| | --- |
| | 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: 1.0 |
| | --- |
| | |
| | <!-- 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-roberta-base |
| |
|
| | This model is a fine-tuned version of [roberta-base](https://huggingface.co/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 |
| | |
