transformers safetensors deberta-v2 multiple-choice generated_from_trainer base_model:microsoft/deberta-v3-base license:mit endpoints_compatible region:us
---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base-zalo-v1
  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. -->

# deberta-v3-base-zalo-v1

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3848
- Map@3: 0.6788

## 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: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Map@3  |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3875        | 0.02  | 25   | 1.3864          | 0.4245 |
| 1.388         | 0.04  | 50   | 1.3863          | 0.4449 |
| 1.388         | 0.06  | 75   | 1.3863          | 0.4549 |
| 1.3862        | 0.08  | 100  | 1.3863          | 0.4674 |
| 1.3867        | 0.1   | 125  | 1.3862          | 0.5061 |
| 1.3859        | 0.13  | 150  | 1.3861          | 0.5347 |
| 1.3871        | 0.15  | 175  | 1.3860          | 0.5556 |
| 1.3851        | 0.17  | 200  | 1.3860          | 0.5764 |
| 1.3865        | 0.19  | 225  | 1.3859          | 0.5751 |
| 1.3853        | 0.21  | 250  | 1.3859          | 0.5864 |
| 1.3863        | 0.23  | 275  | 1.3858          | 0.6128 |
| 1.3858        | 0.25  | 300  | 1.3857          | 0.6176 |
| 1.3875        | 0.27  | 325  | 1.3857          | 0.6350 |
| 1.3863        | 0.29  | 350  | 1.3856          | 0.6432 |
| 1.3858        | 0.31  | 375  | 1.3856          | 0.6502 |
| 1.3861        | 0.34  | 400  | 1.3855          | 0.6589 |
| 1.3867        | 0.36  | 425  | 1.3855          | 0.6593 |
| 1.387         | 0.38  | 450  | 1.3854          | 0.6693 |
| 1.3853        | 0.4   | 475  | 1.3854          | 0.6615 |
| 1.3876        | 0.42  | 500  | 1.3853          | 0.6753 |
| 1.386         | 0.44  | 525  | 1.3853          | 0.6788 |
| 1.3844        | 0.46  | 550  | 1.3852          | 0.6784 |
| 1.386         | 0.48  | 575  | 1.3852          | 0.6845 |
| 1.3861        | 0.5   | 600  | 1.3852          | 0.6753 |
| 1.3872        | 0.52  | 625  | 1.3851          | 0.6788 |
| 1.3864        | 0.55  | 650  | 1.3851          | 0.6827 |
| 1.3857        | 0.57  | 675  | 1.3850          | 0.6918 |
| 1.385         | 0.59  | 700  | 1.3850          | 0.6823 |
| 1.3851        | 0.61  | 725  | 1.3850          | 0.6714 |
| 1.3862        | 0.63  | 750  | 1.3850          | 0.6784 |
| 1.3851        | 0.65  | 775  | 1.3849          | 0.6836 |
| 1.3839        | 0.67  | 800  | 1.3849          | 0.6775 |
| 1.3859        | 0.69  | 825  | 1.3849          | 0.6823 |
| 1.3847        | 0.71  | 850  | 1.3848          | 0.6910 |
| 1.3848        | 0.73  | 875  | 1.3848          | 0.6853 |
| 1.3859        | 0.75  | 900  | 1.3848          | 0.6827 |
| 1.3865        | 0.78  | 925  | 1.3848          | 0.6866 |
| 1.3857        | 0.8   | 950  | 1.3848          | 0.6810 |
| 1.3861        | 0.82  | 975  | 1.3848          | 0.6845 |
| 1.3864        | 0.84  | 1000 | 1.3848          | 0.6840 |
| 1.3851        | 0.86  | 1025 | 1.3848          | 0.6806 |
| 1.3846        | 0.88  | 1050 | 1.3848          | 0.6823 |
| 1.3866        | 0.9   | 1075 | 1.3848          | 0.6845 |
| 1.3857        | 0.92  | 1100 | 1.3847          | 0.6814 |
| 1.3858        | 0.94  | 1125 | 1.3847          | 0.6892 |
| 1.3856        | 0.96  | 1150 | 1.3847          | 0.6810 |
| 1.3853        | 0.99  | 1175 | 1.3848          | 0.6788 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
