|
--- |
|
language: |
|
- en |
|
license: mit |
|
base_model: xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- tmnam20/VieGLUE |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: xlm-roberta-base-sst2-100 |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: tmnam20/VieGLUE/SST2 |
|
type: tmnam20/VieGLUE |
|
config: sst2 |
|
split: validation |
|
args: sst2 |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8944954128440367 |
|
--- |
|
|
|
<!-- 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-base-sst2-100 |
|
|
|
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: 0.3776 |
|
- Accuracy: 0.8945 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.4011 | 0.24 | 500 | 0.3866 | 0.8475 | |
|
| 0.313 | 0.48 | 1000 | 0.3352 | 0.8647 | |
|
| 0.2626 | 0.71 | 1500 | 0.4805 | 0.8349 | |
|
| 0.2597 | 0.95 | 2000 | 0.3691 | 0.8681 | |
|
| 0.2068 | 1.19 | 2500 | 0.3089 | 0.8991 | |
|
| 0.2347 | 1.43 | 3000 | 0.3957 | 0.8842 | |
|
| 0.2133 | 1.66 | 3500 | 0.3049 | 0.8991 | |
|
| 0.1986 | 1.9 | 4000 | 0.3184 | 0.8956 | |
|
| 0.1596 | 2.14 | 4500 | 0.3846 | 0.8853 | |
|
| 0.1457 | 2.38 | 5000 | 0.3667 | 0.8968 | |
|
| 0.1861 | 2.61 | 5500 | 0.3675 | 0.8922 | |
|
| 0.1401 | 2.85 | 6000 | 0.3853 | 0.8899 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.2.0.dev20231203+cu121 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
