| --- |
| library_name: transformers |
| base_model: jay0911/fine-tuned-aemodel |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: ade_biobert_output |
| results: [] |
| datasets: |
| - ade-benchmark-corpus/ade_corpus_v2 |
| --- |
| |
| <!-- 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. --> |
|
|
| # ade_biobert_output |
|
|
| This model is a fine-tuned version of [jay0911/fine-tuned-aemodel](https://huggingface.co/jay0911/fine-tuned-aemodel) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3619 |
| - Precision: 0.9353 |
| - Recall: 0.9358 |
| - F1: 0.9355 |
| - Recall Positive: 0.8686 |
| - Recall Negative: 0.9613 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - 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 |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Recall Positive | Recall Negative | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:---------------:|:---------------:| |
| | 0.1921 | 0.2126 | 500 | 0.2565 | 0.9347 | 0.9332 | 0.9337 | 0.9147 | 0.9412 | |
| | 0.1893 | 0.4252 | 1000 | 0.2461 | 0.9409 | 0.9392 | 0.9397 | 0.9289 | 0.9436 | |
| | 0.2207 | 0.6378 | 1500 | 0.2583 | 0.9421 | 0.9418 | 0.9419 | 0.9104 | 0.9551 | |
| | 0.1706 | 0.8503 | 2000 | 0.3926 | 0.9216 | 0.9205 | 0.9183 | 0.7866 | 0.9776 | |
| | 0.1219 | 1.0629 | 2500 | 0.3413 | 0.9373 | 0.9354 | 0.9359 | 0.9246 | 0.9400 | |
| | 0.1097 | 1.2755 | 3000 | 0.3073 | 0.9453 | 0.9456 | 0.9453 | 0.8919 | 0.9685 | |
| | 0.1645 | 1.4881 | 3500 | 0.2700 | 0.9433 | 0.9430 | 0.9431 | 0.9118 | 0.9563 | |
| | 0.2348 | 1.7007 | 4000 | 0.2449 | 0.9452 | 0.9456 | 0.9452 | 0.8876 | 0.9703 | |
| | 0.2718 | 1.9133 | 4500 | 0.2304 | 0.9425 | 0.9426 | 0.9425 | 0.8990 | 0.9612 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.55.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
