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--- |
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license: mit |
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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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model-index: |
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- name: fine-tuned-IndoNLI-Augmented-with-indobert-base-uncased |
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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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# fine-tuned-IndoNLI-Augmented-with-indobert-base-uncased |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9276 |
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- Accuracy: 0.8014 |
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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: 1e-05 |
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- train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 0.6316 | 1.0 | 6298 | 0.6317 | 0.7414 | |
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| 0.5501 | 2.0 | 12596 | 0.5378 | 0.7888 | |
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| 0.4978 | 3.0 | 18894 | 0.5407 | 0.7948 | |
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| 0.4193 | 4.0 | 25192 | 0.5259 | 0.8013 | |
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| 0.3766 | 5.0 | 31490 | 0.5447 | 0.8042 | |
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| 0.328 | 6.0 | 37788 | 0.5820 | 0.8023 | |
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| 0.2792 | 7.0 | 44086 | 0.6435 | 0.8012 | |
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| 0.261 | 8.0 | 50384 | 0.6578 | 0.8008 | |
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| 0.2071 | 9.0 | 56682 | 0.7064 | 0.8052 | |
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| 0.2004 | 10.0 | 62980 | 0.7446 | 0.8013 | |
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| 0.1657 | 11.0 | 69278 | 0.7735 | 0.8044 | |
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| 0.1729 | 12.0 | 75576 | 0.8078 | 0.8027 | |
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| 0.1399 | 13.0 | 81874 | 0.8660 | 0.8010 | |
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| 0.132 | 14.0 | 88172 | 0.8871 | 0.8006 | |
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| 0.1218 | 15.0 | 94470 | 0.9182 | 0.8001 | |
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| 0.1066 | 16.0 | 100768 | 0.9276 | 0.8014 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.2.0 |
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- Tokenizers 0.13.2 |
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