| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: Bert-RAdam-Large |
| results: [] |
| datasets: |
| - surrey-nlp/PLOD-CW-25 |
| - surrey-nlp/PLODv2-filtered |
| --- |
| |
| <!-- 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. --> |
|
|
| # Bert-RAdam-Large |
|
|
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a subset of the PLODv2-filtered dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2110 |
| - Precision: 0.7864 |
| - Recall: 0.8598 |
| - F1: 0.8215 |
| - Accuracy: 0.9403 |
|
|
| It achieves the following results on the test set: |
| - Loss: 0.1825 |
| - Precision: 0.8017 |
| - Recall: 0.8902 |
| - F1: 0.8436 |
| - Accuracy: 0.9500 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.2244 | 1.0 | 500 | 0.1675 | 0.7653 | 0.8651 | 0.8121 | 0.9355 | |
| | 0.1231 | 2.0 | 1000 | 0.1673 | 0.7433 | 0.9011 | 0.8146 | 0.9375 | |
| | 0.0923 | 3.0 | 1500 | 0.1698 | 0.7867 | 0.8539 | 0.8189 | 0.9391 | |
| | 0.0657 | 4.0 | 2000 | 0.1865 | 0.7857 | 0.8405 | 0.8122 | 0.9394 | |
| | 0.0431 | 5.0 | 2500 | 0.2110 | 0.7864 | 0.8598 | 0.8215 | 0.9403 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.51.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.1 |
| - Tokenizers 0.21.1 |
