Natural Language Processing
Collection
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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:
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.
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.")
print(result)
#[{'label': 'negative', 'score': 0.9994848966598511}]
More information needed
The following hyperparameters were used during training:
(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 |
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