tags: - text-classification - sentiment-analysis - distilbert - imdb datasets: - imdb metrics: - accuracy
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...}]
This is the model card of a ๐ค 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]
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