README.md

metadata

language:
  - en
tags:
  - sentiment-analysis
  - text-classification
  - bert
  - manav
  - ManavDhayeCoder/sentiment-bert
  - ManavDhaye
pipeline_tag: text-classification
base_model:
  - google-bert/bert-base-uncased
datasets:
  - imdb
library_name: transformers
widget:
  - text: This movie was amazing!
  - text: Worst movie I have ever seen.
model-index:
  - name: sentiment-bert
    results: []
metrics:
  - accuracy

📘 BERT Sentiment Analysis Model (Fine-Tuned on IMDB)

This model is a fine-tuned version of google-bert/bert-base-uncased, trained on the IMDB movie reviews dataset for binary sentiment classification.

It predicts whether text expresses negative or positive sentiment.

This model is hosted by @ManavDhayeCoder.

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🚀 Model Overview

Property	Value
Base model	google-bert/bert-base-uncased
Task	Sentiment Analysis (Sequence Classification)
Labels	negative / positive
Dataset	IMDB
Library	Hugging Face Transformers
Format	model.safetensors

The model has two classes:

• LABEL_0 → negative
• LABEL_1 → positive

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🔥 Quick Usage Example

from transformers import pipeline

clf = pipeline("text-classification", model="ManavDhayeCoder/sentiment-bert")

print(clf("This movie was amazing!"))