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---
language:
- en
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-vsfc-100
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tmnam20/VieGLUE/VSFC
type: tmnam20/VieGLUE
config: vsfc
split: validation
args: vsfc
metrics:
- name: Accuracy
type: accuracy
value: 0.9500947567909034
---
<!-- 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. -->
# xlm-roberta-large-vsfc-100
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/VSFC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2097
- Accuracy: 0.9501
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2424 | 1.4 | 500 | 0.2267 | 0.9444 |
| 0.1406 | 2.79 | 1000 | 0.2293 | 0.9495 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|