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This model is a fine-tuned version of distilbert-base-uncased on a subset of the amazon-polarity dataset.

[Update 10/10/23] The model has been retrained on a larger part of the dataset with an improvement on the loss, f1 score and accuracy. It achieves the following results on the evaluation set:

  • Loss: 0.116
  • Accuracy: 0.961
  • F1_score: 0.960

Model description

This sentiment classifier has been trained on 360_000 samples for the training set, 40_000 samples for the validation set and 40_000 samples for the test set.

Intended uses & limitations

from transformers import pipeline

# Create the pipeline
sentiment_classifier = pipeline('text-classification', model='AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon')

# Now you can use the pipeline to get the sentiment
result = sentiment_classifier("This product doesn't fit me at all.")
#[{'label': 'negative', 'score': 0.9994848966598511}]

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 1270
  • optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 150
  • num_epochs: 2
  • weight_decay: 0.01

Training results

(Previous results before retraining from the model evaluator)

key value
eval_accuracy 0.94112
eval_auc 0.9849
eval_f1_score 0.9417
eval_precision 0.9321
eval_recall 0.95149

Framework versions

  • Transformers 4.34.0
  • Pytorch lightning 2.0.9
  • Tokenizers 0.14.0

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Finetuned from

Dataset used to train AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon

Collection including AdamCodd/distilbert-base-uncased-finetuned-sentiment-amazon

Evaluation results