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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: gacha_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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# gacha_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: 0.5789 |
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- Accuracy: 0.8089 |
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- F1: 0.8065 |
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- Precision: 0.8115 |
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- Recall: 0.8052 |
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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 | 0.23 | 50 | 0.5084 | 0.7739 | 0.7737 | 0.7826 | 0.7790 | |
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| No log | 0.47 | 100 | 0.4663 | 0.7972 | 0.7967 | 0.7964 | 0.7971 | |
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| No log | 0.7 | 150 | 0.4834 | 0.8112 | 0.8094 | 0.8125 | 0.8082 | |
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| No log | 0.93 | 200 | 0.4445 | 0.8135 | 0.8104 | 0.8194 | 0.8087 | |
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| No log | 1.16 | 250 | 0.6506 | 0.7879 | 0.7786 | 0.8149 | 0.7781 | |
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| No log | 1.4 | 300 | 0.5314 | 0.7692 | 0.7687 | 0.7810 | 0.7752 | |
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| No log | 1.63 | 350 | 0.5149 | 0.8065 | 0.8021 | 0.8167 | 0.8003 | |
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| No log | 1.86 | 400 | 0.4735 | 0.8298 | 0.8289 | 0.8296 | 0.8284 | |
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| No log | 2.09 | 450 | 0.5093 | 0.8275 | 0.8262 | 0.8280 | 0.8253 | |
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| 0.3338 | 2.33 | 500 | 0.5789 | 0.8089 | 0.8065 | 0.8115 | 0.8052 | |
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| 0.3338 | 2.56 | 550 | 0.6539 | 0.8065 | 0.8059 | 0.8057 | 0.8062 | |
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| 0.3338 | 2.79 | 600 | 0.6995 | 0.8042 | 0.8018 | 0.8068 | 0.8005 | |
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| 0.3338 | 3.02 | 650 | 0.8298 | 0.8182 | 0.8168 | 0.8186 | 0.8160 | |
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| 0.3338 | 3.26 | 700 | 0.7829 | 0.8089 | 0.8077 | 0.8085 | 0.8072 | |
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| 0.3338 | 3.49 | 750 | 0.7700 | 0.8205 | 0.8195 | 0.8202 | 0.8191 | |
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| 0.3338 | 3.72 | 800 | 0.9060 | 0.8089 | 0.8057 | 0.8145 | 0.8040 | |
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| 0.3338 | 3.95 | 850 | 0.9478 | 0.8112 | 0.8072 | 0.8205 | 0.8053 | |
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| 0.3338 | 4.19 | 900 | 0.9171 | 0.8089 | 0.8067 | 0.8109 | 0.8054 | |
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| 0.3338 | 4.42 | 950 | 0.9512 | 0.8065 | 0.8043 | 0.8088 | 0.8030 | |
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| 0.079 | 4.65 | 1000 | 0.9579 | 0.8065 | 0.8047 | 0.8078 | 0.8035 | |
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| 0.079 | 4.88 | 1050 | 0.9471 | 0.8089 | 0.8073 | 0.8095 | 0.8063 | |
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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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