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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- autextification2023 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: ia-detection-roberta-base |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: autextification2023 |
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type: autextification2023 |
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config: detection_en |
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split: train |
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args: detection_en |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.512550384756321 |
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- name: F1 |
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type: f1 |
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value: 0.6777299981830295 |
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- name: Precision |
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type: precision |
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value: 0.512550384756321 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ia-detection-roberta-base |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the autextification2023 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6928 |
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- Accuracy: 0.5126 |
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- F1: 0.6777 |
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- Precision: 0.5126 |
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- Recall: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.7021 | 1.0 | 3808 | 0.6950 | 0.5052 | 0.0 | 0.0 | 0.0 | |
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| 0.6936 | 2.0 | 7616 | 0.6937 | 0.4948 | 0.6621 | 0.4948 | 1.0 | |
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| 0.692 | 3.0 | 11424 | 0.6936 | 0.5052 | 0.0 | 0.0 | 0.0 | |
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| 0.6988 | 4.0 | 15232 | 0.6952 | 0.4948 | 0.6621 | 0.4948 | 1.0 | |
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| 0.6951 | 5.0 | 19040 | 0.6931 | 0.5052 | 0.0 | 0.0 | 0.0 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.13.3 |
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