readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - accuracy
  - f1
  model-index:
  - name: camembert-keyword-discriminator
    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. -->
  
  # camembert-keyword-discriminator
  
  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2180
  - Precision: 0.6646
  - Recall: 0.7047
  - Accuracy: 0.9344
  - F1: 0.6841
  - Ent/precision: 0.7185
  - Ent/accuracy: 0.8157
  - Ent/f1: 0.7640
  - Con/precision: 0.5324
  - Con/accuracy: 0.4860
  - Con/f1: 0.5082
  
  ## 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: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
  | 0.2016        | 1.0   | 1875  | 0.1910          | 0.5947    | 0.7125 | 0.9243   | 0.6483 | 0.6372        | 0.8809       | 0.7395 | 0.4560        | 0.3806       | 0.4149 |
  | 0.1454        | 2.0   | 3750  | 0.1632          | 0.6381    | 0.7056 | 0.9324   | 0.6701 | 0.6887        | 0.8291       | 0.7524 | 0.5064        | 0.4621       | 0.4833 |
  | 0.1211        | 3.0   | 5625  | 0.1702          | 0.6703    | 0.6678 | 0.9343   | 0.6690 | 0.7120        | 0.7988       | 0.7529 | 0.5471        | 0.4094       | 0.4684 |
  | 0.1021        | 4.0   | 7500  | 0.1745          | 0.6777    | 0.6708 | 0.9351   | 0.6742 | 0.7206        | 0.7956       | 0.7562 | 0.5557        | 0.4248       | 0.4815 |
  | 0.0886        | 5.0   | 9375  | 0.1913          | 0.6540    | 0.7184 | 0.9340   | 0.6847 | 0.7022        | 0.8396       | 0.7648 | 0.5288        | 0.4795       | 0.5030 |
  | 0.0781        | 6.0   | 11250 | 0.2021          | 0.6605    | 0.7132 | 0.9344   | 0.6858 | 0.7139        | 0.8258       | 0.7658 | 0.5293        | 0.4913       | 0.5096 |
  | 0.0686        | 7.0   | 13125 | 0.2127          | 0.6539    | 0.7132 | 0.9337   | 0.6822 | 0.7170        | 0.8172       | 0.7638 | 0.5112        | 0.5083       | 0.5098 |
  | 0.0667        | 8.0   | 15000 | 0.2180          | 0.6646    | 0.7047 | 0.9344   | 0.6841 | 0.7185        | 0.8157       | 0.7640 | 0.5324        | 0.4860       | 0.5082 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  