DistilBERT Metacritic Sentiment Classifier
Description
This model performs sentiment analysis on video game reviews from Metacritic.
It has been fine-tuned from distilbert-base-uncased using the dataset: Wada1/Metacritic_Games_Reviews_Dataset
The model classifies reviews into:
- Positive
- Negative
Neutral reviews were removed before training.
Dataset
Features used:
- Review → review text
- Sentiment → target label
Label mapping:
- Positive → 1
- Negative → 0
Neutral reviews were excluded.
Evaluation Results
Performance on evaluation split:
- Accuracy: 0.8953
- F1-score: 0.9164
- Precision: 0.8886
- Recall: 0.9459
Usage
Example:
from transformers import pipeline
classifier = pipeline( "text-classification", model="angelhm/distilbert-metacritic-sentiment-classifier" )
result = classifier("This game is amazing. Great story and gameplay.") print(result)
Labels
- Positive → positive review
- Negative → negative review
Limitations
- Only works with English text
- Trained only on Metacritic video game reviews
- May not generalize well to other domains
- Neutral sentiment is not predicted
Demo
You can test the model interactively in the Hugging Face Space:
https://huggingface.co/spaces/angelhm/distilbert-metacritic-sentiment-classifier
Write a video game review in English and the model will predict whether the sentiment is Positive or Negative.
- Downloads last month
- 52