---
language:
- en
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-large-mrpc-100
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/MRPC
      type: tmnam20/VieGLUE
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8823529411764706
    - name: F1
      type: f1
      value: 0.9163763066202091
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-large-mrpc-100

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3028
- Accuracy: 0.8824
- F1: 0.9164
- Combined Score: 0.8994

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0