transformers safetensors xlm-roberta text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:FacebookAI/xlm-roberta-large base_model:finetune:FacebookAI/xlm-roberta-large license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-large
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-qqp-1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/QQP
      type: tmnam20/VieGLUE
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.9047736829087312
      name: Accuracy
    - type: f1
      value: 0.8721609775534599
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-large-qqp-1

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2725
- Accuracy: 0.9048
- F1: 0.8722
- Combined Score: 0.8885

## 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: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3045        | 0.88  | 10000 | 0.2747          | 0.8808   | 0.8324 | 0.8566         |
| 0.2256        | 1.76  | 20000 | 0.2695          | 0.8957   | 0.8617 | 0.8787         |
| 0.1643        | 2.64  | 30000 | 0.2808          | 0.9019   | 0.8684 | 0.8851         |


### Framework versions

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