---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text: ["Jamaica is the place where Usain Bolt was born", "Usain Bolt was born in the island of Jamaica"]
  example_title: Equivalent
- text: ["Jamaica is the place where Usain Bolt was born", "There are many fast runners in Jamaica"]
  example_title: Not Equivalent

model-index:
- name: platzi-distilroberta-base-mrpc-glue
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: train
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7843137254901961
    - name: F1
      type: f1
      value: 0.8287937743190662
---

<!-- 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

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: 0.5320
- Accuracy: 0.7843
- F1: 0.8288

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5381        | 1.09  | 500  | 0.5320          | 0.7843   | 0.8288 |
| 0.3849        | 2.18  | 1000 | 0.5543          | 0.8431   | 0.8869 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2