| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: pangolin-large |
| 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. --> |
|
|
| # pangolin-large |
|
|
| This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0225 |
| - F1: 0.9904 |
| - Accuracy: 0.9937 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 64 |
| - 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 |
| - num_epochs: 2 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:--------:| |
| | 0.1519 | 0.1042 | 100 | 0.1354 | 0.9229 | 0.9534 | |
| | 0.068 | 0.2083 | 200 | 0.0553 | 0.9689 | 0.9797 | |
| | 0.0458 | 0.3125 | 300 | 0.0555 | 0.9758 | 0.9844 | |
| | 0.0389 | 0.4167 | 400 | 0.0442 | 0.9804 | 0.9874 | |
| | 0.04 | 0.5208 | 500 | 0.0323 | 0.9842 | 0.9897 | |
| | 0.0308 | 0.625 | 600 | 0.0357 | 0.9836 | 0.9894 | |
| | 0.0357 | 0.7292 | 700 | 0.0336 | 0.9861 | 0.9909 | |
| | 0.0306 | 0.8333 | 800 | 0.0299 | 0.9880 | 0.9921 | |
| | 0.0246 | 0.9375 | 900 | 0.0338 | 0.9846 | 0.9900 | |
| | 0.0195 | 1.0417 | 1000 | 0.0260 | 0.9881 | 0.9922 | |
| | 0.0124 | 1.1458 | 1100 | 0.0225 | 0.9887 | 0.9926 | |
| | 0.005 | 1.25 | 1200 | 0.0286 | 0.9874 | 0.9917 | |
| | 0.0075 | 1.3542 | 1300 | 0.0313 | 0.9897 | 0.9933 | |
| | 0.0065 | 1.4583 | 1400 | 0.0318 | 0.9892 | 0.9930 | |
| | 0.0093 | 1.5625 | 1500 | 0.0257 | 0.9903 | 0.9937 | |
| | 0.0099 | 1.6667 | 1600 | 0.0233 | 0.9889 | 0.9927 | |
| | 0.0054 | 1.7708 | 1700 | 0.0221 | 0.9905 | 0.9938 | |
| | 0.0077 | 1.875 | 1800 | 0.0222 | 0.9907 | 0.9939 | |
| | 0.0052 | 1.9792 | 1900 | 0.0225 | 0.9904 | 0.9937 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.3 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
