readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.6603139013452914
      - name: Recall
        type: recall
        value: 0.4682034976152623
      - name: F1
        type: f1
        value: 0.547906976744186
      - name: Accuracy
        type: accuracy
        value: 0.9355430668654662
  ---
  
  <!-- 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. -->
  
  # Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5062
  - Precision: 0.6603
  - Recall: 0.4682
  - F1: 0.5479
  - Accuracy: 0.9355
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 107  | 0.3396          | 0.6470    | 0.4269 | 0.5144 | 0.9330   |
  | No log        | 2.0   | 214  | 0.3475          | 0.5948    | 0.4539 | 0.5149 | 0.9335   |
  | No log        | 3.0   | 321  | 0.3793          | 0.6613    | 0.4253 | 0.5177 | 0.9332   |
  | No log        | 4.0   | 428  | 0.3598          | 0.6195    | 0.4944 | 0.5500 | 0.9354   |
  | 0.0409        | 5.0   | 535  | 0.3702          | 0.5802    | 0.4571 | 0.5113 | 0.9308   |
  | 0.0409        | 6.0   | 642  | 0.4192          | 0.6546    | 0.4459 | 0.5305 | 0.9344   |
  | 0.0409        | 7.0   | 749  | 0.4039          | 0.6360    | 0.4610 | 0.5346 | 0.9354   |
  | 0.0409        | 8.0   | 856  | 0.4104          | 0.6564    | 0.4587 | 0.5400 | 0.9353   |
  | 0.0409        | 9.0   | 963  | 0.3839          | 0.6283    | 0.4944 | 0.5534 | 0.9361   |
  | 0.0132        | 10.0  | 1070 | 0.4331          | 0.6197    | 0.4547 | 0.5245 | 0.9339   |
  | 0.0132        | 11.0  | 1177 | 0.4152          | 0.6196    | 0.4817 | 0.5420 | 0.9355   |
  | 0.0132        | 12.0  | 1284 | 0.4654          | 0.6923    | 0.4507 | 0.5460 | 0.9353   |
  | 0.0132        | 13.0  | 1391 | 0.4869          | 0.6739    | 0.4436 | 0.5350 | 0.9350   |
  | 0.0132        | 14.0  | 1498 | 0.4297          | 0.6424    | 0.4769 | 0.5474 | 0.9353   |
  | 0.0061        | 15.0  | 1605 | 0.4507          | 0.6272    | 0.4626 | 0.5325 | 0.9340   |
  | 0.0061        | 16.0  | 1712 | 0.4410          | 0.6066    | 0.4793 | 0.5355 | 0.9335   |
  | 0.0061        | 17.0  | 1819 | 0.4851          | 0.6639    | 0.4523 | 0.5381 | 0.9351   |
  | 0.0061        | 18.0  | 1926 | 0.4815          | 0.6553    | 0.4563 | 0.5380 | 0.9346   |
  | 0.0035        | 19.0  | 2033 | 0.5188          | 0.6780    | 0.4420 | 0.5351 | 0.9350   |
  | 0.0035        | 20.0  | 2140 | 0.4986          | 0.6770    | 0.4698 | 0.5547 | 0.9363   |
  | 0.0035        | 21.0  | 2247 | 0.4834          | 0.6552    | 0.4714 | 0.5483 | 0.9355   |
  | 0.0035        | 22.0  | 2354 | 0.5094          | 0.6784    | 0.4595 | 0.5479 | 0.9358   |
  | 0.0035        | 23.0  | 2461 | 0.4954          | 0.6583    | 0.4579 | 0.5401 | 0.9354   |
  | 0.0026        | 24.0  | 2568 | 0.5035          | 0.6667    | 0.4595 | 0.5440 | 0.9354   |
  | 0.0026        | 25.0  | 2675 | 0.5000          | 0.6599    | 0.4658 | 0.5461 | 0.9355   |
  | 0.0026        | 26.0  | 2782 | 0.4968          | 0.6697    | 0.4738 | 0.5549 | 0.9357   |
  | 0.0026        | 27.0  | 2889 | 0.4991          | 0.6545    | 0.4714 | 0.5481 | 0.9352   |
  | 0.0026        | 28.0  | 2996 | 0.4936          | 0.6508    | 0.4769 | 0.5505 | 0.9353   |
  | 0.0021        | 29.0  | 3103 | 0.5005          | 0.6535    | 0.4722 | 0.5482 | 0.9353   |
  | 0.0021        | 30.0  | 3210 | 0.5062          | 0.6603    | 0.4682 | 0.5479 | 0.9355   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  