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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-base-0 |
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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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# sentiment-base-0 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3540 |
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- Accuracy: 0.8546 |
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- Precision: 0.8233 |
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- Recall: 0.8297 |
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- F1: 0.8264 |
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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: 30 |
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- eval_batch_size: 8 |
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- seed: 1 |
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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: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5623 | 1.0 | 122 | 0.5053 | 0.7168 | 0.6410 | 0.5796 | 0.5795 | |
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| 0.518 | 2.0 | 244 | 0.4861 | 0.7293 | 0.6674 | 0.5960 | 0.5998 | |
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| 0.4835 | 3.0 | 366 | 0.4552 | 0.7694 | 0.7211 | 0.7094 | 0.7145 | |
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| 0.4497 | 4.0 | 488 | 0.4223 | 0.7945 | 0.7521 | 0.7521 | 0.7521 | |
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| 0.4266 | 5.0 | 610 | 0.3996 | 0.8170 | 0.7814 | 0.7680 | 0.7741 | |
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| 0.3907 | 6.0 | 732 | 0.3830 | 0.8195 | 0.7818 | 0.7873 | 0.7845 | |
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| 0.3742 | 7.0 | 854 | 0.3684 | 0.8346 | 0.8016 | 0.7955 | 0.7984 | |
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| 0.3616 | 8.0 | 976 | 0.3720 | 0.8271 | 0.7902 | 0.8051 | 0.7968 | |
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| 0.3294 | 9.0 | 1098 | 0.3689 | 0.8371 | 0.8019 | 0.8147 | 0.8077 | |
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| 0.3207 | 10.0 | 1220 | 0.3632 | 0.8396 | 0.8047 | 0.8190 | 0.8111 | |
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| 0.3214 | 11.0 | 1342 | 0.3577 | 0.8371 | 0.8017 | 0.8172 | 0.8086 | |
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| 0.3167 | 12.0 | 1464 | 0.3607 | 0.8396 | 0.8046 | 0.8215 | 0.8119 | |
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| 0.289 | 13.0 | 1586 | 0.3684 | 0.8346 | 0.7988 | 0.8155 | 0.8061 | |
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| 0.2997 | 14.0 | 1708 | 0.3480 | 0.8496 | 0.8193 | 0.8161 | 0.8177 | |
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| 0.2986 | 15.0 | 1830 | 0.3576 | 0.8496 | 0.8169 | 0.8261 | 0.8212 | |
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| 0.2914 | 16.0 | 1952 | 0.3497 | 0.8496 | 0.8180 | 0.8211 | 0.8195 | |
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| 0.278 | 17.0 | 2074 | 0.3540 | 0.8521 | 0.8207 | 0.8254 | 0.8229 | |
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| 0.2887 | 18.0 | 2196 | 0.3516 | 0.8521 | 0.8207 | 0.8254 | 0.8229 | |
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| 0.2829 | 19.0 | 2318 | 0.3537 | 0.8521 | 0.8207 | 0.8254 | 0.8229 | |
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| 0.2771 | 20.0 | 2440 | 0.3540 | 0.8546 | 0.8233 | 0.8297 | 0.8264 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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