---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Noisy10per-deberta-v3-small-Label_B-768-epochs-9
  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. -->

# Noisy10per-deberta-v3-small-Label_B-768-epochs-9

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1297
- Accuracy: 0.9854
- F1: 0.9854
- Precision: 0.9856
- Recall: 0.9854

## 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 9

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0764        | 0.9995 | 1066 | 0.1081          | 0.9730   | 0.9731 | 0.9733    | 0.9730 |
| 0.083         | 1.9993 | 2132 | 0.1059          | 0.9763   | 0.9762 | 0.9770    | 0.9763 |
| 0.0474        | 2.9991 | 3198 | 0.0775          | 0.9833   | 0.9833 | 0.9834    | 0.9833 |
| 0.0028        | 3.9998 | 4265 | 0.1005          | 0.9818   | 0.9818 | 0.9821    | 0.9818 |
| 0.0025        | 4.9995 | 5331 | 0.1092          | 0.9841   | 0.9842 | 0.9843    | 0.9841 |
| 0.0287        | 5.9993 | 6397 | 0.1633          | 0.9820   | 0.9821 | 0.9827    | 0.9820 |
| 0.0085        | 6.9991 | 7463 | 0.1640          | 0.9814   | 0.9814 | 0.9818    | 0.9814 |
| 0.001         | 7.9998 | 8530 | 0.1297          | 0.9854   | 0.9854 | 0.9856    | 0.9854 |
| 0.0           | 8.9977 | 9594 | 0.1368          | 0.9851   | 0.9852 | 0.9853    | 0.9851 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3