metadata

datasets:
  - imdb
language:
  - en
library_name: transformers
pipeline_tag: text-classification
tags:
  - movies
  - gpt2
  - sentiment-analysis
  - fine-tuned

Fine-tuned GPT-2 Model for IMDb Movie Review Sentiment Analysis

Model Description

This is a GPT-2 model fine-tuned on the IMDb movie review dataset for sentiment analysis. It classifies a movie review text into two classes: "positive" or "negative".

Intended Uses & Limitations

This model is intended to be used for binary sentiment analysis of English movie reviews. It can determine whether a review is positive or negative. It should not be used for languages other than English, or for text with ambiguous sentiment.

How to Use

Here's a simple way to use this model:

from transformers import GPT2Tokenizer, GPT2ForSequenceClassification

tokenizer = GPT2Tokenizer.from_pretrained("hipnologo/gpt2-imdb-finetune")
model = GPT2ForSequenceClassification.from_pretrained("hipnologo/gpt2-imdb-finetune")

text = "Your review text here!"

# encoding the input text
input_ids = tokenizer.encode(text, return_tensors="pt")

# Move the input_ids tensor to the same device as the model
input_ids = input_ids.to(model.device)

# getting the logits 
logits = model(input_ids).logits

# getting the predicted class
predicted_class = logits.argmax(-1).item()

print(f"The sentiment predicted by the model is: {'Positive' if predicted_class == 1 else 'Negative'}")

Training Procedure

The model was trained using the 'Trainer' class from the transformers library, with a learning rate of 2e-5, batch size of 1, and 3 training epochs.

Fine-tuning Details

The model was fine-tuned using the IMDb movie review dataset.