readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - favsbot
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: camembert-base-NER-favsbot
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: favsbot
        type: favsbot
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.6
      - name: Recall
        type: recall
        value: 0.012145748987854251
      - name: F1
        type: f1
        value: 0.023809523809523808
      - name: Accuracy
        type: accuracy
        value: 0.42078364565587734
  ---
  
  <!-- 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-base-NER-favsbot
  
  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the favsbot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7433
  - Precision: 0.6
  - Recall: 0.0121
  - F1: 0.0238
  - Accuracy: 0.4208
  
  ## 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: 1.5e-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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 4    | 2.2915          | 0.1364    | 0.1215 | 0.1285 | 0.3475   |
  | No log        | 2.0   | 8    | 2.2230          | 0.2909    | 0.0648 | 0.1060 | 0.4395   |
  | No log        | 3.0   | 12   | 2.1573          | 0.4545    | 0.0202 | 0.0388 | 0.4225   |
  | No log        | 4.0   | 16   | 2.0961          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 5.0   | 20   | 2.0426          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 6.0   | 24   | 1.9965          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 7.0   | 28   | 1.9575          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 8.0   | 32   | 1.9233          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 9.0   | 36   | 1.8933          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 10.0  | 40   | 1.8674          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 11.0  | 44   | 1.8441          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 12.0  | 48   | 1.8240          | 0.0       | 0.0    | 0.0    | 0.4123   |
  | No log        | 13.0  | 52   | 1.8060          | 1.0       | 0.0040 | 0.0081 | 0.4140   |
  | No log        | 14.0  | 56   | 1.7899          | 1.0       | 0.0040 | 0.0081 | 0.4140   |
  | No log        | 15.0  | 60   | 1.7762          | 1.0       | 0.0040 | 0.0081 | 0.4140   |
  | No log        | 16.0  | 64   | 1.7647          | 0.5       | 0.0040 | 0.0080 | 0.4157   |
  | No log        | 17.0  | 68   | 1.7556          | 0.5       | 0.0040 | 0.0080 | 0.4157   |
  | No log        | 18.0  | 72   | 1.7490          | 0.6667    | 0.0081 | 0.016  | 0.4174   |
  | No log        | 19.0  | 76   | 1.7449          | 0.75      | 0.0121 | 0.0239 | 0.4191   |
  | No log        | 20.0  | 80   | 1.7433          | 0.6       | 0.0121 | 0.0238 | 0.4208   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  