---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
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
---

<!-- 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-Jonathan-Castillo

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