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---
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-qnli-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tmnam20/VieGLUE/QNLI
type: tmnam20/VieGLUE
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8998718652754897
---
<!-- 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-qnli-1
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2782
- Accuracy: 0.8999
## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3768 | 0.15 | 500 | 0.3291 | 0.8596 |
| 0.3506 | 0.31 | 1000 | 0.2961 | 0.8752 |
| 0.3417 | 0.46 | 1500 | 0.2917 | 0.8808 |
| 0.3319 | 0.61 | 2000 | 0.2742 | 0.8871 |
| 0.3126 | 0.76 | 2500 | 0.2686 | 0.8913 |
| 0.3073 | 0.92 | 3000 | 0.2639 | 0.8916 |
| 0.2867 | 1.07 | 3500 | 0.2557 | 0.8958 |
| 0.2313 | 1.22 | 4000 | 0.2937 | 0.8880 |
| 0.2364 | 1.37 | 4500 | 0.2585 | 0.8971 |
| 0.2533 | 1.53 | 5000 | 0.2545 | 0.8938 |
| 0.2333 | 1.68 | 5500 | 0.2629 | 0.8955 |
| 0.225 | 1.83 | 6000 | 0.2532 | 0.9002 |
| 0.2313 | 1.99 | 6500 | 0.2520 | 0.8988 |
| 0.1793 | 2.14 | 7000 | 0.2819 | 0.8953 |
| 0.1639 | 2.29 | 7500 | 0.2809 | 0.8964 |
| 0.1645 | 2.44 | 8000 | 0.2778 | 0.8990 |
| 0.1753 | 2.6 | 8500 | 0.2802 | 0.8988 |
| 0.1859 | 2.75 | 9000 | 0.2775 | 0.9001 |
| 0.1809 | 2.9 | 9500 | 0.2767 | 0.8988 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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