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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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