README.md

metadata

license: apache-2.0
datasets:
  - amazon_polarity
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased-finetuned-sentiment-amazon
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_polarity
          type: sentiment
          args: default
        metrics:
          - type: accuracy
            value: 0.961
            name: Accuracy
          - type: loss
            value: 0.116
            name: Loss
          - type: f1
            value: 0.96
            name: F1
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_polarity
          type: amazon_polarity
          config: amazon_polarity
          split: test
        metrics:
          - type: accuracy
            value: 0.94112
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9321570625232675
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.95149
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.9849019044624999
            name: AUC
            verified: true
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          - type: f1
            value: 0.9417243188138998
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.16342754662036896
            name: loss
            verified: true
            verifyToken: >-
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distilbert-sentiment

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.")
print(result)
#[{'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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