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README.md

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 # 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.
+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.
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 ## Downloads
 Download from this same Huggingface repo.
 
+## Usage
 
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+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.
 
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