| | --- |
| | license: mit |
| | base_model: LazarusNLP/NusaBERT-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - indonlu |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: NusaBERT-base-POSP |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: indonlu |
| | type: indonlu |
| | config: posp |
| | split: validation |
| | args: posp |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.9577443609022557 |
| | - name: Recall |
| | type: recall |
| | value: 0.9577443609022557 |
| | - name: F1 |
| | type: f1 |
| | value: 0.9577443609022557 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9577443609022557 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # NusaBERT-base-POSP |
| |
|
| | This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co/LazarusNLP/NusaBERT-base) on the indonlu dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1472 |
| | - Precision: 0.9577 |
| | - Recall: 0.9577 |
| | - F1: 0.9577 |
| | - Accuracy: 0.9577 |
| |
|
| | ## 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: 64 |
| | - 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 | 420 | 0.2680 | 0.9203 | 0.9203 | 0.9203 | 0.9203 | |
| | | 0.6283 | 2.0 | 840 | 0.2017 | 0.9379 | 0.9379 | 0.9379 | 0.9379 | |
| | | 0.218 | 3.0 | 1260 | 0.1785 | 0.9449 | 0.9449 | 0.9449 | 0.9449 | |
| | | 0.1612 | 4.0 | 1680 | 0.1692 | 0.9490 | 0.9490 | 0.9490 | 0.9490 | |
| | | 0.1393 | 5.0 | 2100 | 0.1577 | 0.9511 | 0.9511 | 0.9511 | 0.9511 | |
| | | 0.1119 | 6.0 | 2520 | 0.1503 | 0.9539 | 0.9539 | 0.9539 | 0.9539 | |
| | | 0.1119 | 7.0 | 2940 | 0.1499 | 0.9549 | 0.9549 | 0.9549 | 0.9549 | |
| | | 0.0943 | 8.0 | 3360 | 0.1542 | 0.9547 | 0.9547 | 0.9547 | 0.9547 | |
| | | 0.0824 | 9.0 | 3780 | 0.1517 | 0.9558 | 0.9558 | 0.9558 | 0.9558 | |
| | | 0.0785 | 10.0 | 4200 | 0.1519 | 0.9557 | 0.9557 | 0.9557 | 0.9557 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.37.2 |
| | - Pytorch 2.2.0+cu118 |
| | - Datasets 2.17.1 |
| | - Tokenizers 0.15.1 |
| |
|
