---
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-xsmall-beavertails-harmful-qa-classifier
  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. -->

# deberta-v3-xsmall-beavertails-harmful-qa-classifier

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: 0.3043
- Accuracy: 0.8716
- Macro F1: 0.8716
- Macro Precision: 0.8738
- Macro Recall: 0.8736
- Micro F1: 0.8716
- Micro Precision: 0.8716
- Micro Recall: 0.8716

## 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: 6e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Macro Precision | Macro Recall | Micro F1 | Micro Precision | Micro Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|
| 0.3726        | 1.0   | 2349 | 0.3409          | 0.8573   | 0.8573   | 0.8581          | 0.8587       | 0.8573   | 0.8573          | 0.8573       |
| 0.3299        | 2.0   | 4698 | 0.3186          | 0.8669   | 0.8669   | 0.8697          | 0.8692       | 0.8669   | 0.8669          | 0.8669       |
| 0.3181        | 3.0   | 7047 | 0.3092          | 0.8683   | 0.8682   | 0.8715          | 0.8707       | 0.8683   | 0.8683          | 0.8683       |
| 0.3064        | 4.0   | 9396 | 0.3043          | 0.8716   | 0.8716   | 0.8738          | 0.8736       | 0.8716   | 0.8716          | 0.8716       |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1