IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBART model to build natural language generation applications for Indian languages by finetuning the model with supervised training data for tasks like machine translation, summarization, question generation, etc. Some salient features of the IndicBART are: Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, Telugu and English. Not all of these languages are supported by mBART50 and mT5. The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding. Trained on large Indic language corpora (452 million sentences and 9 billion tokens) which also includes Indian English content. All languages, except English, have been represented in Devanagari script to encourage transfer learning among the related languages. For detailed documentation, look here: https://github.com/AI4Bharat/indic-bart/ and https://indicnlp.ai4bharat.org/indic-bart/

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IndicBART-XLSum

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IndicBARTSS

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IndicBERTv2-MLM-only

ai4bharat

A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows: - IndicBERT-MLM [Model] - A vanilla BERT style model trained on IndicCorp v2 with the MLM objective - +Samanantar [Model] - TLM as an additional objective with Samanantar Parallel Corpus [Paper] | [Dataset] - +Back-Translation [Model] - TLM as an additional objective by translating the Indic parts of IndicCorp v2 dataset into English w/ IndicTrans model [Model] - IndicBERT-SS [Model] - To encourage better lexical sharing among languages we convert the scripts from Indic languages to Devanagari and train a BERT style model with the MLM objective Fine-tuning scripts are based on transformers library. Create a new conda environment and set it up as follows:

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