| | --- |
| | library_name: transformers |
| | base_model: microsoft/mpnet-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: g-patentsberta-e2e |
| | 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. --> |
| |
|
| | # g-patentsberta-e2e |
| |
|
| | This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4002 |
| | - Accuracy: 0.8255 |
| | - Precision: 0.2789 |
| | - Recall: 0.8247 |
| | - F1: 0.4168 |
| |
|
| | ## 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: 64 |
| | - 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: 0.06 |
| | - num_epochs: 1.0 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.4534 | 0.1866 | 2000 | 0.4317 | 0.7988 | 0.7985 | 0.7899 | 0.7942 | |
| | | 0.4212 | 0.3731 | 4000 | 0.4359 | 0.8043 | 0.8573 | 0.7218 | 0.7837 | |
| | | 0.4095 | 0.5597 | 6000 | 0.4160 | 0.8157 | 0.8004 | 0.8325 | 0.8161 | |
| | | 0.3992 | 0.7463 | 8000 | 0.4039 | 0.8210 | 0.8255 | 0.8061 | 0.8157 | |
| | | 0.3828 | 0.9328 | 10000 | 0.3913 | 0.8241 | 0.8179 | 0.8260 | 0.8219 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 5.0.0 |
| | - Pytorch 2.9.0+cu128 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.2 |
| |
|
