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---
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](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta).

> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [HuggingFace](https://huggingface.co/) 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:
```python
>>> 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](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Wikipedia Articles - 172k](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-172k) is a dataset with cleaned 172k Wikipedia articles.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/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](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) 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 | **76.44**     | 66.86      | **77.6**                          | 64.9                  | 73.67         |
| WikiNER                 | Token Classification | -         | 90.68      | **95.09**                         | 89.61                 | **92.76**         |
| IITP Product Reviews    | Sentiment Analysis   | **78.01**     | 73.23      | **78.39**                         | 66.16                 | 75.53         |
| IITP Movie Reviews      | Sentiment Analysis   | 60.97     | 52.26      | **70.65**                         | 49.35                 | **61.29**         |

## Team Members
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/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](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.

<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium>