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
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-sst2-10
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/SST2
      type: tmnam20/VieGLUE
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8830275229357798
      name: Accuracy
---

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# xlm-roberta-base-sst2-10

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.8830

## 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3971        | 0.24  | 500  | 0.3420          | 0.8544   |
| 0.3266        | 0.48  | 1000 | 0.3271          | 0.8555   |
| 0.2831        | 0.71  | 1500 | 0.3069          | 0.8761   |
| 0.2752        | 0.95  | 2000 | 0.3220          | 0.8807   |
| 0.2286        | 1.19  | 2500 | 0.3367          | 0.8911   |
| 0.2294        | 1.43  | 3000 | 0.3194          | 0.8761   |
| 0.2055        | 1.66  | 3500 | 0.3312          | 0.8853   |
| 0.1902        | 1.9   | 4000 | 0.3307          | 0.8842   |
| 0.1645        | 2.14  | 4500 | 0.3608          | 0.8956   |
| 0.153         | 2.38  | 5000 | 0.3796          | 0.8888   |
| 0.1868        | 2.61  | 5500 | 0.3763          | 0.8842   |
| 0.1477        | 2.85  | 6000 | 0.3959          | 0.8830   |


### Framework versions

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