| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: deepset/gbert-large |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: gbert_synset_classifier_amdi_small |
| | 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. --> |
| |
|
| | # gbert_synset_classifier_amdi_small |
| |
|
| | This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6371 |
| | - Accuracy: 0.8443 |
| | - F1: 0.8414 |
| | - Precision: 0.8523 |
| | - Recall: 0.8443 |
| | - F1 Macro: 0.7742 |
| | - Precision Macro: 0.7539 |
| | - Recall Macro: 0.8118 |
| | - F1 Micro: 0.8443 |
| | - Precision Micro: 0.8443 |
| | - Recall Micro: 0.8443 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 128 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 50 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:| |
| | | 3.1817 | 0.6483 | 100 | 1.7424 | 0.6200 | 0.5455 | 0.5576 | 0.6200 | 0.2894 | 0.3465 | 0.2954 | 0.6200 | 0.6200 | 0.6200 | |
| | | 1.0711 | 1.2966 | 200 | 0.7171 | 0.8140 | 0.7971 | 0.7992 | 0.8140 | 0.5958 | 0.5870 | 0.6238 | 0.8140 | 0.8140 | 0.8140 | |
| | | 0.649 | 1.9449 | 300 | 0.6003 | 0.8275 | 0.8184 | 0.8282 | 0.8275 | 0.6797 | 0.6812 | 0.7138 | 0.8275 | 0.8275 | 0.8275 | |
| | | 0.4903 | 2.5932 | 400 | 0.5668 | 0.8336 | 0.8268 | 0.8375 | 0.8336 | 0.6942 | 0.6869 | 0.7271 | 0.8336 | 0.8336 | 0.8336 | |
| | | 0.4095 | 3.2415 | 500 | 0.5511 | 0.8387 | 0.8351 | 0.8398 | 0.8387 | 0.7224 | 0.7198 | 0.7414 | 0.8387 | 0.8387 | 0.8387 | |
| | | 0.3586 | 3.8898 | 600 | 0.5313 | 0.8415 | 0.8360 | 0.8452 | 0.8415 | 0.7188 | 0.7075 | 0.7481 | 0.8415 | 0.8415 | 0.8415 | |
| | | 0.2813 | 4.5381 | 700 | 0.5442 | 0.8485 | 0.8451 | 0.8502 | 0.8485 | 0.7290 | 0.7355 | 0.7419 | 0.8485 | 0.8485 | 0.8485 | |
| | | 0.2543 | 5.1864 | 800 | 0.5736 | 0.8494 | 0.8461 | 0.8515 | 0.8494 | 0.7812 | 0.7708 | 0.8047 | 0.8494 | 0.8494 | 0.8494 | |
| | | 0.1928 | 5.8347 | 900 | 0.5791 | 0.8448 | 0.8419 | 0.8484 | 0.8448 | 0.7646 | 0.7536 | 0.7899 | 0.8448 | 0.8448 | 0.8448 | |
| | | 0.1645 | 6.4830 | 1000 | 0.6371 | 0.8443 | 0.8414 | 0.8523 | 0.8443 | 0.7742 | 0.7539 | 0.8118 | 0.8443 | 0.8443 | 0.8443 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.45.2 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.20.3 |
| |
|
