---
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_fold1
  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_fold1

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: 0.5362
- Accuracy: 0.8288
- Precision: 0.7887
- Recall: 0.7656
- F1: 0.7752

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0312        | 1.0   | 19   | 0.8773          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.7596        | 2.0   | 38   | 0.7653          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.7097        | 3.0   | 57   | 0.7352          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.6673        | 4.0   | 76   | 0.7382          | 0.7466   | 0.6626    | 0.5889 | 0.5519 |
| 0.6028        | 5.0   | 95   | 0.7362          | 0.7740   | 0.6837    | 0.6406 | 0.5884 |
| 0.4939        | 6.0   | 114  | 0.6345          | 0.7466   | 0.6145    | 0.6034 | 0.5967 |
| 0.3969        | 7.0   | 133  | 0.5446          | 0.8014   | 0.7220    | 0.7140 | 0.6938 |
| 0.3291        | 8.0   | 152  | 0.5437          | 0.8082   | 0.7452    | 0.7468 | 0.7410 |
| 0.2975        | 9.0   | 171  | 0.5534          | 0.7945   | 0.7437    | 0.7101 | 0.7235 |
| 0.2573        | 10.0  | 190  | 0.5362          | 0.8288   | 0.7887    | 0.7656 | 0.7752 |


### Framework versions

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