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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: fitur_model |
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results: [] |
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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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# fitur_model |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3853 |
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- Accuracy: 0.7857 |
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- F1: 0.6904 |
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- Precision: 0.7095 |
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- Recall: 0.6786 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 0.54 | 50 | 0.6229 | 0.7582 | 0.6537 | 0.6671 | 0.6454 | |
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| No log | 1.09 | 100 | 0.6750 | 0.7747 | 0.6557 | 0.6912 | 0.6414 | |
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| No log | 1.63 | 150 | 0.5936 | 0.7802 | 0.7242 | 0.7137 | 0.7421 | |
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| No log | 2.17 | 200 | 0.7087 | 0.7912 | 0.7312 | 0.7232 | 0.7419 | |
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| No log | 2.72 | 250 | 0.9279 | 0.7802 | 0.6796 | 0.7007 | 0.6675 | |
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| No log | 3.26 | 300 | 1.0408 | 0.7747 | 0.6853 | 0.6940 | 0.6788 | |
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| No log | 3.8 | 350 | 1.2506 | 0.7857 | 0.7007 | 0.7100 | 0.6935 | |
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| No log | 4.35 | 400 | 1.4011 | 0.7802 | 0.6796 | 0.7007 | 0.6675 | |
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| No log | 4.89 | 450 | 1.3770 | 0.7857 | 0.6904 | 0.7095 | 0.6786 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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