DistilBERT IMDb Sentiment Analysis
(8k Training Data)
This model is a fine-tuned version of distilbert-base-uncased for binary sentiment classification on movie reviews.
The model predicts whether a review expresses positive (1) or negative (0) sentiment.
Model Description
This model was fine-tuned using the Transformers library on a cleaned subset of the IMDb movie review dataset.
Key characteristics:
Base model: distilbert-base-uncased
Task: Binary sentiment classification
Labels:
0 → Negative
1 → Positive
Training epochs: 10
Maximum sequence length: 128 tokens
The dataset was preprocessed by removing HTML tags using BeautifulSoup.
Dataset
The training dataset is derived from the IMDb sentiment dataset.
A balanced subset was sampled and cleaned before training.
Dataset split:
- Train: 8,000 reviews (4,000 positive / 4,000 negative)
- Validation: 2,000 reviews (1,000 positive / 1,000 negative)
- Test: 2,000 reviews (1,000 positive / 1,000 negative)
HTML tags were removed using BeautifulSoup and stored in a cleaned_text column.
Dataset repository: https://huggingface.co/datasets/Mahika2026/imdb-sentiment-dataset
Evaluation Results
Best validation metrics during training:
Loss: 0.4104 Accuracy: 0.8565 Precision: 0.8411 Recall: 0.8790 F1 Score: 0.8597
Test Set Performance
Evaluation on the 2,000 review test set produced the following results:
Accuracy: 0.865
| Class | Precision | Recall | F1 |
|---|---|---|---|
| Negative 0 | 0.8875 | 0.8360 | 0.8610 |
| Positive 1 | 0.8450 | 0.8940 | 0.8688 |
Confusion Matrix:
| True\Pred | Negative | Positive |
|---|---|---|
| Negative | 836 | 164 |
| Positive | 106 | 894 |
Training Procedure
The model was trained using the Hugging Face Transformers Trainer API.
Training hyperparameters:
learning_rate: 2e-5 train_batch_size: 32 eval_batch_size: 32 gradient_accumulation_steps: 2 effective_batch_size: 64 num_epochs: 10 max_sequence_length: 128 weight_decay: 0.01 evaluation_strategy: epoch save_strategy: epoch mixed_precision_training: FP16
Optimizer:
AdamW optimizer with linear learning rate scheduler.
Intended Uses
This model can be used for:
- Movie review sentiment analysis
- Binary text classification experiments
- Educational NLP projects
- Benchmarking small fine-tuned Transformer models
Limitations
- The model is trained on a small subset (8k samples) of the IMDb dataset.
- Performance may degrade on other domains (product reviews, tweets, etc.).
- Long texts beyond 128 tokens will be truncated.
Framework Versions
- PyTorch: 2.10.0+cu128
- Transformers: 5.0.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
- scikit-learn: 1.6.1
- accelerate: 1.12.0
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