README.md

metadata

language: en
license: mit
tags:
  - sentiment-analysis
  - imdb
  - bert
  - transformers
  - text-classification
model-index:
  - name: sentiment-bert-imdb
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: IMDB Movie Reviews
          type: imdb
        metrics:
          - type: accuracy
            value: 0.93

Sentiment-BERT-IMDB

A BERT-based model fine-tuned on the IMDB movie reviews dataset for binary sentiment classification (positive/negative). This model is intended for quick deployment and practical use in applications like review analysis, recommendation systems, and content moderation.

Model Details

• Architecture: bert-base-uncased
• Task: Sentiment classification (positive vs. negative)
• Dataset: IMDB
• Classes: positive, negative
• Tokenizer: bert-base-uncased

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model = AutoModelForSequenceClassification.from_pretrained("HrishikeshDeore/sentiment-bert-imdb")
tokenizer = AutoTokenizer.from_pretrained("HrishikeshDeore/sentiment-bert-imdb")

nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
result = nlp("This movie was absolutely fantastic!")
print(result)