model was trained and evaluated on [squad](https://huggingface.co/datasets/squad).
## Dataset
- [squad](https://huggingface.co/datasets/squad)
## Evaluation
The following table summarizes the scores obtained by the model.
## Demo
[Streamlit GPT2 QA](h... | {"language": "en", "tags": ["text-generation"], "datasets": ["squad"]} | m3hrdadfi/gpt2-QA | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"en",
"dataset:squad",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #en #dataset-squad #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2 QA
Using GPT2 in other downstream NLP tasks like QA. The model was trained and evaluated on squad.
## Dataset
- squad
## Evaluation
The following table summarizes the scores obtained by the model.
## Demo
Streamlit GPT2 QA
## How to use
TODO (will be filled shortly)... | [
"# GPT2 QA\nUsing GPT2 in other downstream NLP tasks like QA. The model was trained and evaluated on squad.",
"## Dataset\n- squad",
"## Evaluation\n\nThe following table summarizes the scores obtained by the model.",
"## Demo\nStreamlit GPT2 QA",
"## How to use\nTODO (will be filled shortly)..."
] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #en #dataset-squad #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2 QA\nUsing GPT2 in other downstream NLP tasks like QA. The model was trained and evaluated on squad.",
"## Dataset\n- squad",
"##... |
text-generation | transformers |
# GPT2 QA - Persian
It is a new approach to using GPT2 in other downstream NLP tasks like QA. The model was trained on PersianQA and evaluated on PersianQA and PersiNLU (Reading Comprehension).
## Dataset
- [PersianQA](https://github.com/sajjjadayobi/PersianQA)
- [ParsiNLU](https://github.com/persiannlp/parsinlu)
##... | {"language": "fa", "tags": ["text-generation"], "datasets": ["persian_qa", "parsinlu_reading_comprehension"], "widget": [{"text": "\u0642\u0631\u0627\u0631\u062f\u0627\u062f \u06a9\u0631\u0633\u0646\u062a \u0642\u0631\u0627\u0631\u062f\u0627\u062f\u06cc \u0628\u0631\u0627\u06cc \u0641\u0631\u0648\u0634 \u0631\u0648\u06... | m3hrdadfi/gpt2-persian-qa | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"fa",
"dataset:persian_qa",
"dataset:parsinlu_reading_comprehension",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #fa #dataset-persian_qa #dataset-parsinlu_reading_comprehension #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT2 QA - Persian
=================
It is a new approach to using GPT2 in other downstream NLP tasks like QA. The model was trained on PersianQA and evaluated on PersianQA and PersiNLU (Reading Comprehension).
Dataset
-------
* PersianQA
* ParsiNLU
Evaluation
----------
The following table summarizes the scor... | [] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #fa #dataset-persian_qa #dataset-parsinlu_reading_comprehension #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | transformers |
# Emotion Recognition in Greek (el) Speech using HuBERT
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
```bash
!git clone ht... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "speech", "speech-emotion-recognition"], "datasets": ["aesdd"]} | m3hrdadfi/hubert-base-greek-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"hubert",
"audio",
"speech",
"speech-emotion-recognition",
"el",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #region-us
| Emotion Recognition in Greek (el) Speech using HuBERT
=====================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a Gith... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each clas... |
null | transformers |
# Emotion Recognition in Persian (fa) Speech using HuBERT
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
```bash
!git clone ... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "speech", "speech-emotion-recognition"], "datasets": ["ShEMO"]} | m3hrdadfi/hubert-base-persian-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"hubert",
"audio",
"speech",
"speech-emotion-recognition",
"fa",
"dataset:ShEMO",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #fa #dataset-ShEMO #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Emotion Recognition in Persian (fa) Speech using HuBERT
=======================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a ... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #fa #dataset-ShEMO #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and pe... |
null | transformers |
# Emotion Recognition in Persian (fa) Speech using HuBERT
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
```bash
!git clone ... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "speech", "speech-gender-recognition"], "datasets": ["shemo"]} | m3hrdadfi/hubert-base-persian-speech-gender-recognition | null | [
"transformers",
"pytorch",
"hubert",
"audio",
"speech",
"speech-gender-recognition",
"fa",
"dataset:shemo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #hubert #audio #speech #speech-gender-recognition #fa #dataset-shemo #license-apache-2.0 #endpoints_compatible #region-us
| Emotion Recognition in Persian (fa) Speech using HuBERT
=======================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a ... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #hubert #audio #speech #speech-gender-recognition #fa #dataset-shemo #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class... |
null | transformers |
# Emotion Recognition in Greek (el) Speech using HuBERT
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
```bash
!git clone ht... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "speech", "speech-emotion-recognition"], "datasets": ["aesdd"]} | m3hrdadfi/hubert-large-greek-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"hubert",
"audio",
"speech",
"speech-emotion-recognition",
"el",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #region-us
| Emotion Recognition in Greek (el) Speech using HuBERT
=====================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a Gith... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #hubert #audio #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each clas... |
token-classification | transformers |
# IcelandicNER BERT
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities:
- Date
- Location
- Miscellaneous
- M... | {"language": "is", "license": "apache-2.0", "widget": [{"text": "Kristin manneskja getur ekki lagt fr\u00e1sagnir af Jes\u00fa Kristi \u00e1 hilluna vegna \u00feess a\u00f0 h\u00fan s\u00e9 b\u00fain a\u00f0 lesa \u00fe\u00e6r ."}, {"text": "Til hvers a\u00f0 kj\u00f3sa flokk , sem \u00feykist vera Jafna\u00f0armannafl... | m3hrdadfi/icelandic-ner-bert | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"is",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| IcelandicNER BERT
=================
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The MIM-GOLD-NER corpus was developed at Reykjavik University in 2018–2020 that covered eight types of entities:
* Date
* Location
* Miscellaneous
* Money
* Organization
* Percent
* Person
* Time
... | [
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] |
token-classification | transformers |
# IcelandicNER DistilBERT
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities:
- Date
- Location
- Miscellaneou... | {"language": "is", "license": "apache-2.0", "widget": [{"text": "Kristin manneskja getur ekki lagt fr\u00e1sagnir af Jes\u00fa Kristi \u00e1 hilluna vegna \u00feess a\u00f0 h\u00fan s\u00e9 b\u00fain a\u00f0 lesa \u00fe\u00e6r ."}, {"text": "Til hvers a\u00f0 kj\u00f3sa flokk , sem \u00feykist vera Jafna\u00f0armannafl... | m3hrdadfi/icelandic-ner-distilbert | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"is",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| IcelandicNER DistilBERT
=======================
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The MIM-GOLD-NER corpus was developed at Reykjavik University in 2018–2020 that covered eight types of entities:
* Date
* Location
* Miscellaneous
* Money
* Organization
* Percent
* Pers... | [
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] |
token-classification | transformers |
# IcelandicNER RoBERTa
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities:
- Date
- Location
- Miscellaneous
... | {"language": "is", "license": "apache-2.0", "widget": [{"text": "Kristin manneskja getur ekki lagt fr\u00e1sagnir af Jes\u00fa Kristi \u00e1 hilluna vegna \u00feess a\u00f0 h\u00fan s\u00e9 b\u00fain a\u00f0 lesa \u00fe\u00e6r ."}, {"text": "Til hvers a\u00f0 kj\u00f3sa flokk , sem \u00feykist vera Jafna\u00f0armannafl... | m3hrdadfi/icelandic-ner-roberta | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"token-classification",
"is",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #tf #roberta #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| IcelandicNER RoBERTa
====================
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
The MIM-GOLD-NER corpus was developed at Reykjavik University in 2018–2020 that covered eight types of entities:
* Date
* Location
* Miscellaneous
* Money
* Organization
* Percent
* Person
* T... | [
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] | [
"TAGS\n#transformers #pytorch #tf #roberta #token-classification #is #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Installing requirements",
"### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the IcelandicNER Issues repo."
] |
summarization | transformers |
A b2b-shared model on the pnSummary dataset to summarize articles.
## Eval results | {"language": "fa", "tags": ["summarization"], "datasets": ["pn_summary"], "widget": [{"text": "\u0628\u0627\u0628 \u0627\u062f\u0646\u06a9\u06cc\u0631\u06a9 \u0628\u0627\u0632\u06cc\u06af\u0631 \u06f5\u06f8 \u0633\u0627\u0644\u0647\u200c \u0622\u0645\u0631\u06cc\u06a9\u0627\u06cc\u06cc \u06a9\u0647 \u0633\u0627\u0644\u... | HooshvareLab/pn-summary-b2b-shared | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"dataset:pn_summary",
"doi:10.57967/hf/1662",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1662 #autotrain_compatible #endpoints_compatible #region-us
|
A b2b-shared model on the pnSummary dataset to summarize articles.
## Eval results | [
"## Eval results"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1662 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Eval results"
] |
summarization | transformers |
An mT5-base model on the pnSummary dataset to summarize articles.
## Eval results
The following table summarizes the ROUGE scores obtained by the model for the validation set.
```text
+-----------+------+-----------+--------+-----------+
| Score | Type | Precision | Recall | F-Measure |
+-----------+------+---... | {"language": "fa", "tags": ["summarization", "mt5"], "datasets": ["pn_summary"], "pipeline_tag": "summarization", "widget": [{"text": "\u0628\u0627\u0628 \u0627\u062f\u0646\u06a9\u06cc\u0631\u06a9 \u0628\u0627\u0632\u06cc\u06af\u0631 \u06f5\u06f8 \u0633\u0627\u0644\u0647\u200c \u0622\u0645\u0631\u06cc\u06a9\u0627\u06cc... | HooshvareLab/pn-summary-mt5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"fa",
"dataset:pn_summary",
"doi:10.57967/hf/1661",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1661 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
An mT5-base model on the pnSummary dataset to summarize articles.
## Eval results
The following table summarizes the ROUGE scores obtained by the model for the validation set.
## Test results
The following table summarizes the ROUGE scores obtained by the model for the test set.
| [
"## Eval results\n\nThe following table summarizes the ROUGE scores obtained by the model for the validation set.",
"## Test results\n\nThe following table summarizes the ROUGE scores obtained by the model for the test set."
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1661 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Eval results\n\nThe following table summarizes the ROUGE scores obtained by the model for the validation ... |
summarization | transformers |
An mT5-small model on the pnSummary dataset to summarize articles.
## Eval results
The following table summarizes the ROUGE scores obtained by the model for the validation set.
```text
+-----------+------+-----------+--------+-----------+
| Score | Type | Precision | Recall | F-Measure |
+-----------+------+-... | {"language": "fa", "tags": ["summarization", "mt5"], "datasets": ["pn_summary"], "pipeline_tag": "summarization", "widget": [{"text": "\u0628\u0627\u0628 \u0627\u062f\u0646\u06a9\u06cc\u0631\u06a9 \u0628\u0627\u0632\u06cc\u06af\u0631 \u06f5\u06f8 \u0633\u0627\u0644\u0647\u200c \u0622\u0645\u0631\u06cc\u06a9\u0627\u06cc... | HooshvareLab/pn-summary-mt5-small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"fa",
"dataset:pn_summary",
"doi:10.57967/hf/1660",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1660 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
An mT5-small model on the pnSummary dataset to summarize articles.
## Eval results
The following table summarizes the ROUGE scores obtained by the model for the validation set.
## Test results
The following table summarizes the ROUGE scores obtained by the model for the test set.
| [
"## Eval results\n\nThe following table summarizes the ROUGE scores obtained by the model for the validation set.",
"## Test results\n\nThe following table summarizes the ROUGE scores obtained by the model for the test set."
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #fa #dataset-pn_summary #doi-10.57967/hf/1660 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Eval results\n\nThe following table summarizes the ROUGE scores obtained by the model for the validation ... |
feature-extraction | sentence-transformers |
# Sentence Embeddings with `roberta-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers i... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | m3hrdadfi/roberta-zwnj-wnli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Sentence Embeddings with 'roberta-zwnj-wnli-mean-tokens'
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First... | [
"# Sentence Embeddings with 'roberta-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sentence Embeddings with 'roberta-zwnj-wnli-mean-tokens'",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installe... |
token-classification | transformers |
# Typo Detector
## Dataset Information
For this specific task, I used [NeuSpell](https://github.com/neuspell/neuspell) corpus as my raw data.
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| # | precision | recall | f1-score | support |
|:-----... | {"language": "en", "widget": [{"text": "He had also stgruggled with addiction during his time in Congress ."}, {"text": "The review thoroughla assessed all aspects of JLENS SuR and CPG esign maturit and confidence ."}, {"text": "Letterma also apologized two his staff for the satyation ."}, {"text": "Vincent Jay had ear... | m3hrdadfi/typo-detector-distilbert-en | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| Typo Detector
=============
Dataset Information
-------------------
For this specific task, I used NeuSpell corpus as my raw data.
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
How to use
----------
You use this model with Transformers pipelin... | [
"### Installing requirements",
"### Prediction using pipeline\n\n\nOutput:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the TypoDetector Issues repo."
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Installing requirements",
"### Prediction using pipeline\n\n\nOutput:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the TypoDetector Issues repo."
] |
token-classification | transformers |
# Typo Detector For Icelandic 🇮🇸
## Dataset Information
Synthetic data for this specific task.
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| # | precision | recall | f1-score | support |
|:------------:|:---------:|:--------:|:--------:|:--... | {"language": "is", "widget": [{"text": "P\u00e1li, vini m\u00ednum, langa\u00f0i a\u00f0 horfa \u00e1 sj\u00f3nnvarpi\u00f0."}, {"text": "Leggir \u00feci\u00f0ursins eru \u00feaktir fj\u00f6\u00f0rum til ba\u00f0 edravn fuglnn gekgn kuldanu\u00e9 ."}, {"text": "\u00dear hitta \u00feeir konu Bj\u00f6rns og segir ovs :"}... | m3hrdadfi/typo-detector-distilbert-is | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"is",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #is #autotrain_compatible #endpoints_compatible #has_space #region-us
| Typo Detector For Icelandic 🇮🇸
==============================
Dataset Information
-------------------
Synthetic data for this specific task.
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
How to use
----------
You use this model with Transfor... | [
"### Installing requirements",
"### Prediction using pipeline\n\n\nOutput:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the TypoDetector Issues repo."
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #token-classification #is #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Installing requirements",
"### Prediction using pipeline\n\n\nOutput:\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the TypoDetector Issues repo."
] |
automatic-speech-recognition | transformers |
# Eating Sound Classification using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Prediction
```python
imp... | {"tags": ["audio", "automatic-speech-recognition", "audio-classification"]} | m3hrdadfi/wav2vec2-base-100k-eating-sound-collection | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"audio-classification",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #audio-classification #endpoints_compatible #region-us
| Eating Sound Classification using Wav2Vec 2.0
=============================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a Github issue from HE... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #audio-classification #endpoints_compatible #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\... |
automatic-speech-recognition | transformers |
# Music Genre Classification using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Prediction
```python
impo... | {"tags": ["audio", "automatic-speech-recognition", "audio-classification"]} | m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"audio-classification",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #audio-classification #endpoints_compatible #has_space #region-us
| Music Genre Classification using Wav2Vec 2.0
============================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
Post a Github issue from HERE... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #audio-classification #endpoints_compatible #has_space #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\n... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian using [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 b... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"label": "Common Voice sample 1123", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample1123.flac"}, {"label":... | m3hrdadfi/wav2vec2-large-xlsr-estonian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"et",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian using 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:
Requirements
Prediction
Output:
## Evaluat... | [
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian using Common Voice. When 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:\n\nRequirements\n\n\n\nPrediction\n\n\nOu... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Estonian using Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian using [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... | {"language": "ka", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"example_title": "Common Voice sample 566", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-georgian/resolve/main/sample566.flac"}, {"e... | m3hrdadfi/wav2vec2-large-xlsr-georgian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ka",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ka"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian using 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:
Requirements
Normalizer
Prediction
Output:
... | [
"# Wav2Vec2-Large-XLSR-53-Georgian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian using Common Voice. When 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:\n\nRequirements\n\n\nNormalizer\n\n\nPred... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Georgian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Icelandic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Icelandic using [Malromur](https://clarin.is/en/resources/malromur/). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | {"language": "is", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["malromur"], "widget": [{"example_title": "Malromur sample 1608", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample1608.flac"}, {"exampl... | m3hrdadfi/wav2vec2-large-xlsr-icelandic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"is",
"dataset:malromur",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #is #dataset-malromur #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Icelandic
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Icelandic using Malromur. 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:
Requirements
Normalizer
Prediction
Output:
#... | [
"# Wav2Vec2-Large-XLSR-53-Icelandic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Icelandic using Malromur. When 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:\n\nRequirements\n\n\nNormalizer\n\n\nPredic... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #is #dataset-malromur #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Icelandic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Icelandic using Malromu... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using [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": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"example_title": "Common Voice sample 11", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/resolve/main/sample11.flac"}, {"e... | m3hrdadfi/wav2vec2-large-xlsr-lithuanian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Lithuanian using 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:
Requirements
Normalizer
Prediction
Output... | [
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Lithuanian using Common Voice. When 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:\n\nRequirements\n\n\nNormalizer\n\n\n... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Lithuanian using C... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Persian ShEMO
Fine-tuned [Wav2Vec2-Large-XLSR-53-Persian V2](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v2) in Persian (Farsi) using [ShEMO](https://www.kaggle.com/mansourehk/shemo-persian-speech-emotion-detection-database). When using this model, make sure that your speech ... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["shemo"], "widget": [{"label": "ShEMO sample 250", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample250.flac"}, {"label": "ShEMO sam... | m3hrdadfi/wav2vec2-large-xlsr-persian-shemo | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:shemo",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-shemo #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Persian ShEMO
Fine-tuned Wav2Vec2-Large-XLSR-53-Persian V2 in Persian (Farsi) using ShEMO. 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:
Requirements
Prediction
Output:
## Ev... | [
"# Wav2Vec2-Large-XLSR-53-Persian ShEMO\n\nFine-tuned Wav2Vec2-Large-XLSR-53-Persian V2 in Persian (Farsi) using ShEMO. When 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:\n\nRequirements\n\n\n\nPrediction\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-shemo #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Persian ShEMO\n\nFine-tuned Wav2Vec2-Large-XLSR-53-Persian V2 in Persian (Farsi) usin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Persian V2
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mod... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"label": "Common Voice sample 4024", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v2/resolve/main/sample4024.flac"}, {"label... | m3hrdadfi/wav2vec2-large-xlsr-persian-v2 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Persian V2
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using 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:
Requirements
Prediction
Output:
## ... | [
"# Wav2Vec2-Large-XLSR-53-Persian V2\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using Common Voice. When 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:\n\nRequirements\n\n\n\nPredictio... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Persian V2\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) us... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Persian V3
## Usage
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz.
**Req... | {"language": "fa", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample1.flac"}, {"example_title": "Common Voic... | m3hrdadfi/wav2vec2-large-xlsr-persian-v3 | null | [
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Persian V3
## Usage
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.
Requirements
Normalizer
Downloading data
Cleaning
Prediction
WER Score
Output
## Evaluation
Te... | [
"# Wav2Vec2-Large-XLSR-53-Persian V3",
"## Usage\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.\n\n\nRequirements\n\n\nNormalizer\n\n\nDownloading data\n\n\nCleaning\n\n\nPrediction\n\n\nWER Score\n\n\... | [
"TAGS\n#transformers #pytorch #tf #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Persian V3",
"## Usage\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Persian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [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 ... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"example_title": "Common Voice sample 687", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac"}, {"ex... | m3hrdadfi/wav2vec2-large-xlsr-persian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Persian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using 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:
Requirements
Prediction
Output:
## Eva... | [
"# Wav2Vec2-Large-XLSR-53-Persian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (Farsi) using Common Voice. When 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:\n\nRequirements\n\n\n\nPrediction\n... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fa #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Persian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Persian (F... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Turkish using [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 u... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "widget": [{"label": "Common Voice sample 1378", "src": "https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-turkish/resolve/main/sample1378.flac"}, {"label": ... | m3hrdadfi/wav2vec2-large-xlsr-turkish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Turkish using 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:
Requirements
Prediction
Output:
## Evaluation... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Turkish using Common Voice. When 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:\n\nRequirements\n\n\n\nPrediction\n\n\nOutp... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Turkish us... |
automatic-speech-recognition | transformers |
# Emotion Recognition in Greek (el) Speech using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Prediction
... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "speech-emotion-recognition"], "datasets": ["aesdd"]} | m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"speech",
"speech-emotion-recognition",
"el",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #audio #automatic-speech-recognition #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Emotion Recognition in Greek (el) Speech using Wav2Vec 2.0
==========================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Questions?
----------
P... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #audio #automatic-speech-recognition #speech #speech-emotion-recognition #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the sc... |
automatic-speech-recognition | transformers |
# Emotion Recognition in Persian (Farsi - fa) Speech using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Pr... | {"language": "fa", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "speech-emotion-recognition"], "datasets": ["ShEMO"]} | m3hrdadfi/wav2vec2-xlsr-persian-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"speech",
"speech-emotion-recognition",
"fa",
"dataset:ShEMO",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #speech-emotion-recognition #fa #dataset-ShEMO #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Emotion Recognition in Persian (Farsi - fa) Speech using Wav2Vec 2.0
====================================================================
How to use
----------
### Requirements
### Prediction
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Quest... | [
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #speech-emotion-recognition #fa #dataset-ShEMO #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Requirements",
"### Prediction\n\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores ... |
question-answering | transformers |
# XLM-RoBERTa large for QA (PersianQA - 🇮🇷)
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the [PersianQA](https://github.com/sajjjadayobi/PersianQA) dataset.
## Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-... | {"language": ["fa", "multilingual"], "tags": ["question-answering", "xlm-roberta", "roberta", "squad"], "datasets": ["SajjadAyoubi/persian_qa"], "metrics": ["squad_v2"], "widget": [{"text": "\u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc \u0644\u0627\u067e\u0644\u0627\u0633\u06cc\u0646\u061f", "context": "\u064... | m3hrdadfi/xlmr-large-qa-fa | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"question-answering",
"roberta",
"squad",
"fa",
"multilingual",
"dataset:SajjadAyoubi/persian_qa",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa",
"multilingual"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #question-answering #roberta #squad #fa #multilingual #dataset-SajjadAyoubi/persian_qa #model-index #endpoints_compatible #region-us
|
# XLM-RoBERTa large for QA (PersianQA - 🇮🇷)
This model is a fine-tuned version of xlm-roberta-large on the PersianQA dataset.
## Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4... | [
"# XLM-RoBERTa large for QA (PersianQA - 🇮🇷) \n\nThis model is a fine-tuned version of xlm-roberta-large on the PersianQA dataset.",
"## Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumu... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #question-answering #roberta #squad #fa #multilingual #dataset-SajjadAyoubi/persian_qa #model-index #endpoints_compatible #region-us \n",
"# XLM-RoBERTa large for QA (PersianQA - 🇮🇷) \n\nThis model is a fine-tuned version of xlm-roberta-large on the PersianQA datas... |
question-answering | transformers |
# XLM-RoBERTa large for QA (SwedishQA - 🇸🇪)
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the [SwedishQA](https://github.com/Vottivott/building-a-swedish-qa-model) dataset.
## Hyperparameters
The following hyperparameters were used during training:
- lea... | {"language": ["sv", "multilingual"], "tags": ["question-answering", "xlm-roberta", "roberta", "squad"], "metrics": ["squad_v2"], "widget": [{"text": "Vilket datum \u00e4r den svenska nationaldagen?", "context": "Sveriges nationaldag och svenska flaggans dag firas den 6 juni varje \u00e5r och \u00e4r en helgdag i Sverig... | m3hrdadfi/xlmr-large-qa-sv | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"question-answering",
"roberta",
"squad",
"sv",
"multilingual",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv",
"multilingual"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #question-answering #roberta #squad #sv #multilingual #model-index #endpoints_compatible #region-us
|
# XLM-RoBERTa large for QA (SwedishQA - 🇸🇪)
This model is a fine-tuned version of xlm-roberta-large on the SwedishQA dataset.
## Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8... | [
"# XLM-RoBERTa large for QA (SwedishQA - 🇸🇪) \n\nThis model is a fine-tuned version of xlm-roberta-large on the SwedishQA dataset.",
"## Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumu... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #question-answering #roberta #squad #sv #multilingual #model-index #endpoints_compatible #region-us \n",
"# XLM-RoBERTa large for QA (SwedishQA - 🇸🇪) \n\nThis model is a fine-tuned version of xlm-roberta-large on the SwedishQA dataset.",
"## Hyperparameters\n\nTh... |
text-classification | transformers |
# Zabanshenas - Language Detector
Zabanshenas is a Transformer-based solution for identifying the most likely language of a written document/text. Zabanshenas is a Persian word that has two meanings:
- A person who studies linguistics.
- A way to identify the type of written language.
## How to use
Follow [Zabans... | {"language": ["multilingual", "ace", "afr", "als", "amh", "ang", "ara", "arg", "arz", "asm", "ast", "ava", "aym", "azb", "aze", "bak", "bar", "bcl", "bel", "ben", "bho", "bjn", "bod", "bos", "bpy", "bre", "bul", "bxr", "cat", "cbk", "cdo", "ceb", "ces", "che", "chr", "chv", "ckb", "cor", "cos", "crh", "csb", "cym", "da... | m3hrdadfi/zabanshenas-roberta-base-mix | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"multilingual",
"ace",
"afr",
"als",
"amh",
"ang",
"ara",
"arg",
"arz",
"asm",
"ast",
"ava",
"aym",
"azb",
"aze",
"bak",
"bar",
"bcl",
"bel",
"ben",
"bho",
"bjn",
"bod",
"bos",
"bpy",
"bre"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"ace",
"afr",
"als",
"amh",
"ang",
"ara",
"arg",
"arz",
"asm",
"ast",
"ava",
"aym",
"azb",
"aze",
"bak",
"bar",
"bcl",
"bel",
"ben",
"bho",
"bjn",
"bod",
"bos",
"bpy",
"bre",
"bul",
"bxr",
"cat",
"cbk",
"cdo",
"ceb",
"ces",
"che",
"... | TAGS
#transformers #pytorch #tf #roberta #text-classification #multilingual #ace #afr #als #amh #ang #ara #arg #arz #asm #ast #ava #aym #azb #aze #bak #bar #bcl #bel #ben #bho #bjn #bod #bos #bpy #bre #bul #bxr #cat #cbk #cdo #ceb #ces #che #chr #chv #ckb #cor #cos #crh #csb #cym #dan #deu #diq #div #dsb #dty #egl #ell... | Zabanshenas - Language Detector
===============================
Zabanshenas is a Transformer-based solution for identifying the most likely language of a written document/text. Zabanshenas is a Persian word that has two meanings:
* A person who studies linguistics.
* A way to identify the type of written language.
... | [
"### By Paragraph",
"### By Sentence",
"### By Token (3 to 5)\n\n\n\nQuestions?\n----------\n\n\nPost a Github issue from HERE."
] | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #multilingual #ace #afr #als #amh #ang #ara #arg #arz #asm #ast #ava #aym #azb #aze #bak #bar #bcl #bel #ben #bho #bjn #bod #bos #bpy #bre #bul #bxr #cat #cbk #cdo #ceb #ces #che #chr #chv #ckb #cor #cos #crh #csb #cym #dan #deu #diq #div #dsb #dty #eg... |
fill-mask | transformers | # MatSciBERT
## A Materials Domain Language Model for Text Mining and Information Extraction
This is the pretrained model presented in [MatSciBERT: A materials domain language model for text mining and information extraction](https://rdcu.be/cMAp5), which is a BERT model trained on material science research papers.
T... | {} | m3rg-iitd/matscibert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # MatSciBERT
## A Materials Domain Language Model for Text Mining and Information Extraction
This is the pretrained model presented in MatSciBERT: A materials domain language model for text mining and information extraction, which is a BERT model trained on material science research papers.
The training corpus compri... | [
"# MatSciBERT",
"## A Materials Domain Language Model for Text Mining and Information Extraction\n\nThis is the pretrained model presented in MatSciBERT: A materials domain language model for text mining and information extraction, which is a BERT model trained on material science research papers.\n\nThe training... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MatSciBERT",
"## A Materials Domain Language Model for Text Mining and Information Extraction\n\nThis is the pretrained model presented in MatSciBERT: A materials domain language model for te... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 251844
## Validation Metrics
- Loss: 0.38616305589675903
- Accuracy: 0.8356545961002786
- Precision: 0.8253968253968254
- Recall: 0.8571428571428571
- AUC: 0.9222387781709815
- F1: 0.8409703504043127
## Usage
You can use cURL to acces... | {"language": "en", "tags": "autonlp", "datasets": ["m3tafl0ps/autonlp-data-NLPIsFun"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | m3tafl0ps/autonlp-NLPIsFun-251844 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:m3tafl0ps/autonlp-data-NLPIsFun",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-m3tafl0ps/autonlp-data-NLPIsFun #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 251844
## Validation Metrics
- Loss: 0.38616305589675903
- Accuracy: 0.8356545961002786
- Precision: 0.8253968253968254
- Recall: 0.8571428571428571
- AUC: 0.9222387781709815
- F1: 0.8409703504043127
## Usage
You can use cURL to acces... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 251844",
"## Validation Metrics\n\n- Loss: 0.38616305589675903\n- Accuracy: 0.8356545961002786\n- Precision: 0.8253968253968254\n- Recall: 0.8571428571428571\n- AUC: 0.9222387781709815\n- F1: 0.8409703504043127",
"## Usage\n\nY... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-m3tafl0ps/autonlp-data-NLPIsFun #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 251844",
"## Validation Metrics\n\n- Loss: 0.38616305589... |
text-generation | transformers |
# al-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["al"], "license": "apache-2.0", "datasets": ["wiki-al"], "thumbnail": "https://huggingface.co/macedonizer/al-roberta-base/lets-talk-about-nlp-al.jpg"} | macedonizer/al-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"al",
"dataset:wiki-al",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"al"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #al #dataset-wiki-al #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# al-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
al-gpt2 is a transformers model pretrained on a very large corpus of Albanian data in ... | [
"# al-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\nal-gpt2 is a transformers model pretrained on a very large corpus of Alba... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #al #dataset-wiki-al #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# al-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (... |
fill-mask | transformers |
# AL-RoBERTa base model
Pretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between tirana and Tirana.
# Model description
RoBERTa is a transformers model pre-... | {"language": ["al"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-sh"], "thumbnail": "https://huggingface.co/macedonizer/al-roberta-base/lets-talk-about-nlp-al.jpg"} | macedonizer/al-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"al",
"dataset:wiki-sh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"al"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #al #dataset-wiki-sh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# AL-RoBERTa base model
Pretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between tirana and Tirana.
# Model description
RoBERTa is a transformers model pre-... | [
"# AL-RoBERTa base model\nPretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between tirana and Tirana.",
"# Model description\nRoBERTa is a transformers ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #al #dataset-wiki-sh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# AL-RoBERTa base model\nPretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and fi... |
fill-mask | transformers |
# BA-RoBERTa base model
Pretrained model on Bosnian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between sarajevo and Sarajevo.
# Model description
RoBERTa is a transformers model p... | {"language": ["ba"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-bs"], "thumbnail": "https://huggingface.co/macedonizer/ba-roberta-base/abdulah-sidran.jpg"} | macedonizer/ba-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"ba",
"dataset:wiki-bs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ba"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #ba #dataset-wiki-bs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BA-RoBERTa base model
Pretrained model on Bosnian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between sarajevo and Sarajevo.
# Model description
RoBERTa is a transformers model p... | [
"# BA-RoBERTa base model\nPretrained model on Bosnian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between sarajevo and Sarajevo.",
"# Model description\nRoBERTa is a transforme... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #ba #dataset-wiki-bs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BA-RoBERTa base model\nPretrained model on Bosnian language using a masked language modeling (MLM) objective. It was introduced in this paper and fir... |
text-generation | transformers |
# blaze-koneski
GPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.
## About Blaze Koneski
Born in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, corresponding... | {"language": ["mk"], "license": "apache-2.0", "datasets": ["wiki-mk", "blaze-koneski-poetry"], "thumbnail": "https://huggingface.co/macedonizer/blaze-koneski/blaze-koneski.jpg"} | macedonizer/blaze-koneski | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"mk",
"dataset:wiki-mk",
"dataset:blaze-koneski-poetry",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #mk #dataset-wiki-mk #dataset-blaze-koneski-poetry #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# blaze-koneski
GPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.
## About Blaze Koneski
Born in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, corresponding... | [
"# blaze-koneski\nGPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.",
"## About Blaze Koneski\nBorn in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, cor... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #mk #dataset-wiki-mk #dataset-blaze-koneski-poetry #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# blaze-koneski\nGPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.",... |
text-generation | transformers |
# gr-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["gr"], "license": "apache-2.0", "datasets": ["wiki-gr"], "thumbnail": "https://huggingface.co/macedonizer/gr-roberta-base/lets-talk-about-nlp-gr.jpg"} | macedonizer/gr-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gr",
"dataset:wiki-gr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gr #dataset-wiki-gr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gr-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
gr-gpt2 is a transformers model pretrained on a very large corpus of Greek data in a s... | [
"# gr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\ngr-gpt2 is a transformers model pretrained on a very large corpus of Gree... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gr #dataset-wiki-gr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (... |
fill-mask | transformers |
# GR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between Athens and athens.
# Model description
RoBERTa is a transformers model pr... | {"language": ["gr"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-gr"], "thumbnail": "https://huggingface.co/macedonizer/gr-roberta-base/lets-talk-about-nlp-gr.jpg"} | macedonizer/gr-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"gr",
"dataset:wiki-gr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #gr #dataset-wiki-gr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# GR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between Athens and athens.
# Model description
RoBERTa is a transformers model pr... | [
"# GR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between Athens and athens.",
"# Model description\nRoBERTa is a transformer... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #gr #dataset-wiki-gr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and ... |
text-generation | transformers |
# hr-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["hr"], "license": "apache-2.0", "datasets": ["wiki-hr"], "thumbnail": "https://huggingface.co/macedonizer/hr-gpt2/lets-talk-about-nlp-hr.jpg"} | macedonizer/hr-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"hr",
"dataset:wiki-hr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #hr #dataset-wiki-hr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# hr-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
hr-gpt2 is a transformers model pretrained on a very large corpus of Croation data in ... | [
"# hr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\nhr-gpt2 is a transformers model pretrained on a very large corpus of Croa... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #hr #dataset-wiki-hr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# hr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (... |
fill-mask | transformers |
# HR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | {"language": ["hr"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-hr"], "thumbnail": "https://huggingface.co/macedonizer/hr-roberta-base/ivo-andric.jpg"} | macedonizer/hr-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"dataset:wiki-hr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #masked-lm #hr #dataset-wiki-hr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# HR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | [
"# HR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.",
"# Model description\nRoBERTa is a transformer... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #masked-lm #hr #dataset-wiki-hr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# HR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper... |
text-generation | transformers |
# mk-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["mk"], "license": "apache-2.0", "datasets": ["wiki-mk", "time-mk-news-2010-2015"], "thumbnail": "https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg"} | macedonizer/mk-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"mk",
"dataset:wiki-mk",
"dataset:time-mk-news-2010-2015",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #mk #dataset-wiki-mk #dataset-time-mk-news-2010-2015 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mk-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data i... | [
"# mk-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\nmk-gpt2 is a transformers model pretrained on a very large corpus of Mace... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #mk #dataset-wiki-mk #dataset-time-mk-news-2010-2015 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mk-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language us... |
fill-mask | transformers |
# MK-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | {"language": ["mk"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-mk", "time-mk-news-2010-2015"], "thumbnail": "https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg"} | macedonizer/mk-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"masked-lm",
"mk",
"dataset:wiki-mk",
"dataset:time-mk-news-2010-2015",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mk"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #masked-lm #mk #dataset-wiki-mk #dataset-time-mk-news-2010-2015 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# MK-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | [
"# MK-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.",
"# Model description\nRoBERTa is a transformer... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #masked-lm #mk #dataset-wiki-mk #dataset-time-mk-news-2010-2015 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# MK-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective.... |
text-generation | transformers |
# sl-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["sl"], "license": "apache-2.0", "datasets": ["wiki-sl"], "thumbnail": "https://huggingface.co/macedonizer/mkgpt2/lets-talk-about-nlp.jpg"} | macedonizer/sl-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sl",
"dataset:wiki-sl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #sl #dataset-wiki-sl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# sl-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
sl-gpt2 is a transformers model pretrained on a very large corpus of Slovenian data in... | [
"# sl-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\nsl-gpt2 is a transformers model pretrained on a very large corpus of Slov... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #sl #dataset-wiki-sl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# sl-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (... |
fill-mask | transformers |
# HR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | {"language": ["sl"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-sl"], "thumbnail": "https://huggingface.co/macedonizer/sl-roberta-base/ivan-cankar.jpg"} | macedonizer/sl-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"masked-lm",
"sl",
"dataset:wiki-sl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #masked-lm #sl #dataset-wiki-sl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# HR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pr... | [
"# HR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.",
"# Model description\nRoBERTa is a transformer... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #masked-lm #sl #dataset-wiki-sl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# HR-RoBERTa base model\nPretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper... |
text-generation | transformers |
# sr-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_m... | {"language": ["sr"], "license": "apache-2.0", "datasets": ["wiki-sr"], "thumbnail": "https://huggingface.co/macedonizer/sr-gpt2/desanka-maksimovic.jpeg"} | macedonizer/sr-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sr",
"dataset:wiki-sr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #sr #dataset-wiki-sr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# sr-gpt2
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
sr-gpt2 is a transformers model pretrained on a very large corpus of Serbian data in a... | [
"# sr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\nsr-gpt2 is a transformers model pretrained on a very large corpus of Serb... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #sr #dataset-wiki-sr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# sr-gpt2\nTest the whole generation capabilities here: URL\nPretrained model on English language using a causal language modeling (... |
fill-mask | transformers |
# SR-RoBERTa base model
Pretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pre-t... | {"language": ["sr"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["wiki-sr"], "thumbnail": "https://huggingface.co/macedonizer/sr-roberta-base/lets-talk-about-nlp-sr.jpg"} | macedonizer/sr-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"masked-lm",
"sr",
"dataset:wiki-sr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #masked-lm #sr #dataset-wiki-sr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# SR-RoBERTa base model
Pretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pre-t... | [
"# SR-RoBERTa base model\nPretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.",
"# Model description\nRoBERTa is a transformers m... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #masked-lm #sr #dataset-wiki-sr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# SR-RoBERTa base model\nPretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper an... |
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... | mackseem/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: 0.0623
* Precision: 0.9245
* Recall: 0.9365
* F1: 0.9304
* Accuracy: 0.9834
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... |
text-generation | transformers | # My Awesome Model | {"tags": ["conversational"]} | madbuda/DialoGPT-got-skippy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | madbuda/DialoGPT-medium-skippy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers |
# Problem Description
The ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However, a common challenge faced in such systems is the presence of gibberish or nonsensical input. To address this problem, we present a project focused on developing a gi... | {"language": "en", "tags": ["autonlp"], "datasets": ["madhurjindal/autonlp-data-Gibberish-Detector"], "widget": [{"text": "I love Machine Learning!"}], "co2_eq_emissions": 5.527544460835904} | madhurjindal/autonlp-Gibberish-Detector-492513457 | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:madhurjindal/autonlp-data-Gibberish-Detector",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #autonlp #en #dataset-madhurjindal/autonlp-data-Gibberish-Detector #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Problem Description
The ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However, a common challenge faced in such systems is the presence of gibberish or nonsensical input. To address this problem, we present a project focused on developing a gi... | [
"# Problem Description\nThe ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However, a common challenge faced in such systems is the presence of gibberish or nonsensical input. To address this problem, we present a project focused on developing... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #autonlp #en #dataset-madhurjindal/autonlp-data-Gibberish-Detector #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Problem Description\nThe ability to process and understand user input is crucia... |
question-answering | transformers | Albert v2 finetuned on SQuAD v1.
Trained using the [nn_pruning](https://github.com/huggingface/nn_pruning/tree/main/examples/question_answering) script, with pruning disabled.
[Original results](https://github.com/google-research/albert) are F1=90.2, EM=83.2, we improved them to:
```{
"exact_match": 83.74645222... | {} | madlag/albert-base-v2-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
| Albert v2 finetuned on SQuAD v1.
Trained using the nn_pruning script, with pruning disabled.
Original results are F1=90.2, EM=83.2, we improved them to:
| [] | [
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model is [block-sparse](https://github.com/huggingface/pytorch_block_sparse).
That means that with the right runtime it can run roughly 3x faster than an dense network, with 25% of the original weights.
This of course has some impact on the accuracy (see below... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is located the Eiffel Tower ?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engi... | madlag/bert-base-uncased-squad-v1-sparse0.25 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"arxiv:2005.07683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model is block-sparse.
That means that with the right runtime it can run roughly 3x faster than an dense network, with 25% of the original weights.
This of course has some impact on the accuracy (see below).
It us... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6700K CPU'\n\n\n'Memory: 64 GiB'\n\n\n'GPUs: 1 GeForce GTX 3090, with 24GiB memory'\n\n\n'GPU driver: 455.23.05, CUDA: 11.1'",
"### Results\n\n\nModel s... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:\n\n\n'CPU: Intel(R) ... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model is block sparse: the **linear** layers contains **7.5%** of the original weights.
The model contains **28.2%** of the original weights **overall**.
The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) me... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engin... | madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"arxiv:2005.07683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model is block sparse: the linear layers contains 7.5% of the original weights.
The model contains 28.2% of the original weights overall.
The training use a modified version of Victor Sanh Movement Pruning method.
... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '335M' (original BERT: '438M')\n\n\nMetric: EM, # Value: 71.88, # Original (Table 2): 80.8\nMetric: F1, # Value: 81.36, # Original (Table 2... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPyt... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model is block sparse: the **linear** layers contains **12.5%** of the original weights.
The model contains **32.1%** of the original weights **overall**.
The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) m... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engin... | madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"arxiv:2005.07683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model is block sparse: the linear layers contains 12.5% of the original weights.
The model contains 32.1% of the original weights overall.
The training use a modified version of Victor Sanh Movement Pruning method.
... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '342M' (original BERT: '438M')\n\n\nMetric: EM, # Value: 74.39, # Original (Table 2): 80.8\nMetric: F1, # Value: 83.26, # Original (Table 2... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPyt... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model is block sparse: the **linear** layers contains **20.2%** of the original weights.
The model contains **38.1%** of the original weights **overall**.
The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) m... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engin... | madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"arxiv:2005.07683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model is block sparse: the linear layers contains 20.2% of the original weights.
The model contains 38.1% of the original weights overall.
The training use a modified version of Victor Sanh Movement Pruning method.
... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '347M' (original BERT: '438M')\n\n\nMetric: EM, # Value: 76.98, # Original (Table 2): 80.8\nMetric: F1, # Value: 85.45, # Original (Table 2... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPyt... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model is block sparse: the **linear** layers contains **31.7%** of the original weights.
The model contains **47.0%** of the original weights **overall**.
The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) m... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engin... | madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"arxiv:2005.07683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model is block sparse: the linear layers contains 31.7% of the original weights.
The model contains 47.0% of the original weights overall.
The training use a modified version of Victor Sanh Movement Pruning method.
... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '355M' (original BERT: '438M')\n\n\nMetric: EM, # Value: 79.04, # Original (Table 2): 80.8\nMetric: F1, # Value: 86.70, # Original (Table 2... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPyt... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original weights.
The model contains **28.0%** of the original weights **overall** (the embeddings account for a sig... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 8.0% of the original weights.
The model contains 28.0% of the original weights overall (the embeddings account for a significant part ... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '398MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '398MB'... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 36.0%** of the original weights.
The model contains **50.0%** of the original weights **overall** (the embeddings account for a si... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 36.0% of the original weights.
The model contains 50.0% of the original weights overall (the embeddings account for a significant part... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '379MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '379MB'... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 27.0%** of the original weights.
This model **CANNOT be used without using nn_pruning `optimize_model`** function, as it uses NoNorm... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 27.0% of the original weights.
This model CANNOT be used without using nn\_pruning 'optimize\_model' function, as it uses NoNorms inst... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '374MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '374MB'... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 30.0%** of the original weights.
This model **CANNOT be used without using nn_pruning `optimize_model`** function, as it uses NoNorm... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 30.0% of the original weights.
This model CANNOT be used without using nn\_pruning 'optimize\_model' function, as it uses NoNorms inst... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '374MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '374MB'... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 15.0%** of the original weights.
The model contains **34.0%** of the original weights **overall** (the embeddings account for a si... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 15.0% of the original weights.
The model contains 34.0% of the original weights overall (the embeddings account for a significant part... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '368MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '368MB'... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 26.0%** of the original weights.
The model contains **42.0%** of the original weights **overall** (the embeddings account for a si... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, w... | madlag/bert-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 26.0% of the original weights.
The model contains 42.0% of the original weights overall (the embeddings account for a significant part... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '355MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '355MB'... |
text-classification | transformers | ## BERT-large finetuned on MNLI.
The [reference finetuned model](https://github.com/google-research/bert) has an accuracy of 86.05, we get 86.7:
```
{'eval_loss': 0.3984006643295288, 'eval_accuracy': 0.8667345899133979}
``` | {} | madlag/bert-large-uncased-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## BERT-large finetuned on MNLI.
The reference finetuned model has an accuracy of 86.05, we get 86.7:
| [
"## BERT-large finetuned on MNLI.\n\nThe reference finetuned model has an accuracy of 86.05, we get 86.7:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## BERT-large finetuned on MNLI.\n\nThe reference finetuned model has an accuracy of 86.05, we get 86.7:"
] |
question-answering | transformers | ## BERT-large finetuned on squad v2.
F1 on dev (from paper)[https://arxiv.org/pdf/1810.04805v2.pdf] is 81.9, we reach 81.58.
```
{'exact': 78.6321906847469,
'f1': 81.5816656803201,
'total': 11873,
'HasAns_exact': 73.73481781376518,
'HasAns_f1': 79.64222615088413,
'HasAns_total': 5928,
'NoAns_exact': 83.51555929352... | {} | madlag/bert-large-uncased-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"arxiv:1810.04805",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us
| ## BERT-large finetuned on squad v2.
F1 on dev (from paper)[URL is 81.9, we reach 81.58.
| [
"## BERT-large finetuned on squad v2.\n\nF1 on dev (from paper)[URL is 81.9, we reach 81.58."
] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us \n",
"## BERT-large finetuned on squad v2.\n\nF1 on dev (from paper)[URL is 81.9, we reach 81.58."
] |
question-answering | transformers | Used [run.sh](https://huggingface.co/madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2/blob/main/run.sh) used to train using transformers/example/question_answering code.
Evaluation results : F1= 85.85 , a much better result than the original 81.9 from the BERT paper, due to the use of the "whole-word-mas... | {} | madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us
| Used URL used to train using transformers/example/question_answering code.
Evaluation results : F1= 85.85 , a much better result than the original 81.9 from the BERT paper, due to the use of the "whole-word-masking" variation.
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
## bert-large-uncased-whole-word-masking model fine-tuned on SQuAD v2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 25.0%** of the original weights.
The model contains **32.0%** of the original weights **overall** (the embeddi... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad_v2"], "metrics": ["squad_v2"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eif... | madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| bert-large-uncased-whole-word-masking model fine-tuned on SQuAD v2
------------------------------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 25.0% of the original weights.
The model contains 32.0% of the original weights overall (the e... | [
"# samples: 130.0K\nDataset: SQuAD 2.0, Split: eval, # samples: 11.9k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '1119MB' (original BERT: '1228.0MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us \n",
"# samples: 130.0K\nDataset: SQuAD 2.0, Split: eval, # samples: 11.9k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '1... |
question-answering | transformers |
## bert-large-uncased-whole-word-masking model fine-tuned on SQuAD v2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 16.0%** of the original weights.
The model contains **24.0%** of the original weights **overall** (the embeddi... | {"language": "en", "license": "mit", "tags": ["question-answering"], "datasets": ["squad_v2"], "metrics": ["squad_v2"], "widget": [{"text": "Where is the Eiffel Tower located?", "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eif... | madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us
| bert-large-uncased-whole-word-masking model fine-tuned on SQuAD v2
------------------------------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 16.0% of the original weights.
The model contains 24.0% of the original weights overall (the e... | [
"# samples: 130.0K\nDataset: SQuAD 2.0, Split: eval, # samples: 11.9k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model file size: '1084MB' (original BERT: '1228.0MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, ... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #en #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us \n",
"# samples: 130.0K\nDataset: SQuAD 2.0, Split: eval, # samples: 11.9k",
"### Fine-tuning\n\n\n* Python: '3.8.5'\n* Machine specs:",
"### Results\n\n\nPytorch model fi... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | mahaamami/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4385
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | mahaamami/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"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 #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8833
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #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\\_size: ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-model-transcript
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilrobe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-model-transcript", "results": []}]} | mahaamami/distilroberta-base-model-transcript | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"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 #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-model-transcript
===================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8922
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #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\\_size: ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-model", "results": []}]} | mahaamami/distilroberta-base-model | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"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 #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-model
========================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7929
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #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\\_size: ... |
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. -->
# English_ASR
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "English_ASR", "results": []}]} | maher13/English_ASR | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| English\_ASR
============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4971
* Wer: 0.3397
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# arabic-iti
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-larg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "arabic-iti", "results": []}]} | maher13/arabic-iti | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"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 #has_space #region-us
| arabic-iti
==========
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0154
* Wer: 0.6350
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 0.0005\n* tra... |
text-generation | transformers |
# DialoGPT Joe Bot | {"tags": ["conversational"]} | majonez57/JoeBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Joe Bot | [
"# DialoGPT Joe Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Joe Bot"
] |
feature-extraction | transformers |
# t5-base-standard-bahasa-cased
Pretrained T5 base standard language model for Malay.
## Pretraining Corpus
`t5-base-standard-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliamen... | {"language": "ms"} | mesolitica/t5-base-standard-bahasa-cased | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"ms",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-standard-bahasa-cased
Pretrained T5 base standard language model for Malay.
## Pretraining Corpus
't5-base-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa parliament and tra... | [
"# t5-base-standard-bahasa-cased\n\nPretrained T5 base standard language model for Malay.",
"## Pretraining Corpus\n\n't5-base-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,\n\n1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa parli... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-standard-bahasa-cased\n\nPretrained T5 base standard language model for Malay.",
"## Pretraining Corpus\n\n't5-base-standard-bahasa-cased' model was pretrained on multiple tasks.... |
feature-extraction | transformers |
# t5-small-standard-bahasa-cased
Pretrained T5 small standard language model for Malay.
## Pretraining Corpus
`t5-small-standard-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parlia... | {"language": "ms"} | mesolitica/t5-small-standard-bahasa-cased | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"ms",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-standard-bahasa-cased
Pretrained T5 small standard language model for Malay.
## Pretraining Corpus
't5-small-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa parliament and ... | [
"# t5-small-standard-bahasa-cased\n\nPretrained T5 small standard language model for Malay.",
"## Pretraining Corpus\n\n't5-small-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,\n\n1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa pa... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-standard-bahasa-cased\n\nPretrained T5 small standard language model for Malay.",
"## Pretraining Corpus\n\n't5-small-standard-bahasa-cased' model was pretrained on multiple tas... |
feature-extraction | transformers |
# t5-super-super-tiny-standard-bahasa-cased
Pretrained T5 super-super-tiny standard language model for Malay.
## Pretraining Corpus
`t5-super-super-tiny-standard-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, b... | {"language": "ms"} | mesolitica/t5-super-super-tiny-standard-bahasa-cased | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"ms",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us
|
# t5-super-super-tiny-standard-bahasa-cased
Pretrained T5 super-super-tiny standard language model for Malay.
## Pretraining Corpus
't5-super-super-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, b... | [
"# t5-super-super-tiny-standard-bahasa-cased\n\nPretrained T5 super-super-tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-super-super-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,\n\n1. Language masking task on bahasa news, bahasa... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-super-super-tiny-standard-bahasa-cased\n\nPretrained T5 super-super-tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-super-super-tiny-standard-bahasa-cased' mod... |
feature-extraction | transformers |
# t5-super-tiny-standard-bahasa-cased
Pretrained T5 super-tiny standard language model for Malay.
## Pretraining Corpus
`t5-super-tiny-standard-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu... | {"language": "ms"} | mesolitica/t5-super-tiny-standard-bahasa-cased | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"ms",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us
|
# t5-super-tiny-standard-bahasa-cased
Pretrained T5 super-tiny standard language model for Malay.
## Pretraining Corpus
't5-super-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa ... | [
"# t5-super-tiny-standard-bahasa-cased\n\nPretrained T5 super-tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-super-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,\n\n1. Language masking task on bahasa news, bahasa Wikipedia, bahasa... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-super-tiny-standard-bahasa-cased\n\nPretrained T5 super-tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-super-tiny-standard-bahasa-cased' model was pretrained ... |
feature-extraction | transformers |
# t5-tiny-standard-bahasa-cased
Pretrained T5 tiny standard language model for Malay.
## Pretraining Corpus
`t5-tiny-standard-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliamen... | {"language": "ms"} | mesolitica/t5-tiny-standard-bahasa-cased | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"ms",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-standard-bahasa-cased
Pretrained T5 tiny standard language model for Malay.
## Pretraining Corpus
't5-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa parliament and tra... | [
"# t5-tiny-standard-bahasa-cased\n\nPretrained T5 tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-tiny-standard-bahasa-cased' model was pretrained on multiple tasks. Below is list of tasks we trained on,\n\n1. Language masking task on bahasa news, bahasa Wikipedia, bahasa URL, bahasa parli... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #ms #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-standard-bahasa-cased\n\nPretrained T5 tiny standard language model for Malay.",
"## Pretraining Corpus\n\n't5-tiny-standard-bahasa-cased' model was pretrained on multiple tasks.... |
feature-extraction | transformers |
# xlnet-large-bahasa-cased
Pretrained XLNET large language model for Malay.
## Pretraining Corpus
`xlnet-large-bahasa-cased` model was pretrained on ~1.4 Billion words. Below is list of data we trained on,
1. [cleaned local texts](https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean).
2. [transla... | {"language": "ms"} | malaysia-ai/xlnet-large-bahasa-cased | null | [
"transformers",
"pytorch",
"xlnet",
"feature-extraction",
"ms",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ms"
] | TAGS
#transformers #pytorch #xlnet #feature-extraction #ms #endpoints_compatible #region-us
|
# xlnet-large-bahasa-cased
Pretrained XLNET large language model for Malay.
## Pretraining Corpus
'xlnet-large-bahasa-cased' model was pretrained on ~1.4 Billion words. Below is list of data we trained on,
1. cleaned local texts.
2. translated The Pile.
## Pretraining details
- All steps can reproduce from here,... | [
"# xlnet-large-bahasa-cased\n\nPretrained XLNET large language model for Malay.",
"## Pretraining Corpus\n\n'xlnet-large-bahasa-cased' model was pretrained on ~1.4 Billion words. Below is list of data we trained on,\n\n1. cleaned local texts.\n2. translated The Pile.",
"## Pretraining details\n\n- All steps can... | [
"TAGS\n#transformers #pytorch #xlnet #feature-extraction #ms #endpoints_compatible #region-us \n",
"# xlnet-large-bahasa-cased\n\nPretrained XLNET large language model for Malay.",
"## Pretraining Corpus\n\n'xlnet-large-bahasa-cased' model was pretrained on ~1.4 Billion words. Below is list of data we trained o... |
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"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | malduwais/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0601
* Precision: 0.9229
* Recall: 0.9352
* F1: 0.9290
* Accuracy: 0.9831
Model descri... | [
"### 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 #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
translation | null | # OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for English to G... | {"language": ["de", "en"], "license": "mit", "tags": ["translation", "pytorch"], "datasets": ["WMT"], "metrics": ["bleu"]} | malloc/OpenNMT-py-English-German-Transformer | null | [
"translation",
"pytorch",
"de",
"en",
"dataset:WMT",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"en"
] | TAGS
#translation #pytorch #de #en #dataset-WMT #license-mit #has_space #region-us
| # OpenNMT-py-English-German-Transformer
OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several pretrained models. This one is trained particularly for English to German translation.
- Configuration: Base Transformer configuration with st... | [
"# OpenNMT-py-English-German-Transformer\nOpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.\nOpenNMT has several pretrained models. This one is trained particularly for English to German translation.\n\n- Configuration: Base Transformer configuratio... | [
"TAGS\n#translation #pytorch #de #en #dataset-WMT #license-mit #has_space #region-us \n",
"# OpenNMT-py-English-German-Transformer\nOpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.\nOpenNMT has several pretrained models. This one is trained parti... |
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