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tmnam20
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mdeberta-v3-base-qqp-1

Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results
Inference Endpoints
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mdeberta-v3-base-qqp-1
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---
language:
- en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
- f1
model-index:
- name: mdeberta-v3-base-qqp-1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/QQP
      type: tmnam20/VieGLUE
      config: qqp
      split: validation
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8996784565916399
    - name: F1
      type: f1
      value: 0.865810891285648
---

<!-- 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-qqp-1

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2774
- Accuracy: 0.8997
- F1: 0.8658
- Combined Score: 0.8827

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2888        | 0.44  | 5000  | 0.2928          | 0.8740   | 0.8314 | 0.8527         |
| 0.2968        | 0.88  | 10000 | 0.2770          | 0.8793   | 0.8325 | 0.8559         |
| 0.2365        | 1.32  | 15000 | 0.2894          | 0.8871   | 0.8507 | 0.8689         |
| 0.2257        | 1.76  | 20000 | 0.2664          | 0.8941   | 0.8572 | 0.8757         |
| 0.1939        | 2.2   | 25000 | 0.2777          | 0.8970   | 0.8617 | 0.8793         |
| 0.2001        | 2.64  | 30000 | 0.2762          | 0.8987   | 0.8643 | 0.8815         |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0