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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_V3
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_V3
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: 0.1015
- Accuracy: 0.9693
- F1: 0.8766
- Precision: 0.8803
- Recall: 0.8729
## 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: 3e-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 | 100 | 0.3499 | 0.875 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 200 | 0.2517 | 0.9068 | 0.4211 | 0.9412 | 0.2712 |
| No log | 3.0 | 300 | 0.1835 | 0.9396 | 0.7077 | 0.8961 | 0.5847 |
| No log | 4.0 | 400 | 0.1338 | 0.9587 | 0.8219 | 0.8911 | 0.7627 |
| 0.2507 | 5.0 | 500 | 0.1043 | 0.9640 | 0.8522 | 0.875 | 0.8305 |
| 0.2507 | 6.0 | 600 | 0.1076 | 0.9629 | 0.8472 | 0.8739 | 0.8220 |
| 0.2507 | 7.0 | 700 | 0.1061 | 0.9619 | 0.8475 | 0.8475 | 0.8475 |
| 0.2507 | 8.0 | 800 | 0.1015 | 0.9693 | 0.8766 | 0.8803 | 0.8729 |
| 0.2507 | 9.0 | 900 | 0.1099 | 0.9650 | 0.8596 | 0.8632 | 0.8559 |
| 0.0434 | 10.0 | 1000 | 0.1101 | 0.9661 | 0.8632 | 0.8707 | 0.8559 |
| 0.0434 | 11.0 | 1100 | 0.1054 | 0.9693 | 0.8766 | 0.8803 | 0.8729 |
| 0.0434 | 12.0 | 1200 | 0.1066 | 0.9682 | 0.8729 | 0.8729 | 0.8729 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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