transformers safetensors deberta-v2 text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:microsoft/mdeberta-v3-base base_model:finetune:microsoft/mdeberta-v3-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mdeberta-v3-base
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base-sst2-100
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/SST2
      type: tmnam20/VieGLUE
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8944954128440367
      name: Accuracy
---

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# mdeberta-v3-base-sst2-100

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: 0.3880
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3414        | 0.24  | 500  | 0.3477          | 0.8681   |
| 0.2858        | 0.48  | 1000 | 0.3121          | 0.8911   |
| 0.2358        | 0.71  | 1500 | 0.3466          | 0.8807   |
| 0.2413        | 0.95  | 2000 | 0.3225          | 0.8819   |
| 0.1722        | 1.19  | 2500 | 0.3268          | 0.8933   |
| 0.1926        | 1.43  | 3000 | 0.3712          | 0.8899   |
| 0.1766        | 1.66  | 3500 | 0.3130          | 0.9014   |
| 0.1706        | 1.9   | 4000 | 0.3517          | 0.8899   |
| 0.1308        | 2.14  | 4500 | 0.3970          | 0.9014   |
| 0.1315        | 2.38  | 5000 | 0.3525          | 0.8991   |
| 0.1504        | 2.61  | 5500 | 0.3728          | 0.8968   |
| 0.1178        | 2.85  | 6000 | 0.3987          | 0.8922   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
