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---
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
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