distilbert-imdb-lora
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Model Details
Model Description
Fine-tuned version of distilbert-base-uncased for binary sentiment classification,
adapted using LoRA (Low-Rank Adaptation) rather than full fine-tuning.
- Developed by: Mohammad (moh0405)
- Model type: Text classification (sequence classification)
- Language(s): English
- License: Apache 2.0
- Finetuned from model: distilbert-base-uncased
Model Sources [optional]
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Uses
Direct Use
Classifies English-language text (originally movie reviews) as POSITIVE or NEGATIVE sentiment. Suitable for quick sentiment tagging tasks similar in style to IMDB reviews.
Downstream Use [optional]
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Out-of-Scope Use
Not intended for nuanced/mixed sentiment detection, non-English text, or domains far from movie reviews (e.g. financial sentiment, medical text) without further fine-tuning. Trained on a small subset for a learning exercise — not validated for production use.
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
```python from transformers import pipeline
classifier = pipeline("text-classification", model="moh0405/distilbert-imdb-lora") result = classifier("This movie was surprisingly good.") print(result) ```
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Training Details
Training Data
Subset of the IMDB movie review dataset (stanfordnlp/imdb) — 2,000 training examples,
500 evaluation examples, randomly sampled (seed=42) from the full 25,000/25,000 split.
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Method: LoRA (PEFT), r=8, alpha=16, dropout=0.1, target_modules=["q_lin","v_lin"]
- Trainable parameters: 739,586 / 67,694,596 total (1.09%)
- Epochs: 1
- Batch size: 16 (train), 32 (eval)
- Training regime: fp32
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
500-example held-out split from stanfordnlp/imdb test set.
Metrics
Accuracy
Factors
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Results
| Stage | Accuracy |
|---|---|
| Before fine-tuning | 50.8% |
| After fine-tuning (1 epoch) | 78.6% |
Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
Apple Mac Mini (Apple Silicon, MPS backend)
Software
transformers, peft, datasets, PyTorch
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