README.md

metadata

library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
  - sentiment-analysis
  - text-classification
  - bert
  - transformers
  - pytorch
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: finetuned-bert-sentiment
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: IMDb Movie Reviews
          type: imdb
        metrics:
          - type: accuracy
            value: 0.9225
          - type: f1
            value: 0.9238
          - type: precision
            value: 0.9086
          - type: recall
            value: 0.9395

🎬 Finetuned BERT for Sentiment Analysis

This model is a fine-tuned version of BERT (bert-base-uncased) for binary sentiment classification (positive vs negative).

It is trained on the IMDb movie reviews dataset, a widely used benchmark for sentiment analysis tasks.

---

🚀 Model Performance

Metric	Score
Accuracy	92.25%
F1 Score	92.38%
Precision	90.86%
Recall	93.95%

Confusion Matrix Insights

• Strong balance between positive and negative predictions
• Slight tendency toward higher recall (fewer false negatives)
• Overall robust generalization on full test dataset (25,000 samples)

---

📌 Model Description

This project demonstrates fine-tuning of a pre-trained Transformer model for NLP classification tasks using the Hugging Face ecosystem.

Key features:

• Pretrained BERT encoder
• Fine-tuned for binary sentiment classification
• Implemented using Hugging Face Transformers Trainer API
• Evaluated using standard classification metrics

---

📊 Dataset

• Name: IMDb Movie Reviews Dataset
• Size:
  • Train: 25,000 samples
  • Test: 25,000 samples
• Classes:
  • 0 → Negative
  • 1 → Positive

The dataset is balanced across both classes.

---

🏋️ Training Procedure

Hyperparameters

• Learning rate: 2e-5
• Batch size: 8
• Epochs: 2
• Optimizer: AdamW
• Scheduler: Linear decay
• Mixed precision: Enabled (FP16)

Training Details

• Framework: Hugging Face Trainer
• Hardware: Google Colab GPU
• Loss function: Cross-entropy

---

🧠 Intended Use

This model can be used for:

• Sentiment analysis on movie reviews
• Product review classification
• Social media sentiment detection
• NLP learning and experimentation

---

⚠️ Limitations

• Trained only on English text
• Domain-specific (movie reviews) → may not generalize perfectly to other domains
• Binary classification only (no neutral sentiment)
• May inherit biases present in training data

---

🛠️ How to Use

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="ashwini10521/finetuned_bert")

result = classifier("This movie was absolutely amazing!")
print(result)