metadata

widget:
  - text: मुझे उनसे बात करना <mask> अच्छा लगा
  - text: हम आपके सुखद <mask> की कामना करते हैं
  - text: सभी अच्छी चीजों का एक <mask> होता है

RoBERTa base model for Hindi language

Pretrained model on Marathi language using a masked language modeling (MLM) objective. RoBERTa was introduced in this paper and first released in this repository.

This is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.

Model description

RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data in a self-supervised fashion.

How to use

You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("मुझे उनसे बात करना <mask> अच्छा लगा")

[{'score': 0.2096337080001831,
  'sequence': 'मुझे उनसे बात करना एकदम अच्छा लगा',
  'token': 1462,
  'token_str': ' एकदम'},
 {'score': 0.17915162444114685,
  'sequence': 'मुझे उनसे बात करना तब अच्छा लगा',
  'token': 594,
  'token_str': ' तब'},
 {'score': 0.15887945890426636,
  'sequence': 'मुझे उनसे बात करना और अच्छा लगा',
  'token': 324,
  'token_str': ' और'},
 {'score': 0.12024253606796265,
  'sequence': 'मुझे उनसे बात करना लगभग अच्छा लगा',
  'token': 743,
  'token_str': ' लगभग'},
 {'score': 0.07114479690790176,
  'sequence': 'मुझे उनसे बात करना कब अच्छा लगा',
  'token': 672,
  'token_str': ' कब'}]

Training data

The RoBERTa model was pretrained on the reunion of the following datasets:

OSCAR is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
IndicGLUE is a natural language understanding benchmark.
Samanantar is a parallel corpora collection for Indic language.
Hindi Wikipedia Articles - 172k is a dataset with cleaned 172k Wikipedia articles.
Hindi Text Short and Large Summarization Corpus is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
Hindi Text Short Summarization Corpus is a collection of ~330k articles with their headlines collected from Hindi News Websites.
Old Newspapers Hindi is a cleaned subset of HC Corpora newspapers.

Training procedure 👨🏻‍💻

Preprocessing

The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked with <s> and the end of one by </s> The details of the masking procedure for each sentence are the following:

15% of the tokens are masked.
In 80% of the cases, the masked tokens are replaced by <mask>.
In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
In the 10% remaining cases, the masked tokens are left as is. Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).

Pretraining

The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) 8 v3 TPU cores for 42K steps with a batch size of 128 and a sequence length of 128. The optimizer used is Adam with a learning rate of 6e-4, $\beta_{1} = 0.9$ , $\beta_{2} = 0.98$ and $\epsilon = 1e-6$ , a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning rate after.

Evaluation Results

RoBERTa Hindi is evaluated on downstream tasks. The results are summarized below.

Task	Task Type	IndicBERT	HindiBERTa	Indic Transformers Hindi BERT	RoBERTa Hindi Guj San	RoBERTa Hindi
BBC News Classification	Genre Classification
WikiNER	Token Classification
IITP Product Reviews	Sentiment Analysis
IITP Movie Reviews	Sentiment Analysis

Team Members

Kartik Godawat (dk-crazydiv)
Aman K (amankhandelia)
Haswanth Aekula (hassiahk)
Rahul Dev (mlkorra)
Prateek Agrawal (prateekagrawal)

Credits

Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week. Especially for providing such massive computing resource. Big thanks to Suraj Patil & Patrick von Platen for mentoring during the whole week.