metadata
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-tiny-random-gptj
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: 0.633198973983144
- name: F1
type: f1
value: 0.7005683517798384
- name: Precision
type: precision
value: 0.6022888003086023
- name: Recall
type: recall
value: 0.8371760500446828
ia-detection-tiny-random-gptj
This model is a fine-tuned version of ydshieh/tiny-random-gptj-for-sequence-classification on the autextification2023 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7313
- Accuracy: 0.6332
- F1: 0.7006
- Precision: 0.6023
- Recall: 0.8372
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 |
|---|---|---|---|---|---|---|---|
| 0.6038 | 1.0 | 3808 | 0.5362 | 0.7309 | 0.7270 | 0.7294 | 0.7246 |
| 0.5303 | 2.0 | 7616 | 0.5109 | 0.7465 | 0.7358 | 0.7592 | 0.7139 |
| 0.4588 | 3.0 | 11424 | 0.5258 | 0.7424 | 0.7568 | 0.7097 | 0.8106 |
| 0.4459 | 4.0 | 15232 | 0.5137 | 0.7477 | 0.7428 | 0.7491 | 0.7366 |
| 0.3586 | 5.0 | 19040 | 0.5062 | 0.7572 | 0.7452 | 0.7745 | 0.7180 |
| 0.4072 | 6.0 | 22848 | 0.5264 | 0.7539 | 0.7565 | 0.7407 | 0.7730 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3