distilbert-imdb-lora

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

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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

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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. [More Information Needed]

Training Procedure

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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

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Testing Data, Factors & Metrics

Testing Data

500-example held-out split from stanfordnlp/imdb test set.

Metrics

Accuracy

Factors

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Results

Summary

Model Examination [optional]

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Environmental Impact

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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

Citation [optional]

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Glossary [optional]

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Model Card Authors [optional]

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