test_trainer
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0633
- F1: 0.3413
- Precision: 0.2765
- Recall: 0.4456
- Accuracy: 0.7017
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.6981 | 1.0 | 285 | 0.6720 | 0.3035 | 0.2919 | 0.3161 | 0.7484 |
| 0.6726 | 2.0 | 570 | 0.6554 | 0.3547 | 0.2740 | 0.5026 | 0.6828 |
| 0.6402 | 3.0 | 855 | 0.6574 | 0.3609 | 0.2675 | 0.5544 | 0.6595 |
| 0.568 | 4.0 | 1140 | 0.7293 | 0.3620 | 0.3154 | 0.4249 | 0.7403 |
| 0.4926 | 5.0 | 1425 | 0.8515 | 0.3383 | 0.2883 | 0.4093 | 0.7224 |
| 0.4303 | 6.0 | 1710 | 0.9507 | 0.3538 | 0.2813 | 0.4767 | 0.6981 |
| 0.38 | 7.0 | 1995 | 1.0129 | 0.3366 | 0.2685 | 0.4508 | 0.6918 |
| 0.3437 | 8.0 | 2280 | 1.0633 | 0.3413 | 0.2765 | 0.4456 | 0.7017 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for IvAnastasia/sequence-ranker-for-dbpedia-ontology
Base model
google-bert/bert-base-cased