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Saana2005/distilbert-imdb-sentiment-classifier

sourceHugging Faceupdated 16d agoView on Hugging Face

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Model Card

DistilBERT IMDb Sentiment Classifier

This model is a fine-tuned version of `distilbert-base-uncased` for binary sentiment classification of English IMDb movie reviews. It predicts either NEGATIVE or POSITIVE.

The uploaded checkpoint is the seed-42 model, selected because it achieved the highest validation accuracy among three independently trained seeds.

Model details

Item	Value
Architecture	DistilBERT for sequence classification
Parameters	66,955,010
Language	English
Classes	`NEGATIVE`, `POSITIVE`
Maximum input length	256 tokens
Framework	PyTorch / Transformers
Base model	`distilbert/distilbert-base-uncased`

Intended use

The model is intended for educational experiments and binary sentiment classification of English movie reviews. It can be used through the Transformers pipeline:

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Saana2005/distilbert-imdb-sentiment-classifier",
)

result = classifier(
    "The movie was emotional, engaging, and beautifully acted.",
    truncation=True,
    max_length=256,
)
print(result)

Training data

The model was trained with the `stanfordnlp/imdb` dataset.

Split	Examples	Purpose
Training	20,000	Parameter updates
Validation	5,000	Best-checkpoint selection
Test	25,000	Final evaluation
Unsupervised	50,000	Not used

The official 25,000-example training split was divided with fixed split seed 2026. The official test split was kept separate from model training and checkpoint selection.

Training procedure

Hyperparameter	Value
Epochs	2
Training batch size	32
Evaluation batch size	64
Learning rate	`2e-5`
Weight decay	`0.01`
Warmup	10% of training steps
Optimizer	AdamW
Scheduler	Linear decay
Precision	FP16
Random seed	42
Selection metric	Validation accuracy

Evaluation

The uploaded seed-42 checkpoint produced:

Metric	Result
Best validation accuracy	91.44%
Test accuracy	91.14%
Test F1	91.15%

Three runs with seeds 7, 42, and 123 achieved mean test accuracy of 91.16% ± 0.02% and mean test F1 of 91.21% ± 0.07% (sample standard deviation).

Limitations

—The model handles only two sentiment classes and cannot represent neutral or mixed sentiment directly.
—It was trained on English movie reviews and is not a general-purpose emotion or opinion classifier.
—Inputs longer than 256 tokens are truncated, so important conclusions near the end of a long review may be omitted.
—Sarcasm, conflicting opinions, plot-heavy language, and annotation noise can cause errors.
—Confidence scores are model probability estimates, not guarantees that a prediction is correct.
—The model may inherit biases present in its pretrained model and IMDb data.

Source code

Training, evaluation, error-analysis, Slurm, command-line inference, and Gradio code are available in the `Sana1025/distilbert-imdb-sentiment-classifier` GitHub repository.