sudeshna5/Text_Classification

🤖 DistilBERT Fine-Tuned on IMDb Movie Reviews for Sentiment Classification

DistilBERT model fine-tuned on the IMDb dataset for binary sentiment classification (Positive/Negative).

🧠 Model Description

• —Architecture: DistilBERT (lighter version of BERT)
• —Task: Sentiment Analysis / Text Classification
• —Dataset: IMDb movie reviews (binary sentiment: positive or negative)
• —Base Model: distilbert-base-uncased
• —Fine-tuned using: Hugging Face Transformers + Trainer API

📊 Training Details

• —Training Samples: 5,000 (subset of IMDb train split)
• —Test Samples: 1,000 (subset of IMDb test split)
• —Epochs: 2
• —Batch Size: 8
• —Optimizer: AdamW
• —Evaluation Metric: Accuracy

🚀 How to Use

You can directly use this model for inference using transformers pipeline:

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="your-username/distilbert-finetuned-imdb-sentiment")

print(classifier("This movie was absolutely amazing!"))
# Output: [{'label': 'POSITIVE', 'score': 0.99}]