metadata
license: mit
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-roberta-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: 0.512550384756321
- name: F1
type: f1
value: 0.6777299981830295
- name: Precision
type: precision
value: 0.512550384756321
- name: Recall
type: recall
value: 1
ia-detection-roberta-base
This model is a fine-tuned version of roberta-base on the autextification2023 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6928
- Accuracy: 0.5126
- F1: 0.6777
- Precision: 0.5126
- Recall: 1.0
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 |
|---|---|---|---|---|---|---|---|
| 0.7021 | 1.0 | 3808 | 0.6950 | 0.5052 | 0.0 | 0.0 | 0.0 |
| 0.6936 | 2.0 | 7616 | 0.6937 | 0.4948 | 0.6621 | 0.4948 | 1.0 |
| 0.692 | 3.0 | 11424 | 0.6936 | 0.5052 | 0.0 | 0.0 | 0.0 |
| 0.6988 | 4.0 | 15232 | 0.6952 | 0.4948 | 0.6621 | 0.4948 | 1.0 |
| 0.6951 | 5.0 | 19040 | 0.6931 | 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