readme: ---
  license: gpl-3.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-tagalog-base-uncased-ner-v1
    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. -->
  
  # Description
  - The dataset consists of 148 Filipino storytelling books, 5,005 total sentences, 45,792 total tokens, and 5,646 unique tokens.
  - This NER model only supports the Filipino language and does not include proper nouns, verbs, adjectives, and adverbs as of the moment
  - The input must undergo preprocessing. Soon I will upload the code to GitHub for preprocessing the input
  - To replicate the preprocessed input use this example as a guide 
  - Input: "May umaapoy na bahay "
  - Preprocessed Input: "apoy bahay"
  
  # bert-tagalog-base-uncased-ner-v1
  
  This model is a fine-tuned version of [jcblaise/bert-tagalog-base-uncased](https://huggingface.co/jcblaise/bert-tagalog-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2824
  - Precision: 0.9091
  - Recall: 0.8988
  - F1: 0.9039
  - Accuracy: 0.9488
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 205  | 0.5311          | 0.6465    | 0.5458 | 0.5919 | 0.8387   |
  | No log        | 2.0   | 410  | 0.3052          | 0.7736    | 0.7811 | 0.7774 | 0.9110   |
  | 0.4693        | 3.0   | 615  | 0.2531          | 0.8493    | 0.8363 | 0.8427 | 0.9319   |
  | 0.4693        | 4.0   | 820  | 0.2384          | 0.8755    | 0.8715 | 0.8735 | 0.9402   |
  | 0.064         | 5.0   | 1025 | 0.2671          | 0.8909    | 0.8823 | 0.8866 | 0.9435   |
  | 0.064         | 6.0   | 1230 | 0.2527          | 0.8864    | 0.8920 | 0.8892 | 0.9459   |
  | 0.064         | 7.0   | 1435 | 0.2708          | 0.9088    | 0.9011 | 0.9049 | 0.9491   |
  | 0.0111        | 8.0   | 1640 | 0.2733          | 0.8992    | 0.8977 | 0.8984 | 0.9490   |
  | 0.0111        | 9.0   | 1845 | 0.2765          | 0.8991    | 0.8965 | 0.8978 | 0.9485   |
  | 0.0037        | 10.0  | 2050 | 0.2824          | 0.9091    | 0.8988 | 0.9039 | 0.9488   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  