README.md

metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: finetuning-DistillBERT-amazon-polarity
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_polarity
          type: sentiment
          args: default
        metrics:
          - type: accuracy
            value: 0.9166666666666666
            name: Accuracy
          - type: loss
            value: 0.1919892132282257
            name: Loss
          - type: f1
            value: 0.9169435215946843
            name: F1
datasets:
  - amazon_polarity
pipeline_tag: text-classification

finetuning-DistillBERT-amazon-polarity

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

• Loss: 0.1920
• Accuracy: 0.9167
• F1: 0.9169

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2