---
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: 0.4601
- Accuracy: 0.8630
- Precision: 0.8456
- Recall: 0.8212
- F1: 0.8320

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0441        | 1.0   | 19   | 0.8982          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.8108        | 2.0   | 38   | 0.7417          | 0.5890   | 0.8630    | 0.3333 | 0.2471 |
| 0.7043        | 3.0   | 57   | 0.7361          | 0.6438   | 0.7410    | 0.4222 | 0.3842 |
| 0.5828        | 4.0   | 76   | 0.6559          | 0.7192   | 0.6862    | 0.5517 | 0.5662 |
| 0.5089        | 5.0   | 95   | 0.5497          | 0.8562   | 0.8516    | 0.7811 | 0.7923 |
| 0.3767        | 6.0   | 114  | 0.5299          | 0.8425   | 0.8558    | 0.7517 | 0.7753 |
| 0.3405        | 7.0   | 133  | 0.4696          | 0.8699   | 0.8707    | 0.8034 | 0.8248 |
| 0.2441        | 8.0   | 152  | 0.4845          | 0.8425   | 0.8207    | 0.8168 | 0.8186 |
| 0.2385        | 9.0   | 171  | 0.4611          | 0.8767   | 0.8706    | 0.8217 | 0.8400 |
| 0.1887        | 10.0  | 190  | 0.4601          | 0.8630   | 0.8456    | 0.8212 | 0.8320 |


### Framework versions

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