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vaishnavi/indic-bert-512
indic-bert-512 is a machine learning model from vaishnavi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
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…
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From the Hugging Face model README
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. For more information, checkout our project page or our paper.
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 |
IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our official repo
| 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 |
| 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.
The model can be downloaded here. Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from Huggingface.
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:
The IndicBERT code (and models) are released under the MIT License.
This work is the outcome of a volunteer effort as part of AI4Bharat initiative.