|
--- |
|
language: |
|
- en |
|
license: mit |
|
base_model: microsoft/mdeberta-v3-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- tmnam20/VieGLUE |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: mdeberta-v3-base-sst2-10 |
|
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: 0.8979357798165137 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# mdeberta-v3-base-sst2-10 |
|
|
|
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/SST2 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3852 |
|
- Accuracy: 0.8979 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.3449 | 0.24 | 500 | 0.3368 | 0.8567 | |
|
| 0.2987 | 0.48 | 1000 | 0.3037 | 0.8716 | |
|
| 0.2492 | 0.71 | 1500 | 0.3347 | 0.8842 | |
|
| 0.24 | 0.95 | 2000 | 0.2953 | 0.8830 | |
|
| 0.195 | 1.19 | 2500 | 0.3445 | 0.8842 | |
|
| 0.1934 | 1.43 | 3000 | 0.3217 | 0.8876 | |
|
| 0.1697 | 1.66 | 3500 | 0.3627 | 0.8876 | |
|
| 0.1757 | 1.9 | 4000 | 0.3366 | 0.8899 | |
|
| 0.1328 | 2.14 | 4500 | 0.4266 | 0.8876 | |
|
| 0.1475 | 2.38 | 5000 | 0.3737 | 0.8933 | |
|
| 0.1574 | 2.61 | 5500 | 0.3888 | 0.8911 | |
|
| 0.1548 | 2.85 | 6000 | 0.4063 | 0.8865 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
