2- Fine-Tuning DistilBERT for NER with HuggingFace

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In this tutorial, we will learn how to fine-tune DistilBERT for named entity recognition (NER) tagging. NER tagging is the task of identifying named entities in a text, such as people, organizations, and locations. DistilBERT is a small, fast, and efficient Transformer model that can be fine-tuned for a variety of NLP tasks, including NER tagging.

The tutorial will cover the following steps:
- Preparing the data for training
- Loading the DistilBERT model
- Fine-tuning the DistilBERT model
- Evaluating the fine-tuned model

The tutorial will use the CoNLLPP dataset for NER tagging. This dataset contains a set of sentences with their corresponding named entities. The tutorial will also use the HuggingFace Transformers library to fine-tune the DistilBERT model.

This tutorial is for beginners who want to learn how to fine-tune DistilBERT for NER tagging. No prior knowledge of NLP or Transformers is required.

Here are some of the things you will learn in this tutorial:
- How to prepare data for NER tagging
- How to load a pre-trained Transformer model
- How to fine-tune a Transformer model
- How to evaluate a fine-tuned Transformer model

I hope you find this tutorial helpful!
Download Code: https://github.com/laxmimerit/NLP-Tutorials-with-HuggingFace

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