| # 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>. | |