best_distilbert_model
Model Description
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the Pitchfork Album Reviews dataset. The model is designed to classify sentiment in album reviews as positive (1) or negative (0).
Intended Uses & Limitations
✅ Intended Use
- Primary Task: Sentiment analysis for album reviews.
- Dataset: Fine-tuned on 19,305 album reviews (binary labels: 1 = Positive, 0 = Negative).
- Ideal for: Music review sentiment analysis.
⚠️ Limitations
- May not generalize well to non-music-related reviews.
- Optimized for binary sentiment classification, not multi-class sentiment.
Training & Evaluation Data
Dataset Details
- Dataset Source: Pitchfork Album Reviews
- Training Set Size: 19,305 reviews
- Test Set Size: 1,566 reviews
- Labels: Binary classification (0 = Negative, 1 = Positive)
Evaluation Metrics
- Best Test Accuracy: 73.44%
- Best Generalization Settings:
- Dropout:
0.2 - Learning Rate:
5e-5 - Batch Size:
16 - Warmup Steps:
500
- Dropout:
Training Procedure
Hyperparameters Used
- Learning Rate:
5e-5 - Train Batch Size:
16 - Eval Batch Size:
16 - Epochs:
2 - Weight Decay:
0.01 - Dropout:
0.2 - Optimizer: AdamW
(betas=(0.9, 0.999), epsilon=1e-08) - LR Scheduler:
Linear - Warmup Steps:
500
Framework Versions
- Transformers:
4.48.3 - PyTorch:
2.6.0+cu124 - Datasets:
3.4.1 - Tokenizers:
0.21.1
Performance Metrics
- Best Test Accuracy: 73.44%
- Evaluation Metrics Used: Accuracy
- Generalization Settings:
- Dropout: 0.2
- Learning Rate: 5e-5
- Batch Size: 16
- Warmup Steps: 500
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