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)

Developed by: <a href="https://huggingface.co/FlySharker" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">FlySharker
Model type: Transformer-based text classifier
Language(s) (NLP): English
License: apache-2.0
Finetuned from model: distilbert-base-uncased

python

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}]

FlySharker/distilbert-rotten-tomatoes

Introduction

Model Description

Task & Dataset

Quick Start (Usage)

FlySharker/
distilbert-rotten-tomatoes