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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# checkpoints_30_9_microsoft_deberta_V1.0_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: 1.5746
- Map@3: 0.7625
- Accuracy: 0.655

## 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-07
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.6081        | 0.05  | 100  | 1.6083          | 0.7092 | 0.585    |
| 1.6107        | 0.11  | 200  | 1.6078          | 0.7375 | 0.625    |
| 1.6077        | 0.16  | 300  | 1.6070          | 0.7517 | 0.65     |
| 1.6097        | 0.21  | 400  | 1.6055          | 0.7542 | 0.645    |
| 1.6083        | 0.27  | 500  | 1.6030          | 0.7650 | 0.65     |
| 1.6006        | 0.32  | 600  | 1.5989          | 0.7733 | 0.665    |
| 1.5932        | 0.37  | 700  | 1.5927          | 0.7742 | 0.66     |
| 1.5881        | 0.43  | 800  | 1.5858          | 0.7742 | 0.665    |
| 1.578         | 0.48  | 900  | 1.5800          | 0.7708 | 0.66     |
| 1.5717        | 0.53  | 1000 | 1.5763          | 0.7658 | 0.655    |
| 1.5677        | 0.59  | 1100 | 1.5748          | 0.7625 | 0.655    |
| 1.5666        | 0.64  | 1200 | 1.5746          | 0.7625 | 0.655    |


### Framework versions

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