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--- |
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language: |
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- id |
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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-unipelt-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-unipelt-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: 0.3013 |
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- Accuracy: 0.8922 |
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- Precision: 0.8694 |
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- Recall: 0.8712 |
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- F1: 0.8703 |
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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: 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: 20.0 |
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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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| 0.5538 | 1.0 | 122 | 0.4789 | 0.7193 | 0.6517 | 0.6289 | 0.6359 | |
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| 0.4356 | 2.0 | 244 | 0.4088 | 0.7845 | 0.7518 | 0.7900 | 0.7610 | |
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| 0.3417 | 3.0 | 366 | 0.3369 | 0.8571 | 0.8365 | 0.8089 | 0.8206 | |
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| 0.2904 | 4.0 | 488 | 0.3267 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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| 0.263 | 5.0 | 610 | 0.3210 | 0.8672 | 0.8356 | 0.8585 | 0.8453 | |
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| 0.2463 | 6.0 | 732 | 0.3551 | 0.8421 | 0.8093 | 0.8483 | 0.8220 | |
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| 0.2303 | 7.0 | 854 | 0.3028 | 0.8722 | 0.8409 | 0.8696 | 0.8524 | |
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| 0.2208 | 8.0 | 976 | 0.2673 | 0.8897 | 0.8695 | 0.8620 | 0.8656 | |
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| 0.1994 | 9.0 | 1098 | 0.2715 | 0.8897 | 0.8649 | 0.8720 | 0.8683 | |
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| 0.1836 | 10.0 | 1220 | 0.2595 | 0.9098 | 0.8999 | 0.8787 | 0.8883 | |
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| 0.1706 | 11.0 | 1342 | 0.2833 | 0.8922 | 0.8650 | 0.8838 | 0.8734 | |
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| 0.1623 | 12.0 | 1464 | 0.2993 | 0.8872 | 0.8599 | 0.8752 | 0.8669 | |
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| 0.1478 | 13.0 | 1586 | 0.2864 | 0.8972 | 0.8849 | 0.8623 | 0.8724 | |
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| 0.1467 | 14.0 | 1708 | 0.2805 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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| 0.132 | 15.0 | 1830 | 0.2869 | 0.8997 | 0.8748 | 0.8891 | 0.8814 | |
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| 0.125 | 16.0 | 1952 | 0.3052 | 0.8972 | 0.8723 | 0.8848 | 0.8781 | |
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| 0.1183 | 17.0 | 2074 | 0.2968 | 0.8897 | 0.8649 | 0.8720 | 0.8683 | |
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| 0.1185 | 18.0 | 2196 | 0.3033 | 0.8922 | 0.8673 | 0.8763 | 0.8716 | |
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| 0.1132 | 19.0 | 2318 | 0.3063 | 0.8897 | 0.8640 | 0.8745 | 0.8689 | |
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| 0.1195 | 20.0 | 2440 | 0.3013 | 0.8922 | 0.8694 | 0.8712 | 0.8703 | |
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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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