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--- |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: aadhistii/indobert-ner-model |
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results: [] |
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datasets: |
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- id_nergrit_corpus |
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language: |
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- id |
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library_name: transformers |
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pipeline_tag: token-classification |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# aadhistii/indobert-ner-model |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on dataset [id_nergrit_corpus](https://huggingface.co/datasets/id_nergrit_corpus). |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.1471 |
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- Validation Loss: 0.1801 |
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- Train Precision: 0.8077 |
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- Train Recall: 0.8437 |
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- Train F1: 0.8253 |
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- Train Accuracy: 0.9471 |
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- Epoch: 2 |
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## Model description |
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Dataset Entities: |
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* 'CRD': Cardinal |
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* 'DAT': Date |
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* 'EVT': Event |
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* 'FAC': Facility |
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* 'GPE': Geopolitical Entity |
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* 'LAW': Law Entity (such as Undang-Undang) |
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* 'LOC': Location |
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* 'MON': Money |
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* 'NOR': Political Organization |
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* 'ORD': Ordinal |
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* 'ORG': Organization |
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* 'PER': Person |
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* 'PRC': Percent |
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* 'PRD': Product |
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* 'QTY': Quantity |
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* 'REG': Religion |
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* 'TIM': Time |
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* 'WOA': Work of Art |
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* 'LAN': Language |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2349, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| |
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| 0.5182 | 0.2042 | 0.7770 | 0.8146 | 0.7954 | 0.9395 | 0 | |
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| 0.1907 | 0.1810 | 0.8020 | 0.8344 | 0.8179 | 0.9469 | 1 | |
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| 0.1471 | 0.1801 | 0.8077 | 0.8437 | 0.8253 | 0.9471 | 2 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- TensorFlow 2.15.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
