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
model-index:
  - name: platzi-distilroberta-base-mrpc-glue-Jonathan-Castillo
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: datasetX
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8382352941176471
          - name: F1
            type: f1
            value: 0.8850174216027874

platzi-distilroberta-base-mrpc-glue-Jonathan-Castillo

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

Loss: 0.6592
Accuracy: 0.8382
F1: 0.8850

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5304	1.09	500	0.7888	0.7966	0.8659
0.3762	2.18	1000	0.6592	0.8382	0.8850
0.2122	3.27	1500	0.9311	0.8333	0.8828
0.1345	4.36	2000	0.9803	0.8505	0.8968
0.066	5.45	2500	1.0714	0.8578	0.8968
0.0306	6.54	3000	1.2510	0.8456	0.8923
0.0198	7.63	3500	1.2969	0.8456	0.8916

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3