readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: new_camembert_jb
    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. -->
  
  # new_camembert_jb
  
  This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0752
  - Overall Precision: 0.8202
  - Overall Recall: 0.8595
  - Overall F1: 0.8394
  - Overall Accuracy: 0.9814
  - Er F1: 0.8430
  - Oc F1: 0.8418
  - Umanprod F1: 0.6933
  
  ## 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: 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Er F1  | Oc F1  | Umanprod F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:-----------:|
  | 0.2682        | 1.0   | 613  | 0.0813          | 0.7550            | 0.8071         | 0.7802     | 0.9749           | 0.7920 | 0.7709 | 0.6667      |
  | 0.0717        | 2.0   | 1226 | 0.0706          | 0.8139            | 0.8411         | 0.8273     | 0.9808           | 0.8446 | 0.8126 | 0.6857      |
  | 0.0524        | 3.0   | 1839 | 0.0723          | 0.8215            | 0.8567         | 0.8387     | 0.9812           | 0.8462 | 0.8346 | 0.7368      |
  | 0.0372        | 4.0   | 2452 | 0.0752          | 0.8202            | 0.8595         | 0.8394     | 0.9814           | 0.8430 | 0.8418 | 0.6933      |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cpu
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  