
# Bangla-Electra

This is a second attempt at a Bangla/Bengali language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).

**As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script**: https://huggingface.co/google/muril-base-cased and https://huggingface.co/google/muril-large-cased

**For causal language models, I would suggest https://huggingface.co/sberbank-ai/mGPT, though this is a large model**

Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1gpwHvXAnNQaqcu-YNx1kafEVxz07g2jL

V1 - 120,000 steps; V2 - 190,000 steps

## Classification

Classification with SimpleTransformers: https://colab.research.google.com/drive/1vltPI81atzRvlALv4eCvEB0KdFoEaCOb

On Soham Chatterjee's [news classification task](https://github.com/soham96/Bangla2Vec):
(Random: 16.7%, mBERT: 72.3%, Bangla-Electra: 82.3%)

Similar to mBERT on some tasks and configurations described in https://arxiv.org/abs/2004.07807

## Question Answering

This model can be used for Question Answering - this notebook uses Bangla questions from Google's TyDi dataset:
https://colab.research.google.com/drive/1i6fidh2tItf_-IDkljMuaIGmEU6HT2Ar

## Corpus

Trained on a web crawl from https://oscar-corpus.com/ (deduped version, 5.8GB) and 1 July 2020 dump of bn.wikipedia.org (414MB)

## Vocabulary

Included as vocab.txt in the upload - vocab_size is 29898

{"language": "bn"}
transformers pytorch tf electra bn arxiv:2004.07807 doi:10.57967/hf/1380 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #electra #bn #arxiv-2004.07807 #doi-10.57967/hf/1380 #endpoints_compatible #region-us 


# Bangla-Electra

This is a second attempt at a Bangla/Bengali language model trained with
Google Research's ELECTRA.

As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL

For causal language models, I would suggest URL though this is a large model

Tokenization and pre-training CoLab: URL

V1 - 120,000 steps; V2 - 190,000 steps

## Classification

Classification with SimpleTransformers: URL

On Soham Chatterjee's news classification task:
(Random: 16.7%, mBERT: 72.3%, Bangla-Electra: 82.3%)

Similar to mBERT on some tasks and configurations described in URL

## Question Answering

This model can be used for Question Answering - this notebook uses Bangla questions from Google's TyDi dataset:
URL

## Corpus

Trained on a web crawl from URL (deduped version, 5.8GB) and 1 July 2020 dump of URL (414MB)

## Vocabulary

Included as URL in the upload - vocab_size is 29898

# Bangla-Electra

This is a second attempt at a Bangla/Bengali language model trained with
Google Research's ELECTRA.

As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL

For causal language models, I would suggest URL though this is a large model

Tokenization and pre-training CoLab: URL

V1 - 120,000 steps; V2 - 190,000 steps ## Classification

Classification with SimpleTransformers: URL

On Soham Chatterjee's news classification task:
(Random: 16.7%, mBERT: 72.3%, Bangla-Electra: 82.3%)

Similar to mBERT on some tasks and configurations described in URL ## Question Answering

This model can be used for Question Answering - this notebook uses Bangla questions from Google's TyDi dataset:
URL ## Corpus

Trained on a web crawl from URL (deduped version, 5.8GB) and 1 July 2020 dump of URL (414MB) ## Vocabulary

Included as URL in the upload - vocab_size is 29898
TAGS
#transformers #pytorch #tf #electra #bn #arxiv-2004.07807 #doi-10.57967/hf/1380 #endpoints_compatible #region-us 
 # Bangla-Electra

This is a second attempt at a Bangla/Bengali language model trained with
Google Research's ELECTRA.

As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL

For causal language models, I would suggest URL though this is a large model

Tokenization and pre-training CoLab: URL

V1 - 120,000 steps; V2 - 190,000 steps ## Classification

Classification with SimpleTransformers: URL

On Soham Chatterjee's news classification task:
(Random: 16.7%, mBERT: 72.3%, Bangla-Electra: 82.3%)

Similar to mBERT on some tasks and configurations described in URL ## Question Answering

This model can be used for Question Answering - this notebook uses Bangla questions from Google's TyDi dataset:
URL ## Corpus

Trained on a web crawl from URL (deduped version, 5.8GB) and 1 July 2020 dump of URL (414MB) ## Vocabulary

Included as URL in the upload - vocab_size is 29898
passage: TAGS
#transformers #pytorch #tf #electra #bn #arxiv-2004.07807 #doi-10.57967/hf/1380 #endpoints_compatible #region-us 
# Bangla-Electra

This is a second attempt at a Bangla/Bengali language model trained with
Google Research's ELECTRA.

As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL

For causal language models, I would suggest URL though this is a large model

Tokenization and pre-training CoLab: URL

V1 - 120,000 steps; V2 - 190,000 steps## Classification

Classification with SimpleTransformers: URL

On Soham Chatterjee's news classification task:
(Random: 16.7%, mBERT: 72.3%, Bangla-Electra: 82.3%)

Similar to mBERT on some tasks and configurations described in URL## Question Answering

This model can be used for Question Answering - this notebook uses Bangla questions from Google's TyDi dataset:
URL## Corpus

Trained on a web crawl from URL (deduped version, 5.8GB) and 1 July 2020 dump of URL (414MB)## Vocabulary

Included as URL in the upload - vocab_size is 29898