readme: ---
  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: 0.0369
  - 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
  