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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_V4
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. -->
# DeBERTaV3_model_V4
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1557
- Accuracy: 0.9485
- F1: 0.7248
- Precision: 0.7778
- Recall: 0.6786
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 159 | 0.2879 | 0.9 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 318 | 0.2226 | 0.9189 | 0.3457 | 0.8936 | 0.2143 |
| No log | 3.0 | 477 | 0.1883 | 0.9347 | 0.5975 | 0.7787 | 0.4847 |
| 0.2618 | 4.0 | 636 | 0.1557 | 0.9485 | 0.7248 | 0.7778 | 0.6786 |
| 0.2618 | 5.0 | 795 | 0.1593 | 0.9480 | 0.7273 | 0.7640 | 0.6939 |
| 0.2618 | 6.0 | 954 | 0.1564 | 0.9505 | 0.7413 | 0.7765 | 0.7092 |
| 0.0829 | 7.0 | 1113 | 0.1636 | 0.9520 | 0.7552 | 0.7713 | 0.7398 |
| 0.0829 | 8.0 | 1272 | 0.1761 | 0.9485 | 0.7363 | 0.7540 | 0.7194 |
| 0.0829 | 9.0 | 1431 | 0.1686 | 0.9536 | 0.7599 | 0.7869 | 0.7347 |
| 0.0297 | 10.0 | 1590 | 0.1807 | 0.9526 | 0.7584 | 0.7725 | 0.7449 |
| 0.0297 | 11.0 | 1749 | 0.1765 | 0.9531 | 0.7629 | 0.7708 | 0.7551 |
| 0.0297 | 12.0 | 1908 | 0.1790 | 0.9536 | 0.7649 | 0.7749 | 0.7551 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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