Create README.md

README.md

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+# Named Entity Recognition using Transformers
+This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance.
+
+# Dataset
+**CoNLL-2003** :
+The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br>
+**Link** : https://huggingface.co/datasets/conll2003
+
+# Using this fine-tuned version
+
+From python, download the whole pipeline and use it instantly using the following code :
+```
+from transformers import pipeline
+
+# Loading the pipeline from hub
+# Pipeline handles the preprocessing and post processing steps
+model_checkpoint = "balamurugan1603/bert-finetuned-ner"
+namedEntityRecogniser = pipeline(
+    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
+)
+```
+
+Reference for using this pipeline to find NER tags can be found in this <a href="https://github.com/balamurugan1603/Named-Entity-Recognition-using-Tranformers/blob/main/named-entity-recognition-using-transfer-learning.ipynb">notebook</a>.