README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
datasets:
  - amazon_us_reviews
model-index:
  - name: distilbert-amazon-shoe-reviews
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          type: amazon_us_reviews
          name: Amazon US reviews
          split: Shoes
        metrics:
          - type: accuracy
            value: 0.6819221967963387
            name: Accuracy

distilbert-amazon-shoe-reviews

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9536
• Accuracy: 0.5767
• F1: [0.62380713 0.45806452 0.5077951 0.56106774 0.73541247]
• Precision: [0.62537764 0.45920398 0.49326923 0.58508403 0.72376238]
• Recall: [0.62224449 0.45693069 0.52320245 0.53894533 0.74744376]

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.9704	1.0	2813	0.9536	0.5767	[0.62380713 0.45806452 0.5077951 0.56106774 0.73541247]	[0.62537764 0.45920398 0.49326923 0.58508403 0.72376238]	[0.62224449 0.45693069 0.52320245 0.53894533 0.74744376]

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu102
• Datasets 2.2.2
• Tokenizers 0.12.1