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metadata
license: apache-2.0
tags:
  - text-classification
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
widget:
  - text:
      - >-
        Yucaipa owned Dominick 's before selling the chain to Safeway in 1998
        for $ 2.5 billion.
      - >-
        Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to
        Safeway for $ 1.8 billion in 1998.
    example_title: Not equivalent
  - text:
      - >-
        Revenue in the first quarter of the year dropped 15 percent from the
        same period a year earlier.
      - >-
        With the scandal hanging over Stewart's company revenue the first
        quarter of the year dropped 15 percent from the same period a year
        earlier.
    example_title: Equivalent
base_model: distilroberta-base
model-index:
  - name: platzi-distilroberta-base-mrpc-glue-jose-alcocer
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8259803921568627
            name: Accuracy
          - type: f1
            value: 0.875219683655536
            name: F1

platzi-distilroberta-base-mrpc-glue-jose-alcocer

This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets. It achieves the following results on the evaluation set:

  • Loss: 0.5314
  • Accuracy: 0.8260
  • F1: 0.8752

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5317 1.09 500 0.5314 0.8260 0.8752
0.3602 2.18 1000 0.6965 0.8358 0.8855
0.2204 3.27 1500 0.7159 0.8529 0.8962

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3