Fine-Tuning Huggingface Model With Custom Dataset

Introduction

Huggingface is a popular library for natural language processing (NLP) tasks. It provides pre-trained models for various NLP tasks such as text classification, question answering, and language translation. Fine-tuning is a technique that allows us to customize these pre-trained models for our specific use case by training them on our own dataset. In this article, we will discuss how to fine-tune a Huggingface model with a custom dataset.

Common Mistakes

When it comes to fine-tuning Huggingface models with custom datasets, there are some common mistakes that people make:

Not preparing the dataset properly

The dataset should be prepared properly before fine-tuning the model. This includes cleaning the data, splitting it into train/validation/test sets, and converting it into the appropriate format for the model. Failure to do so can result in poor performance of the model.

Choosing the wrong model

Choosing the wrong pre-trained model for your task can also lead to poor performance. You should choose a model that is pre-trained on a similar task and has the same architecture as your target task. Huggingface provides a wide range of pre-trained models, so it’s important to choose the right one for your use case.

Not fine-tuning long enough

Fine-tuning a model takes time and patience. Not training the model for long enough can result in poor performance. It’s important to monitor the model’s performance on the validation set and fine-tune it until the performance stops improving.

Examples

Here are two examples of fine-tuning Huggingface models with custom datasets:

Sentiment Analysis

Suppose we want to build a sentiment analysis model for customer reviews of a product. We can fine-tune the pre-trained BERT model provided by Huggingface on our own dataset of customer reviews. We first prepare the dataset by cleaning the data and converting it into the appropriate format. We then fine-tune the model on the dataset by training it on the train set and validating it on the validation set. We monitor the model’s performance on the validation set and fine-tune it until the performance stops improving. Once the model is fine-tuned, we can use it to predict the sentiment of new customer reviews.

Question Answering

Suppose we want to build a question answering system for a specific domain such as medical research. We can fine-tune the pre-trained RoBERTa model provided by Huggingface on our own dataset of medical research papers. We first prepare the dataset by cleaning the data and converting it into the appropriate format. We then fine-tune the model on the dataset by training it on the train set and validating it on the validation set. We monitor the model’s performance on the validation set and fine-tune it until the performance stops improving. Once the model is fine-tuned, we can use it to answer questions related to medical research.