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sudeshna5/
Text_Classification

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🤖 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:

python

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}]

sudeshna5/Text_Classification

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

🧠 Model Description

📊 Training Details

🚀 How to Use

sudeshna5/
Text_Classification