readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-small-finetuned-xglue-ner-longer50
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        config: wnut_17
        split: train
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.6182136602451839
      - name: Recall
        type: recall
        value: 0.4222488038277512
      - name: F1
        type: f1
        value: 0.5017768301350392
      - name: Accuracy
        type: accuracy
        value: 0.9252207821997935
  ---
  
  <!-- 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. -->
  
  # bert-small-finetuned-xglue-ner-longer50
  
  This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer20](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner-longer20) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7236
  - Precision: 0.6182
  - Recall: 0.4222
  - F1: 0.5018
  - Accuracy: 0.9252
  
  ## 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: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 425   | 0.5693          | 0.5232    | 0.4581 | 0.4885 | 0.9268   |
  | 0.0032        | 2.0   | 850   | 0.6191          | 0.5281    | 0.4498 | 0.4858 | 0.9260   |
  | 0.0035        | 3.0   | 1275  | 0.7045          | 0.6011    | 0.4055 | 0.4843 | 0.9241   |
  | 0.0056        | 4.0   | 1700  | 0.6715          | 0.5571    | 0.4438 | 0.4940 | 0.9261   |
  | 0.004         | 5.0   | 2125  | 0.6537          | 0.5645    | 0.4294 | 0.4878 | 0.9256   |
  | 0.0063        | 6.0   | 2550  | 0.6646          | 0.5659    | 0.4211 | 0.4829 | 0.9255   |
  | 0.0063        | 7.0   | 2975  | 0.6269          | 0.5306    | 0.4354 | 0.4783 | 0.9238   |
  | 0.003         | 8.0   | 3400  | 0.7235          | 0.5921    | 0.3959 | 0.4746 | 0.9238   |
  | 0.0051        | 9.0   | 3825  | 0.6334          | 0.5330    | 0.4450 | 0.4850 | 0.9237   |
  | 0.0047        | 10.0  | 4250  | 0.6408          | 0.5893    | 0.4462 | 0.5078 | 0.9271   |
  | 0.004         | 11.0  | 4675  | 0.6721          | 0.5840    | 0.4282 | 0.4941 | 0.9255   |
  | 0.0051        | 12.0  | 5100  | 0.6853          | 0.5795    | 0.4318 | 0.4949 | 0.9258   |
  | 0.0038        | 13.0  | 5525  | 0.6870          | 0.5789    | 0.4211 | 0.4875 | 0.9249   |
  | 0.0038        | 14.0  | 5950  | 0.6931          | 0.6032    | 0.4091 | 0.4875 | 0.9241   |
  | 0.0033        | 15.0  | 6375  | 0.6502          | 0.5965    | 0.4510 | 0.5136 | 0.9266   |
  | 0.0032        | 16.0  | 6800  | 0.6941          | 0.6126    | 0.4426 | 0.5139 | 0.9267   |
  | 0.0042        | 17.0  | 7225  | 0.6603          | 0.5856    | 0.4462 | 0.5064 | 0.9266   |
  | 0.0016        | 18.0  | 7650  | 0.6870          | 0.6121    | 0.4474 | 0.5169 | 0.9273   |
  | 0.0028        | 19.0  | 8075  | 0.6922          | 0.5906    | 0.4366 | 0.5021 | 0.9250   |
  | 0.0023        | 20.0  | 8500  | 0.7096          | 0.6089    | 0.4246 | 0.5004 | 0.9250   |
  | 0.0023        | 21.0  | 8925  | 0.6763          | 0.5772    | 0.4426 | 0.5010 | 0.9261   |
  | 0.0025        | 22.0  | 9350  | 0.6880          | 0.5696    | 0.4258 | 0.4873 | 0.9241   |
  | 0.0018        | 23.0  | 9775  | 0.6759          | 0.5836    | 0.4426 | 0.5034 | 0.9259   |
  | 0.0017        | 24.0  | 10200 | 0.7044          | 0.6198    | 0.4270 | 0.5057 | 0.9262   |
  | 0.0018        | 25.0  | 10625 | 0.6948          | 0.6040    | 0.4306 | 0.5028 | 0.9245   |
  | 0.0018        | 26.0  | 11050 | 0.6930          | 0.5948    | 0.4354 | 0.5028 | 0.9255   |
  | 0.0018        | 27.0  | 11475 | 0.7077          | 0.6048    | 0.4246 | 0.4989 | 0.9250   |
  | 0.0023        | 28.0  | 11900 | 0.7127          | 0.6103    | 0.4270 | 0.5025 | 0.9252   |
  | 0.0013        | 29.0  | 12325 | 0.7253          | 0.6243    | 0.4234 | 0.5046 | 0.9254   |
  | 0.0015        | 30.0  | 12750 | 0.7236          | 0.6182    | 0.4222 | 0.5018 | 0.9252   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  