---
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_fold4
  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_fold4

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: 0.3705
- Accuracy: 0.8552
- Precision: 0.8128
- Recall: 0.8276
- F1: 0.8194

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0349        | 1.0   | 19   | 0.9208          | 0.5931   | 0.8644    | 0.3333 | 0.2482 |
| 0.88          | 2.0   | 38   | 0.7011          | 0.5931   | 0.8644    | 0.3333 | 0.2482 |
| 0.68          | 3.0   | 57   | 0.6370          | 0.5931   | 0.8644    | 0.3333 | 0.2482 |
| 0.6179        | 4.0   | 76   | 0.5360          | 0.8      | 0.6860    | 0.6971 | 0.6459 |
| 0.4709        | 5.0   | 95   | 0.3949          | 0.8483   | 0.7967    | 0.7860 | 0.7852 |
| 0.3643        | 6.0   | 114  | 0.3526          | 0.8690   | 0.8279    | 0.8209 | 0.8236 |
| 0.2901        | 7.0   | 133  | 0.3713          | 0.8690   | 0.8269    | 0.8277 | 0.8242 |
| 0.2414        | 8.0   | 152  | 0.3506          | 0.8759   | 0.8394    | 0.8392 | 0.8374 |
| 0.1941        | 9.0   | 171  | 0.3766          | 0.8621   | 0.8206    | 0.8391 | 0.8290 |
| 0.1977        | 10.0  | 190  | 0.3705          | 0.8552   | 0.8128    | 0.8276 | 0.8194 |


### Framework versions

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