finetuned_bert – AI Model by ashwini10521 | AlphaNeural AI

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ashwini10521/
finetuned_bert

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text-classification

sentiment-analysis

google-bert/bert-base-uncased

text-embeddings-inference

endpoints_compatible

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🎬 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

python

from transformers import pipeline

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

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