---
license: mit
base_model: VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_27_9_microsoft_deberta_21_9
  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_27_9_microsoft_deberta_21_9

This model is a fine-tuned version of [VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9](https://huggingface.co/VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6632
- Map@3: 0.8608
- Accuracy: 0.775
- MAX_INPUT = 256
## 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: 1e-05
- 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: 0.2
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6308        | 0.05  | 100  | 0.6775          | 0.8842 | 0.815    |
| 0.3472        | 0.11  | 200  | 0.7255          | 0.8767 | 0.805    |
| 0.2267        | 0.16  | 300  | 0.7786          | 0.8608 | 0.785    |
| 0.143         | 0.21  | 400  | 0.8580          | 0.8333 | 0.735    |
| 0.0723        | 0.27  | 500  | 0.9517          | 0.8358 | 0.735    |
| 0.3952        | 0.32  | 600  | 0.6632          | 0.8608 | 0.775    |


### Framework versions

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