Add model card for ai4bharat/indic-bert

model_cards/ai4bharat/indic-bert/README.md

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+---
+language: en
+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))