metadata
license: mit
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-deberta-v3-small
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: autextification2023
type: autextification2023
config: detection_en
split: train
args: detection_en
metrics:
- name: Accuracy
type: accuracy
value: 0.6245419567607182
- name: F1
type: f1
value: 0.7308134379823322
- name: Precision
type: precision
value: 0.5776958621047713
- name: Recall
type: recall
value: 0.9943699731903485
ia-detection-deberta-v3-small
This model is a fine-tuned version of microsoft/deberta-v3-small on the autextification2023 dataset. It achieves the following results on the evaluation set:
- Loss: 2.0506
- Accuracy: 0.6245
- F1: 0.7308
- Precision: 0.5777
- Recall: 0.9944
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2303 | 1.0 | 3808 | 0.3607 | 0.8984 | 0.8934 | 0.9231 | 0.8655 |
| 0.1757 | 2.0 | 7616 | 0.5627 | 0.8606 | 0.8731 | 0.7903 | 0.9754 |
| 0.0372 | 3.0 | 11424 | 0.4746 | 0.8978 | 0.9014 | 0.8575 | 0.9502 |
| 0.1016 | 4.0 | 15232 | 0.6520 | 0.8910 | 0.8932 | 0.8620 | 0.9267 |
| 0.0871 | 5.0 | 19040 | 0.7452 | 0.8730 | 0.8797 | 0.8235 | 0.9441 |
| 0.0002 | 6.0 | 22848 | 0.7724 | 0.8942 | 0.8942 | 0.8802 | 0.9087 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3