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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-r8-4 |
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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-r8-4 |
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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: 0.2847 |
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- Accuracy: 0.8672 |
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- Precision: 0.8423 |
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- Recall: 0.8335 |
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- F1: 0.8377 |
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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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| 0.5577 | 1.0 | 122 | 0.5372 | 0.7193 | 0.6692 | 0.6814 | 0.6738 | |
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| 0.5012 | 2.0 | 244 | 0.4771 | 0.7669 | 0.7370 | 0.6501 | 0.6651 | |
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| 0.4626 | 3.0 | 366 | 0.4250 | 0.8070 | 0.7756 | 0.7360 | 0.7504 | |
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| 0.4055 | 4.0 | 488 | 0.3896 | 0.8346 | 0.7996 | 0.8055 | 0.8024 | |
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| 0.3709 | 5.0 | 610 | 0.3578 | 0.8296 | 0.7961 | 0.7869 | 0.7912 | |
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| 0.3385 | 6.0 | 732 | 0.3523 | 0.8371 | 0.8017 | 0.8172 | 0.8086 | |
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| 0.3276 | 7.0 | 854 | 0.3307 | 0.8521 | 0.8271 | 0.8079 | 0.8164 | |
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| 0.3133 | 8.0 | 976 | 0.3256 | 0.8571 | 0.8381 | 0.8064 | 0.8196 | |
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| 0.3039 | 9.0 | 1098 | 0.3282 | 0.8647 | 0.8491 | 0.8142 | 0.8286 | |
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| 0.2831 | 10.0 | 1220 | 0.3142 | 0.8596 | 0.8316 | 0.8282 | 0.8298 | |
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| 0.2798 | 11.0 | 1342 | 0.3034 | 0.8747 | 0.8523 | 0.8413 | 0.8465 | |
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| 0.269 | 12.0 | 1464 | 0.3002 | 0.8672 | 0.8479 | 0.8235 | 0.8342 | |
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| 0.2699 | 13.0 | 1586 | 0.2973 | 0.8697 | 0.8428 | 0.8428 | 0.8428 | |
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| 0.2657 | 14.0 | 1708 | 0.2985 | 0.8722 | 0.8445 | 0.8496 | 0.8470 | |
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| 0.2537 | 15.0 | 1830 | 0.2886 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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| 0.2529 | 16.0 | 1952 | 0.2878 | 0.8647 | 0.8387 | 0.8317 | 0.8351 | |
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| 0.2565 | 17.0 | 2074 | 0.2877 | 0.8722 | 0.8463 | 0.8446 | 0.8454 | |
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| 0.2514 | 18.0 | 2196 | 0.2857 | 0.8672 | 0.8412 | 0.8360 | 0.8385 | |
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| 0.2517 | 19.0 | 2318 | 0.2844 | 0.8697 | 0.8438 | 0.8403 | 0.8420 | |
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| 0.2512 | 20.0 | 2440 | 0.2847 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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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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