Huggingface¶

Huggingface is a popular NLP library that provides a lot of pre-trained models for various tasks such as text classification, named entity recognition, and question answering. It also provides a simple interface for training and fine-tuning these models on custom datasets.

Here is an example of how to use Huggingface to fine-tune a pre-trained model for sentiment analysis on a custom dataset:

from transformers import pipeline

classifier = pipeline('sentiment-analysis')

# Custom dataset
data = [
    ("I love this movie", "positive"),
    ("This is a terrible movie", "negative"),
    ("I hate it", "negative"),
    ("I don't care", "neutral"),
    ("I'm so happy today", "positive"),
]

# Fine-tune the pre-trained model on the custom dataset
classifier.train(data)

# Test the fine-tuned model
result = classifier("I'm so happy today")
print(result)

This code will fine-tune a pre-trained model for sentiment analysis on the custom dataset and then test the fine-tuned model on a sample sentence. The output will be a dictionary containing the predicted sentiment and its corresponding score.

You can also use Huggingface to perform other NLP tasks such as text classification, named entity recognition, and question answering.