Fine-Tuned BERT for Named-Entity Recognition (NER) using Transformers and PyTorch

In this notebook, the process of fine-tuning a pre-trained BERT-based model (DistilBERT) for the task of Named-Entity Recognition (NER) will be explored. NER is a fundamental task in Natural Language Processing (NLP) that involves identifying entities such as persons, locations, and organizations in text.

Why Fine-Tune BERT for NER?

Pre-trained models like BERT and DistilBERT are general-purpose language models trained on vast amounts of text data. However, for domain-specific tasks like NER, fine-tuning is essential to adapt the model to the specific task requirements and dataset. This enables the model to achieve higher accuracy by leveraging task-specific labeled data.

How to Fine-Tune BERT?

This notebook demonstrates two approaches to fine-tuning:

1. Trainer API from the Transformers Library
   The first method utilizes the Trainer API provided by the Transformers library, which simplifies the fine-tuning process.

2. Custom Training Loop with PyTorch
   The second method involves implementing a custom training loop using PyTorch for greater flexibility and control.

Both models will be trained and evaluated, followed by predictions using the trained models.

Steps

0. Install the basic libraries

   • Transformers, Datasets, and Evaluate libraries.

1. Load and understand the dataset

   • Gain insights into the data used for the NER task.

2. Preprocess the data

   • Transform the data to make it compatible with the model.

3. Fine-tune the model using the Trainer API

   • Fine-tune the model with the built-in Trainer API for simplicity.

4. Fine-tune the model using a custom PyTorch implementation

   • Implement a custom training loop for more control over the training process.

5. Make predictions using the pipeline API on the Trainer API fine-tuned model

   • Use the trained model with the pipeline API to make predictions.

6. Make predictions using the PyTorch fine-tuned model

   • Similarly, use the PyTorch fine-tuned model to make predictions.

Model Availability

The model is saved on my account of HuggingFace. It can be imported and used in your own projects. For example, see Step 5 for instructions on how to load and use the model.

• Wencho/distilledbert-finetuned-ner