readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bkk-ner-model
    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. -->
  
  # bkk-ner-model
  
  This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0518
  - Precision: 0.8850
  - Recall: 0.9615
  - F1: 0.9217
  - Accuracy: 0.9822
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 8    | 0.5592          | 0.3698    | 0.6827 | 0.4797 | 0.7818   |
  | No log        | 2.0   | 16   | 0.4491          | 0.4831    | 0.8269 | 0.6099 | 0.8062   |
  | No log        | 3.0   | 24   | 0.3738          | 0.6226    | 0.9519 | 0.7529 | 0.8399   |
  | No log        | 4.0   | 32   | 0.1781          | 0.6691    | 0.8942 | 0.7654 | 0.9401   |
  | No log        | 5.0   | 40   | 0.2201          | 0.8095    | 0.9808 | 0.8870 | 0.9204   |
  | No log        | 6.0   | 48   | 0.0936          | 0.8130    | 0.9615 | 0.8811 | 0.9710   |
  | No log        | 7.0   | 56   | 0.0692          | 0.8197    | 0.9615 | 0.8850 | 0.9757   |
  | No log        | 8.0   | 64   | 0.0712          | 0.8264    | 0.9615 | 0.8889 | 0.9710   |
  | No log        | 9.0   | 72   | 0.0575          | 0.8621    | 0.9615 | 0.9091 | 0.9803   |
  | No log        | 10.0  | 80   | 0.0625          | 0.8487    | 0.9712 | 0.9058 | 0.9766   |
  | No log        | 11.0  | 88   | 0.0580          | 0.8584    | 0.9327 | 0.8940 | 0.9766   |
  | No log        | 12.0  | 96   | 0.0551          | 0.8684    | 0.9519 | 0.9083 | 0.9813   |
  | No log        | 13.0  | 104  | 0.0554          | 0.8761    | 0.9519 | 0.9124 | 0.9803   |
  | No log        | 14.0  | 112  | 0.0535          | 0.8772    | 0.9615 | 0.9174 | 0.9813   |
  | No log        | 15.0  | 120  | 0.0518          | 0.8850    | 0.9615 | 0.9217 | 0.9822   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  