---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mdeberta-domain_EN_fold2
  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. -->

# mdeberta-domain_EN_fold2

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: 0.4394
- Accuracy: 0.8207
- Precision: 0.7590
- Recall: 0.7755
- F1: 0.7645

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0408        | 1.0   | 19   | 0.8182          | 0.5931   | 0.8644    | 0.3333 | 0.2482 |
| 0.7383        | 2.0   | 38   | 0.6728          | 0.5931   | 0.8644    | 0.3333 | 0.2482 |
| 0.6381        | 3.0   | 57   | 0.6066          | 0.7862   | 0.8283    | 0.6696 | 0.6001 |
| 0.5638        | 4.0   | 76   | 0.5272          | 0.7931   | 0.7363    | 0.6804 | 0.6422 |
| 0.4701        | 5.0   | 95   | 0.4757          | 0.7931   | 0.7324    | 0.6811 | 0.6138 |
| 0.4087        | 6.0   | 114  | 0.4957          | 0.8      | 0.7199    | 0.6911 | 0.6670 |
| 0.3438        | 7.0   | 133  | 0.5317          | 0.7862   | 0.7128    | 0.6673 | 0.6757 |
| 0.281         | 8.0   | 152  | 0.4456          | 0.8069   | 0.7325    | 0.7532 | 0.7376 |
| 0.2428        | 9.0   | 171  | 0.4442          | 0.8069   | 0.7445    | 0.7677 | 0.7540 |
| 0.2166        | 10.0  | 190  | 0.4394          | 0.8207   | 0.7590    | 0.7755 | 0.7645 |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1