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distilbert-rotten-tomatoes
FlySharker/distilbert-rotten-tomatoes
distilbert-rotten-tomatoes is a text classification model from FlySharker. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 31, 2026
- Updated
- Mar 31, 2026
- SHA
- 36f128ea
Try a prompt
Base models
Model card
From the Hugging Face model README
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}]
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Languages
- en
- Created
- Mar 31, 2026
- Updated
- Mar 31, 2026