distilbert-base-uncased-imdb-sentiment

tags:

• text-classification
• sentiment-analysis
• distilbert
• imdb datasets:
• imdb metrics:
• accuracy

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DistilBERT Fine-tuned for IMDB Sentiment Analysis

This is a DistilBERT model fine-tuned on the IMDB movie review dataset for sentiment analysis (binary classification). The model classifies movie reviews as either positive (1) or negative (0).

Model Details

• Model type: Text classification (DistilBERT)
• Language: English
• License: Apache 2.0
• Fine-tuned from: distilbert-base-uncased
• Dataset: IMDB

Training Details

• Training data: 5,000 samples from IMDB train set
• Validation data: 1,000 samples from IMDB test set
• Training epochs: 3
• Batch size: 8 (with gradient accumulation steps of 2)
• Mixed precision training: FP16 enabled
• Training hardware: Google Colab
• Metrics: Accuracy

How to Use

You can use this model directly with the Hugging Face pipeline:

from transformers import pipeline

classifier = pipeline("text-classification", model="uncleMehrzad/distilbert-base-uncased-imdb-sentiment")
result = classifier("This movie was absolutely fantastic! The acting was superb.")
print(result)  # [{'label': 'POSITIVE', 'score': 0.999...}]

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Amirreza Mehrzadian]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [Apache 2.0]
• Finetuned from model [Distill bert]: [More Information Needed]

Metrics

[accuracy]

Results

[89.6 on 1000 subsample of imdb dataset]