FlySharker/distilbert-rotten-tomatoes

Introduction

This repository contains a fine-tuned version of DistilBERT optimized for sentiment classification.

Model Description

• Developed by: FlySharker
• Model type: Transformer-based text classifier
• Language(s) (NLP): English
• License: apache-2.0
• Finetuned from model: distilbert-base-uncased

Task & Dataset

The model was trained on the Rotten Tomatoes dataset, which consists of 10,662 movie snippets from the Rotten Tomatoes editorial staff. The goal is to determine whether a given review snippet is "Fresh" (positive) or "Rotten" (negative).

Quick Start (Usage)

from transformers import pipeline

classifier = pipeline("text-classification", model="你的用户名/你的模型名")
result = classifier("This movie was an absolute masterpiece with stunning visuals!")
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.999}]