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Librarian Bot: Add base_model information to model (#1)
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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 in 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-gio-testing
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: datasetX
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8333333333333334
            name: Accuracy
          - type: f1
            value: 0.8802816901408451
            name: F1

platzi-distilroberta-base-mrpc-glue-gio-testing

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

  • Loss: 0.5089
  • Accuracy: 0.8333
  • F1: 0.8803

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: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5359 1.09 500 0.5089 0.8333 0.8803
0.3784 2.18 1000 0.5384 0.8407 0.8850

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3