---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# platzi-distilroberta-base-mrpc-glue-joselier

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/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"](https://platzi.com/cursos/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