| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: skt/A.X-Encoder-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: aha_sentence_classification |
| 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. --> |
|
|
| # aha_sentence_classification |
|
|
| This model is a fine-tuned version of [skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8454 |
| - Accuracy: 0.6900 |
| - F1 Micro: 0.6900 |
| - F1 Macro: 0.6503 |
| - Precision Macro: 0.6078 |
| - Recall Macro: 0.7221 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - 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: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 25 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Macro | Recall Macro | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:| |
| | 0.9702 | 0.5949 | 1000 | 1.1520 | 0.5590 | 0.5590 | 0.5444 | 0.5142 | 0.6791 | |
| | 0.7293 | 1.1898 | 2000 | 1.0469 | 0.5992 | 0.5992 | 0.5966 | 0.5599 | 0.7238 | |
| | 0.7779 | 1.7847 | 3000 | 0.9977 | 0.6278 | 0.6278 | 0.5964 | 0.5646 | 0.7274 | |
| | 0.5545 | 2.3795 | 4000 | 0.9847 | 0.6290 | 0.6290 | 0.6208 | 0.5849 | 0.7236 | |
| | 0.5692 | 2.9744 | 5000 | 0.8454 | 0.6900 | 0.6900 | 0.6503 | 0.6078 | 0.7221 | |
| | 0.3962 | 3.5693 | 6000 | 1.0074 | 0.6488 | 0.6488 | 0.6316 | 0.6093 | 0.7081 | |
| | 0.1624 | 4.1642 | 7000 | 1.1059 | 0.6732 | 0.6732 | 0.6533 | 0.6322 | 0.6930 | |
| | 0.1816 | 4.7591 | 8000 | 1.1277 | 0.6872 | 0.6872 | 0.6513 | 0.6429 | 0.6690 | |
| | 0.0934 | 5.3540 | 9000 | 1.4084 | 0.6882 | 0.6882 | 0.6468 | 0.6380 | 0.6649 | |
| | 0.0882 | 5.9488 | 10000 | 1.4941 | 0.6918 | 0.6918 | 0.6450 | 0.6428 | 0.6606 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.56.1 |
| - Pytorch 2.7.0+cu126 |
| - Tokenizers 0.22.0 |
| |
