Fine-tuned DistilBERT-base-uncased for IMDB Classification
Model Description
DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on IMDB dataset with the purpose of classifying positive reviews from the bad ones. The model predicts these 2 classes.
The model is a fine-tuned version of DistilBERT.
It was fine-tuned on IMDB dataset [https://huggingface.co/datasets/imdb].
This model is a fine-tuned version of distilbert-base-uncased on IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2265
- Accuracy: 0.9312
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.2273 | 1.0 | 1563 | 0.2471 | 0.9122 |
0.1524 | 2.0 | 3126 | 0.2265 | 0.9312 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
How to Use
from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/imdb_text_classifier")
classifier("I see it and it was awesome.")
Output:
[{'label': 'POSITIVE', 'score': 0.9958052635192871}]
Contact
Please reach out to luca.flammia@gmail.com if you have any questions or feedback.
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