IndicBERT is a multilingual ALBERT model trained on large-scale corpora, covering 12 major Indian languages: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. IndicBERT has much less parameters than other public models like mBERT and XLM-R while it still manages to give state of the art performance on several tasks.

Download Model

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.


The easiest way to use Indic BERT is through the Huggingface transformers library. It can be simply loaded like this:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('ai4bharat/indic-bert')
model = AutoModel.from_pretrained('ai4bharat/indic-bert')


If you want to quickly try experimenting with IndicBERT, we suggest checking out our tutorials and other fine-tuning notebooks that run on Google Colab:

  • General Finetuning Open In Colab

Pretraining Details

IndicBERT is pre-trained with IndicNLP corpus which covers 12 Indian languages (including English) The amount of pretraining data for each language is listed below:

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

In total, the pretraining corpus has a size of 120GB and contains 8.9B tokens.


We evaluate IndicBERT model on a set of tasks as described in the IndicGLUE page. Here are the results that we obtain:


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.


  • Kakwani, D., Kunchukuttan, A., Golla, S., N.C. G., Bhattacharyya, A., Khapra, M.M. and Kumar, P., 2020. IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages. Accepted by Findings of EMNLP 2020 pdf