metadata
language: en
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- distilbert
- imdb
- huggingface
datasets:
- imdb
metrics:
- accuracy
DistilBERT Sentiment Analysis (IMDB)
This model is a fine-tuned version of DistilBERT-base-uncased for binary sentiment classification on movie reviews.
It classifies reviews as Positive or Negative.
Model Details
- Base Model: distilbert-base-uncased
- Dataset: IMDB Movie Reviews (25k train, 25k test)
- Task: Sentiment Analysis (Positive / Negative)
- Training: Fine-tuned using Hugging Face Trainer API
Results
- Accuracy on small subset (2k examples): 89.0%
- Expected accuracy on full dataset: ~92-94%
Usage
from transformers import pipeline
classifier = pipeline(
"sentiment-analysis",
model="kckdeepak/imdb-distilbert-sentiment-analysis"
)
result = classifier("This movie was fantastic!")
print(result)