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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.")
[{'label': 'POSITIVE', 'score': 0.9958052635192871}]


Please reach out to luca.flammia@gmail.com if you have any questions or feedback.

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