pytorch bert token-classification as bn gu hi kn ml mr or pa ta te dataset:Samanantar transformers ner Pytorch transformer multilingual nlp indicnlp license:mit autotrain_compatible
---
language:
- as 
- bn
- gu 
- hi 
- kn 
- ml 
- mr 
- or 
- pa 
- ta 
- te 
license: mit
datasets:
- Samanantar
tags:
- ner
- Pytorch
- transformer
- multilingual
- nlp
- indicnlp
---

# IndicNER
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 IndicBERT are: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
The link to our GitHub repository containing all our code can be found [here](https://github.com/AI4Bharat/indicner). The link to our paper can be found here.

## Training Corpus
Our model was trained on a [dataset](https://huggingface.co/datasets/ai4bharat/IndicNER) 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.

## Evaluation Results
Benchmarking on our testset.

Language | bn | hi | kn | ml | mr | gu | ta | te | as | or | pa
-----| ----- | ----- | ------ | -----| ----- | ----- | ------ | -----| ----- | ----- | ------ 
F1 score | 79.75 | 82.33 | 80.01 | 80.73 | 80.51 | 73.82 | 80.98 | 80.88 | 62.50 | 27.05 | 74.88

The first 5 languages (bn, hi, kn, ml, mr ) have large human annotated testsets consisting of around 500-1000 sentences. The next 3 (gu, ta, te) have smaller human annotated testsets with only around 50 sentences. The final 3 (as, or, pa) languages have mined projected testsets not supervised by humans.


## Downloads
Download from this same Huggingface repo.





<!-- citing information -->
## Citing

If you are using IndicNER, please cite the following article:
```
@misc{mhaske2022indicner,
      title={Naamapadam: A Large-Scale Named Entity Annotated Data for Indic
Languages}, 
      author={Arnav Mhaske, Harshit Kedia, Rudramurthy. V, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Khapra},
      year={2022},
      eprint={to be published soon},
    }    
```
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 -->
## License

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



<!-- Contributors -->
## Contributors
 - Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
 - Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
 - Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/)) </sub>
 - Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
 - Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
 - Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>

This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org).


<!-- Contact -->
## 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))