readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: ijelid-bert-base-multilingual
    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. -->
  
  # ijelid-bert-base-multilingual
  
  This model is a fine-tuned version of [BERT multilingual base model (cased)](https://huggingface.co/bert-base-multilingual-cased) on the Indonesian-Javanese-English code-mixed Twitter dataset.
  
  Label ID and its corresponding name:
  
  | Label ID        | Label Name                                | 
  |:---------------:|:------------------------------------------:
  | LABEL_0        | English (EN)                              | 
  | LABEL_1        | Indonesian (ID)                           |
  | LABEL_2        | Javanese (JV)                             |
  | LABEL_3        | Mixed Indonesian-English (MIX-ID-EN)      |
  | LABEL_4        | Mixed Indonesian-Javanese (MIX-ID-JV)     |
  | LABEL_5        | Mixed Javanese-English (MIX-JV-EN)        |
  | LABEL_6        | Other (O)                                 | 
  
  It achieves the following results on the evaluation set:
  - Loss: 0.3553
  - Precision: 0.9189
  - Recall: 0.9188
  - F1: 0.9187
  - Accuracy: 0.9451
  
  It achieves the following results on the test set:
  - Overall Precision: 0.9249
  - Overall Recall: 0.9251
  - Overall F1: 0.925
  - Overall Accuracy: 0.951
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 386  | 0.2340          | 0.8956    | 0.8507 | 0.8715 | 0.9239   |
  | 0.3379        | 2.0   | 772  | 0.2101          | 0.9057    | 0.8904 | 0.8962 | 0.9342   |
  | 0.1603        | 3.0   | 1158 | 0.2231          | 0.9252    | 0.8896 | 0.9065 | 0.9367   |
  | 0.1079        | 4.0   | 1544 | 0.2013          | 0.9272    | 0.8902 | 0.9070 | 0.9420   |
  | 0.1079        | 5.0   | 1930 | 0.2179          | 0.9031    | 0.9179 | 0.9103 | 0.9425   |
  | 0.0701        | 6.0   | 2316 | 0.2330          | 0.9075    | 0.9165 | 0.9114 | 0.9435   |
  | 0.051         | 7.0   | 2702 | 0.2433          | 0.9117    | 0.9190 | 0.9150 | 0.9432   |
  | 0.0384        | 8.0   | 3088 | 0.2545          | 0.9001    | 0.9167 | 0.9078 | 0.9439   |
  | 0.0384        | 9.0   | 3474 | 0.2629          | 0.9164    | 0.9159 | 0.9158 | 0.9444   |
  | 0.0293        | 10.0  | 3860 | 0.2881          | 0.9263    | 0.9096 | 0.9178 | 0.9427   |
  | 0.022         | 11.0  | 4246 | 0.2882          | 0.9167    | 0.9222 | 0.9191 | 0.9450   |
  | 0.0171        | 12.0  | 4632 | 0.3028          | 0.9203    | 0.9152 | 0.9177 | 0.9447   |
  | 0.0143        | 13.0  | 5018 | 0.3236          | 0.9155    | 0.9167 | 0.9158 | 0.9440   |
  | 0.0143        | 14.0  | 5404 | 0.3301          | 0.9237    | 0.9163 | 0.9199 | 0.9444   |
  | 0.0109        | 15.0  | 5790 | 0.3290          | 0.9187    | 0.9154 | 0.9169 | 0.9442   |
  | 0.0092        | 16.0  | 6176 | 0.3308          | 0.9213    | 0.9178 | 0.9194 | 0.9448   |
  | 0.0075        | 17.0  | 6562 | 0.3501          | 0.9273    | 0.9142 | 0.9206 | 0.9445   |
  | 0.0075        | 18.0  | 6948 | 0.3520          | 0.9200    | 0.9184 | 0.9190 | 0.9447   |
  | 0.0062        | 19.0  | 7334 | 0.3524          | 0.9238    | 0.9183 | 0.9210 | 0.9458   |
  | 0.0051        | 20.0  | 7720 | 0.3553          | 0.9189    | 0.9188 | 0.9187 | 0.9451   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.7.1
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  