---
base_model: VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_10_1_microsoft_deberta_V1.1_384
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# checkpoints_10_1_microsoft_deberta_V1.1_384

This model is a fine-tuned version of [VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384](https://huggingface.co./VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7675
- Map@3: 0.8483
- Accuracy: 0.755

## 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-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.5583        | 0.05  | 100  | 1.4269          | 0.7675 | 0.65     |
| 1.1541        | 0.11  | 200  | 1.0863          | 0.765  | 0.66     |
| 1.0126        | 0.16  | 300  | 0.9547          | 0.8133 | 0.72     |
| 0.9608        | 0.21  | 400  | 0.8926          | 0.8275 | 0.74     |
| 0.9224        | 0.27  | 500  | 0.8429          | 0.8400 | 0.76     |
| 0.8834        | 0.32  | 600  | 0.8297          | 0.8342 | 0.745    |
| 0.8585        | 0.37  | 700  | 0.7904          | 0.8483 | 0.76     |
| 0.8491        | 0.43  | 800  | 0.7726          | 0.8542 | 0.765    |
| 0.878         | 0.48  | 900  | 0.7693          | 0.8517 | 0.755    |
| 0.8529        | 0.53  | 1000 | 0.7703          | 0.8450 | 0.75     |
| 0.8485        | 0.59  | 1100 | 0.7682          | 0.8483 | 0.755    |
| 0.8353        | 0.64  | 1200 | 0.7675          | 0.8483 | 0.755    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3