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fg-bert-sustainability-15-1.5e-05-0.02-64
This model is a fine-tuned version of Raccourci/fairguest-bert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0711
- F1: 0.9215
- Roc Auc: 0.9565
- Accuracy: 0.8846
On the validation dataset :
- The accuracy with hamming loss is 0.7800788954635107
- The acccuracy as a metric is 0.8326530612244898
- The following is the global precision score: 0.8695652173913043
- The following is the global recall score: 0.8536585365853658
- The following is the global f1-score: 0.8615384615384616
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: 1.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| No log | 1.0 | 55 | 0.3273 | 0.0 | 0.5 | 0.0956 |
| No log | 2.0 | 110 | 0.2344 | 0.3710 | 0.6182 | 0.2328 |
| No log | 3.0 | 165 | 0.1464 | 0.8973 | 0.9300 | 0.8441 |
| No log | 4.0 | 220 | 0.1143 | 0.9066 | 0.9405 | 0.8617 |
| No log | 5.0 | 275 | 0.0998 | 0.9091 | 0.9455 | 0.8659 |
| No log | 6.0 | 330 | 0.0901 | 0.9142 | 0.9490 | 0.8732 |
| No log | 7.0 | 385 | 0.0854 | 0.9121 | 0.9534 | 0.8721 |
| No log | 8.0 | 440 | 0.0778 | 0.9185 | 0.9538 | 0.8825 |
| No log | 9.0 | 495 | 0.0775 | 0.9119 | 0.9473 | 0.8763 |
| 0.1683 | 10.0 | 550 | 0.0742 | 0.9200 | 0.9535 | 0.8815 |
| 0.1683 | 11.0 | 605 | 0.0730 | 0.9196 | 0.9544 | 0.8805 |
| 0.1683 | 12.0 | 660 | 0.0716 | 0.9213 | 0.9556 | 0.8825 |
| 0.1683 | 13.0 | 715 | 0.0722 | 0.9218 | 0.9585 | 0.8836 |
| 0.1683 | 14.0 | 770 | 0.0712 | 0.9222 | 0.9580 | 0.8836 |
| 0.1683 | 15.0 | 825 | 0.0711 | 0.9215 | 0.9565 | 0.8846 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
