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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_trainer |
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datasets: |
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- id_nergrit_corpus |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: my-nergrit-model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: id_nergrit_corpus |
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type: id_nergrit_corpus |
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config: ner |
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split: validation |
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args: ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8166034264688973 |
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- name: Recall |
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type: recall |
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value: 0.8423674534456174 |
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- name: F1 |
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type: f1 |
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value: 0.8292853806632415 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9476005188067445 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# my-nergrit-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 the id_nergrit_corpus dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1792 |
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- Precision: 0.8166 |
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- Recall: 0.8424 |
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- F1: 0.8293 |
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- Accuracy: 0.9476 |
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## Model description |
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More information needed |
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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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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4887 | 1.0 | 784 | 0.1891 | 0.7908 | 0.8305 | 0.8102 | 0.9427 | |
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| 0.1624 | 2.0 | 1568 | 0.1792 | 0.8166 | 0.8424 | 0.8293 | 0.9476 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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