metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-bart-base
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: 0.7699248809087578
- name: F1
type: f1
value: 0.7727252160535721
- name: Precision
type: precision
value: 0.7826047108422692
- name: Recall
type: recall
value: 0.7630920464700626
ia-detection-bart-base
This model is a fine-tuned version of facebook/bart-base on the autextification2023 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6968
- Accuracy: 0.7699
- F1: 0.7727
- Precision: 0.7826
- Recall: 0.7631
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 |
|---|---|---|---|---|---|---|---|
| 0.5243 | 1.0 | 3808 | 0.4726 | 0.7861 | 0.7309 | 0.9685 | 0.5869 |
| 0.627 | 2.0 | 7616 | 0.6362 | 0.6151 | 0.7120 | 0.5653 | 0.9618 |
| 0.6919 | 3.0 | 11424 | 0.7017 | 0.5052 | 0.0 | 0.0 | 0.0 |
| 0.7018 | 4.0 | 15232 | 0.6932 | 0.5052 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3