metadata

license: apache-2.0
base_model: distilroberta-base
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-joselier
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.821078431372549
          - name: F1
            type: f1
            value: 0.8809135399673736

platzi-distilroberta-base-mrpc-glue-joselier

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: 0.5993
Accuracy: 0.8211
F1: 0.8809

Model description

This model uses transfer learning to classify 2 sentences (a string of 2 sentences separated by a comma) in "Equivalent" or "Not Equivalent". The model platzi-distilroberta-base-mrpc-glue-joselier was programmed as part of a class from Platzi's course "Curso de Transfer Learning con Hugging Face"

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
0.5365	1.09	500	0.5993	0.8211	0.8809
0.3458	2.18	1000	0.8336	0.8235	0.8767

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3