| | --- |
| | 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 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # ia-detection-tiny-random-gptj |
| |
|
| | This model is a fine-tuned version of [ydshieh/tiny-random-gptj-for-sequence-classification](https://huggingface.co/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 |
| | |
