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neuroapps_sentiment_classifier

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2332
  • Accuracy: 0.9319

Model description

This model outputs the sentiment value, either positive or negative from the sentence or phrase.

Intended uses & limitations

This model could be used for extracting sentiments from product reviews, product feedback, or general conversational text.

Training and evaluation data

his model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.

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
0.2335 1.0 1563 0.1892 0.9277
0.1487 2.0 3126 0.2332 0.9319

Framework versions

  • Transformers 4.27.3
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2
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Dataset used to train neuroapps/sentiments_classifier

Evaluation results