---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Noisy-deberta-v3-xsmall-Label_B-768-epochs-5
  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. -->

# Noisy-deberta-v3-xsmall-Label_B-768-epochs-5

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0850
- Accuracy: 0.9839
- F1: 0.9839
- Precision: 0.9841
- Recall: 0.9839

## 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: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0801        | 0.9995 | 1066 | 0.1455          | 0.9542   | 0.9535 | 0.9565    | 0.9542 |
| 0.0581        | 1.9993 | 2132 | 0.0792          | 0.9805   | 0.9805 | 0.9807    | 0.9805 |
| 0.0543        | 2.9991 | 3198 | 0.3059          | 0.9434   | 0.9423 | 0.9495    | 0.9434 |
| 0.0003        | 3.9998 | 4265 | 0.0850          | 0.9839   | 0.9839 | 0.9841    | 0.9839 |
| 0.0006        | 4.9986 | 5330 | 0.1618          | 0.9737   | 0.9737 | 0.9747    | 0.9737 |


### Framework versions

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