File size: 3,059 Bytes
740f719 54434c0 740f719 54434c0 740f719 1e3cc90 54434c0 e38517b 54434c0 04b27d7 54434c0 740f719 54434c0 740f719 54434c0 740f719 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilBERT-finetuned-resumes-sections
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. -->
# distilBERT-finetuned-resumes-sections
This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0450
- F1: 0.9585
- Roc Auc: 0.9774
- Accuracy: 0.9557
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.0518 | 1.0 | 1174 | 0.0368 | 0.9406 | 0.9635 | 0.9302 |
| 0.0251 | 2.0 | 2348 | 0.0346 | 0.9375 | 0.9653 | 0.9289 |
| 0.0136 | 3.0 | 3522 | 0.0343 | 0.9475 | 0.9707 | 0.9425 |
| 0.0096 | 4.0 | 4696 | 0.0326 | 0.9539 | 0.9737 | 0.9468 |
| 0.007 | 5.0 | 5870 | 0.0357 | 0.9521 | 0.9740 | 0.9480 |
| 0.007 | 6.0 | 7044 | 0.0389 | 0.9509 | 0.9725 | 0.9472 |
| 0.0034 | 7.0 | 8218 | 0.0403 | 0.9532 | 0.9746 | 0.9510 |
| 0.0033 | 8.0 | 9392 | 0.0422 | 0.9493 | 0.9722 | 0.9468 |
| 0.0024 | 9.0 | 10566 | 0.0425 | 0.9512 | 0.9733 | 0.9485 |
| 0.0023 | 10.0 | 11740 | 0.0431 | 0.9537 | 0.9743 | 0.9502 |
| 0.0019 | 11.0 | 12914 | 0.0457 | 0.9501 | 0.9719 | 0.9463 |
| 0.002 | 12.0 | 14088 | 0.0428 | 0.9560 | 0.9751 | 0.9536 |
| 0.0012 | 13.0 | 15262 | 0.0435 | 0.9569 | 0.9761 | 0.9553 |
| 0.001 | 14.0 | 16436 | 0.0464 | 0.9565 | 0.9759 | 0.9544 |
| 0.001 | 15.0 | 17610 | 0.0460 | 0.9574 | 0.9766 | 0.9549 |
| 0.0007 | 16.0 | 18784 | 0.0450 | 0.9585 | 0.9774 | 0.9557 |
| 0.0003 | 17.0 | 19958 | 0.0481 | 0.9572 | 0.9764 | 0.9553 |
| 0.0005 | 18.0 | 21132 | 0.0478 | 0.9576 | 0.9764 | 0.9557 |
| 0.0005 | 19.0 | 22306 | 0.0483 | 0.9574 | 0.9766 | 0.9553 |
| 0.0005 | 20.0 | 23480 | 0.0481 | 0.9576 | 0.9766 | 0.9557 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|