Edit model card

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 [email protected] if you have any questions or feedback.

Downloads last month
20
Safetensors
Model size
67M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.