---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mdeberta-domain_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_fold1

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5289
- Accuracy: 0.8562
- Precision: 0.8293
- Recall: 0.7956
- F1: 0.8011

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0269        | 1.0   | 19   | 0.8985          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.8123        | 2.0   | 38   | 0.7571          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.7079        | 3.0   | 57   | 0.7243          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.6259        | 4.0   | 76   | 0.7249          | 0.7808   | 0.8511    | 0.6444 | 0.5909 |
| 0.5818        | 5.0   | 95   | 0.7192          | 0.7671   | 0.6242    | 0.6222 | 0.5773 |
| 0.5081        | 6.0   | 114  | 0.6343          | 0.7877   | 0.7129    | 0.6628 | 0.6687 |
| 0.4902        | 7.0   | 133  | 0.5858          | 0.8219   | 0.7819    | 0.7256 | 0.7107 |
| 0.3946        | 8.0   | 152  | 0.5584          | 0.8288   | 0.7766    | 0.7656 | 0.7603 |
| 0.3876        | 9.0   | 171  | 0.5457          | 0.8562   | 0.8396    | 0.7811 | 0.7859 |
| 0.3239        | 10.0  | 190  | 0.5289          | 0.8562   | 0.8293    | 0.7956 | 0.8011 |


### Framework versions

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