readme: ---
  language: 
  - as
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  license: mit
  datasets:
  - AI4Bharat IndicNLP Corpora
  ---
  
  # IndicBERT
  
  IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R etc.) while it also achieves a performance on-par or better than these models.
  
  The 12 languages covered by IndicBERT are: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
  
  The code can be found [here](https://github.com/divkakwani/indic-bert). For more information, checkout our [project page](https://indicnlp.ai4bharat.org/) or our [paper](https://indicnlp.ai4bharat.org/papers/arxiv2020_indicnlp_corpus.pdf).
  
  
  
  ## Pretraining Corpus
  
  We pre-trained indic-bert on AI4Bharat's monolingual corpus. The corpus has the following distribution of languages:
  
  
  | Language          | as     | bn     | en     | gu     | hi     | kn     |         |
  | ----------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------- |
  | **No. of Tokens** | 36.9M  | 815M   | 1.34B  | 724M   | 1.84B  | 712M   |         |
  | **Language**      | **ml** | **mr** | **or** | **pa** | **ta** | **te** | **all** |
  | **No. of Tokens** | 767M   | 560M   | 104M   | 814M   | 549M   | 671M   | 8.9B    |
  
  
  
  ## Evaluation Results
  
  IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our [official repo](https://github.com/divkakwani/indic-bert)
  
  #### IndicGLUE
  
  Task | mBERT | XLM-R | IndicBERT
  -----| ----- | ----- | ------ 
  News Article Headline Prediction | 89.58 | 95.52 | **95.87** 
  Wikipedia Section Title Prediction| **73.66** | 66.33 | 73.31 
  Cloze-style multiple-choice QA | 39.16 | 27.98 | **41.87** 
  Article Genre Classification | 90.63 | 97.03 | **97.34** 
  Named Entity Recognition (F1-score) | **73.24** | 65.93 | 64.47 
  Cross-Lingual Sentence Retrieval Task | 21.46 | 13.74 | **27.12** 
  Average | 64.62 | 61.09 | **66.66** 
  
  #### Additional Tasks
  
  
  Task | Task Type | mBERT | XLM-R | IndicBERT 
  -----| ----- | ----- | ------ | ----- 
  BBC News Classification | Genre Classification | 60.55 | **75.52** | 74.60 
  IIT Product Reviews | Sentiment Analysis | 74.57 | **78.97** | 71.32 
  IITP Movie Reviews | Sentiment Analaysis | 56.77 | **61.61** | 59.03 
  Soham News Article | Genre Classification | 80.23 | **87.6** | 78.45 
  Midas Discourse | Discourse Analysis | 71.20 | **79.94** | 78.44 
  iNLTK Headlines Classification | Genre Classification | 87.95 | 93.38 | **94.52** 
  ACTSA Sentiment Analysis | Sentiment Analysis | 48.53 | 59.33 | **61.18** 
  Winograd NLI | Natural Language Inference | 56.34 | 55.87 | **56.34** 
  Choice of Plausible Alternative (COPA) | Natural Language Inference | 54.92 | 51.13 | **58.33** 
  Amrita Exact Paraphrase | Paraphrase Detection | **93.81** | 93.02 | 93.75 
  Amrita Rough Paraphrase | Paraphrase Detection | 83.38 | 82.20 | **84.33** 
  Average |  |  69.84 | **74.42** | 73.66 
  
  
  \* Note: all models have been restricted to a max_seq_length of 128.
  
  
  
  ## Downloads
  
  The model can be downloaded [here](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/models/indic-bert-v1.tar.gz). Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from [Huggingface](https://huggingface.co/ai4bharat/indic-bert).
  
  
  
  ## Citing
  
  If you are using any of the resources, please cite the following article:
  
  ```
  @inproceedings{kakwani2020indicnlpsuite,
      title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
      author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
      year={2020},
      booktitle={Findings of EMNLP},
  }
  ```
  
  We would like to hear from you if:
  
  - You are using our resources. Please let us know how you are putting these resources to use.
  - You have any feedback on these resources.
  
  
  
  ## License
  
  The IndicBERT code (and models) are released under the MIT License.
  
  ## Contributors
  
  - Divyanshu Kakwani
  - Anoop Kunchukuttan
  - Gokul NC
  - Satish Golla
  - Avik Bhattacharyya
  - Mitesh Khapra
  - Pratyush Kumar
  
  This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org).
  
  
  
  ## Contact
  
  - Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com))
  - Mitesh Khapra ([miteshk@cse.iitm.ac.in](mailto:miteshk@cse.iitm.ac.in))
  - Pratyush Kumar ([pratyush@cse.iitm.ac.in](mailto:pratyush@cse.iitm.ac.in))
  