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---
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
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