---
license: apache-2.0
base_model: distilroberta-base
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
widget:
 - text: '["Yucaipa owned Dominicks before selling the chain to Safeway in 1998 for $ 2.5 billion.","Yucaipa bought Dominicks 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 Stewarts 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-anthony-zakhaur
  results: []
---

<!-- 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-anthony-zakhaur

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: 0.7964
- Accuracy: 0.8284
- F1: 0.8763

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4026        | 1.09  | 500  | 0.7964          | 0.8284   | 0.8763 |
| 0.2436        | 2.18  | 1000 | 0.8661          | 0.8333   | 0.8807 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1