transformers safetensors xlm-roberta text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-qqp-100
  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: 0.8946326984912194
      name: Accuracy
    - type: f1
      value: 0.858697094334616
      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-base-qqp-100

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2785
- Accuracy: 0.8946
- F1: 0.8587
- Combined Score: 0.8767

## 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

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3304        | 0.44  | 5000  | 0.3286          | 0.8591   | 0.8046 | 0.8318         |
| 0.2856        | 0.88  | 10000 | 0.2910          | 0.8744   | 0.8273 | 0.8509         |
| 0.2795        | 1.32  | 15000 | 0.2818          | 0.8808   | 0.8413 | 0.8610         |
| 0.2492        | 1.76  | 20000 | 0.2750          | 0.8863   | 0.8484 | 0.8674         |
| 0.2093        | 2.2   | 25000 | 0.2791          | 0.8919   | 0.8542 | 0.8730         |
| 0.2022        | 2.64  | 30000 | 0.2926          | 0.8928   | 0.8566 | 0.8747         |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
