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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-lora-r8 |
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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-lora-r8 |
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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: 0.2908 |
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- Accuracy: 0.8772 |
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- Precision: 0.8535 |
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- Recall: 0.8481 |
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- F1: 0.8507 |
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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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| 0.556 | 1.0 | 122 | 0.5325 | 0.7168 | 0.6617 | 0.6671 | 0.6641 | |
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| 0.5103 | 2.0 | 244 | 0.4822 | 0.7719 | 0.7715 | 0.6386 | 0.6524 | |
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| 0.4637 | 3.0 | 366 | 0.4245 | 0.8045 | 0.7715 | 0.7342 | 0.7480 | |
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| 0.4173 | 4.0 | 488 | 0.3898 | 0.8246 | 0.7888 | 0.7859 | 0.7873 | |
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| 0.3674 | 5.0 | 610 | 0.3571 | 0.8371 | 0.8059 | 0.7947 | 0.7999 | |
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| 0.3484 | 6.0 | 732 | 0.3432 | 0.8371 | 0.8038 | 0.8022 | 0.8030 | |
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| 0.3247 | 7.0 | 854 | 0.3299 | 0.8521 | 0.8271 | 0.8079 | 0.8164 | |
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| 0.3102 | 8.0 | 976 | 0.3260 | 0.8622 | 0.8510 | 0.8050 | 0.8228 | |
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| 0.2991 | 9.0 | 1098 | 0.3138 | 0.8571 | 0.8349 | 0.8114 | 0.8216 | |
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| 0.29 | 10.0 | 1220 | 0.3123 | 0.8546 | 0.8324 | 0.8071 | 0.8180 | |
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| 0.2778 | 11.0 | 1342 | 0.3065 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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| 0.2702 | 12.0 | 1464 | 0.3006 | 0.8571 | 0.8349 | 0.8114 | 0.8216 | |
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| 0.2664 | 13.0 | 1586 | 0.2996 | 0.8596 | 0.8316 | 0.8282 | 0.8298 | |
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| 0.264 | 14.0 | 1708 | 0.2987 | 0.8722 | 0.8437 | 0.8521 | 0.8477 | |
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| 0.254 | 15.0 | 1830 | 0.2951 | 0.8772 | 0.8514 | 0.8531 | 0.8522 | |
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| 0.2571 | 16.0 | 1952 | 0.2945 | 0.8672 | 0.8463 | 0.8260 | 0.8351 | |
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| 0.2511 | 17.0 | 2074 | 0.2918 | 0.8722 | 0.8463 | 0.8446 | 0.8454 | |
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| 0.2574 | 18.0 | 2196 | 0.2909 | 0.8747 | 0.8510 | 0.8438 | 0.8473 | |
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| 0.2508 | 19.0 | 2318 | 0.2907 | 0.8772 | 0.8535 | 0.8481 | 0.8507 | |
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| 0.2536 | 20.0 | 2440 | 0.2908 | 0.8772 | 0.8535 | 0.8481 | 0.8507 | |
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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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