README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - amazon_polarity
metrics:
  - accuracy
  - f1
model-index:
  - name: SentimentClassifier
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: amazon_polarity
          type: amazon_polarity
          args: amazon_polarity
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.91
          - name: F1
            type: f1
            value: 0.91

SentimentClassifier

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

• Loss: 0.4425
• Accuracy: 0.91
• F1: 0.91

Model description

More information needed

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: 3e-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: 5

Training results

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1