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distilBERT-finetuned-resumes-sections
This model is a fine-tuned version of Geotrend/distilbert-base-en-fr-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0369
- F1: 0.9652
- Roc Auc: 0.9808
- Accuracy: 0.9621
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.0509 | 1.0 | 1173 | 0.0331 | 0.9439 | 0.9659 | 0.9356 |
| 0.024 | 2.0 | 2346 | 0.0274 | 0.9550 | 0.9750 | 0.9493 |
| 0.0148 | 3.0 | 3519 | 0.0290 | 0.9493 | 0.9712 | 0.9446 |
| 0.0089 | 4.0 | 4692 | 0.0324 | 0.9492 | 0.9714 | 0.9442 |
| 0.0071 | 5.0 | 5865 | 0.0317 | 0.9540 | 0.9732 | 0.9476 |
| 0.0064 | 6.0 | 7038 | 0.0324 | 0.9527 | 0.9742 | 0.9484 |
| 0.0036 | 7.0 | 8211 | 0.0320 | 0.9574 | 0.9766 | 0.9540 |
| 0.0042 | 8.0 | 9384 | 0.0367 | 0.9528 | 0.9732 | 0.9493 |
| 0.0052 | 9.0 | 10557 | 0.0342 | 0.9563 | 0.9757 | 0.9531 |
| 0.0027 | 10.0 | 11730 | 0.0294 | 0.9629 | 0.9800 | 0.9595 |
| 0.0017 | 11.0 | 12903 | 0.0355 | 0.9605 | 0.9778 | 0.9582 |
| 0.0022 | 12.0 | 14076 | 0.0338 | 0.9627 | 0.9792 | 0.9591 |
| 0.0012 | 13.0 | 15249 | 0.0358 | 0.9609 | 0.9780 | 0.9591 |
| 0.0011 | 14.0 | 16422 | 0.0360 | 0.9618 | 0.9791 | 0.9604 |
| 0.0009 | 15.0 | 17595 | 0.0358 | 0.9648 | 0.9807 | 0.9625 |
| 0.0007 | 16.0 | 18768 | 0.0373 | 0.9627 | 0.9794 | 0.9595 |
| 0.0006 | 17.0 | 19941 | 0.0397 | 0.9597 | 0.9774 | 0.9574 |
| 0.0008 | 18.0 | 21114 | 0.0369 | 0.9652 | 0.9808 | 0.9621 |
| 0.0007 | 19.0 | 22287 | 0.0377 | 0.9646 | 0.9801 | 0.9621 |
| 0.0005 | 20.0 | 23460 | 0.0381 | 0.9639 | 0.9797 | 0.9616 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
