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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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metrics: |
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- accuracy |
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model-index: |
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- name: text-classification |
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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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# text-classification |
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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: 0.9158 |
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- Accuracy: 0.7695 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 20 |
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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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| 1.0037 | 1.0 | 499 | 1.0119 | 0.7024 | |
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| 0.7645 | 2.0 | 998 | 0.9929 | 0.7275 | |
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| 0.6417 | 3.0 | 1497 | 0.9623 | 0.7335 | |
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| 0.8177 | 4.0 | 1996 | 0.9158 | 0.7695 | |
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| 0.4176 | 5.0 | 2495 | 1.2640 | 0.7635 | |
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| 0.7335 | 6.0 | 2994 | 1.2080 | 0.7615 | |
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| 0.3151 | 7.0 | 3493 | 1.3485 | 0.7575 | |
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| 0.7147 | 8.0 | 3992 | 1.2736 | 0.7605 | |
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| 0.0728 | 9.0 | 4491 | 1.4076 | 0.7565 | |
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| 0.2183 | 10.0 | 4990 | 1.5012 | 0.7505 | |
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| 0.2202 | 11.0 | 5489 | 1.5981 | 0.7405 | |
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| 0.2694 | 12.0 | 5988 | 1.5516 | 0.7415 | |
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| 0.0497 | 13.0 | 6487 | 1.6425 | 0.7485 | |
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| 0.2473 | 14.0 | 6986 | 1.7087 | 0.7475 | |
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| 0.1949 | 15.0 | 7485 | 1.6820 | 0.7535 | |
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| 0.1233 | 16.0 | 7984 | 1.7447 | 0.7405 | |
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| 0.0632 | 17.0 | 8483 | 1.7229 | 0.7475 | |
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| 0.1161 | 18.0 | 8982 | 1.7292 | 0.7545 | |
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| 0.0023 | 19.0 | 9481 | 1.7930 | 0.7465 | |
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| 0.0854 | 20.0 | 9980 | 1.8089 | 0.7495 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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