---
language:
- as 
- bn
- gu 
- hi 
- kn 
- ml 
- mr 
- or 
- pa 
- ta 
- te 
license: mit
datasets:
- Samanantar
tags:
- ner
- Pytorch
- transformer
- multilingual
- nlp
- indicnlp
---

# fine-tuned IndicNER
fine-tuned IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publicly available Indian NER datasets.
The 11 languages covered by IndicNER are: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.

## Training Corpus
Our model was trained on a [dataset](https://huggingface.co/datasets/ai4bharat/naamapadam) which we mined from the existing [Samanantar Corpus](https://huggingface.co/datasets/ai4bharat/samanantar). We used a bert-base-multilingual-uncased model as the starting point and then fine-tuned it to the NER dataset mentioned previously.

## Downloads
Download from this same Huggingface repo.

Update 20 Dec 2022: We released a new paper documenting IndicNER and Naamapadam. We have a different model reported in the paper. We will update the repo here soon with this model.

## Usage

You can use [this Colab notebook](https://colab.research.google.com/drive/1sYa-PDdZQ_c9SzUgnhyb3Fl7j96QBCS8?usp=sharing) for samples on using IndicNER or for finetuning a pre-trained model on Naampadam dataset to build your own NER models.



<!-- License -->
## License

The fine-tuned-IndicNER code (and models) are released under the MIT License.