| --- |
| library_name: transformers |
| base_model: allenai/scibert_scivocab_uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Fake-Citation-Detector-V2-scibert |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # Fake-Citation-Detector-V2-scibert |
|
|
| This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8244 |
| - Accuracy: 0.6205 |
| - Macro F1: 0.6028 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 51 |
| - num_epochs: 6 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
| | 0.7055 | 1.0 | 86 | 0.6790 | 0.5723 | 0.5499 | |
| | 0.6908 | 2.0 | 172 | 0.6773 | 0.5723 | 0.5195 | |
| | 0.6122 | 3.0 | 258 | 0.6426 | 0.6325 | 0.6133 | |
| | 0.5333 | 4.0 | 344 | 0.6915 | 0.6205 | 0.5983 | |
| | 0.3916 | 5.0 | 430 | 0.7555 | 0.6205 | 0.6006 | |
| | 0.2788 | 6.0 | 516 | 0.8244 | 0.6205 | 0.6028 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 5.17.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.23.2 |
|
|
