readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: ijelid-indobertweet
    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-indobertweet
  
  This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) 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: 0.2804
  - Precision: 0.9323
  - Recall: 0.9394
  - F1: 0.9356
  - Accuracy: 0.9587
  
  It achieves the following results on the test set:
  - Overall Precision: 0.9326
  - Overall Recall: 0.9421
  - Overall F1: 0.9371
  - Overall Accuracy: 0.9643
  
  ## 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  | 0.1666          | 0.8968    | 0.9014 | 0.8982 | 0.9465   |
  | 0.257         | 2.0   | 772  | 0.1522          | 0.9062    | 0.9368 | 0.9206 | 0.9517   |
  | 0.1092        | 3.0   | 1158 | 0.1462          | 0.9233    | 0.9335 | 0.9280 | 0.9556   |
  | 0.0739        | 4.0   | 1544 | 0.1563          | 0.9312    | 0.9361 | 0.9336 | 0.9568   |
  | 0.0739        | 5.0   | 1930 | 0.1671          | 0.9224    | 0.9413 | 0.9312 | 0.9573   |
  | 0.0474        | 6.0   | 2316 | 0.1719          | 0.9303    | 0.9394 | 0.9346 | 0.9578   |
  | 0.0339        | 7.0   | 2702 | 0.1841          | 0.9249    | 0.9389 | 0.9314 | 0.9576   |
  | 0.0237        | 8.0   | 3088 | 0.2030          | 0.9224    | 0.9380 | 0.9297 | 0.9570   |
  | 0.0237        | 9.0   | 3474 | 0.2106          | 0.9289    | 0.9377 | 0.9331 | 0.9576   |
  | 0.0185        | 10.0  | 3860 | 0.2264          | 0.9277    | 0.9389 | 0.9330 | 0.9571   |
  | 0.0132        | 11.0  | 4246 | 0.2331          | 0.9336    | 0.9344 | 0.9339 | 0.9574   |
  | 0.0101        | 12.0  | 4632 | 0.2403          | 0.9353    | 0.9375 | 0.9363 | 0.9586   |
  | 0.0082        | 13.0  | 5018 | 0.2509          | 0.9311    | 0.9373 | 0.9340 | 0.9582   |
  | 0.0082        | 14.0  | 5404 | 0.2548          | 0.9344    | 0.9351 | 0.9346 | 0.9578   |
  | 0.0062        | 15.0  | 5790 | 0.2608          | 0.9359    | 0.9372 | 0.9365 | 0.9588   |
  | 0.005         | 16.0  | 6176 | 0.2667          | 0.9298    | 0.9407 | 0.9350 | 0.9587   |
  | 0.0045        | 17.0  | 6562 | 0.2741          | 0.9337    | 0.9408 | 0.9371 | 0.9592   |
  | 0.0045        | 18.0  | 6948 | 0.2793          | 0.9342    | 0.9371 | 0.9355 | 0.9589   |
  | 0.0035        | 19.0  | 7334 | 0.2806          | 0.9299    | 0.9391 | 0.9342 | 0.9588   |
  | 0.0034        | 20.0  | 7720 | 0.2804          | 0.9323    | 0.9394 | 0.9356 | 0.9587   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.7.1
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  