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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-r4a1d0.15-1 |
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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-r4a1d0.15-1 |
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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: 0.3312 |
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- Accuracy: 0.8622 |
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- Precision: 0.8414 |
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- Recall: 0.8175 |
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- F1: 0.8279 |
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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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| 0.566 | 1.0 | 122 | 0.5206 | 0.7143 | 0.6484 | 0.6353 | 0.6403 | |
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| 0.5117 | 2.0 | 244 | 0.5062 | 0.7343 | 0.6880 | 0.7045 | 0.6939 | |
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| 0.4804 | 3.0 | 366 | 0.4667 | 0.7669 | 0.7182 | 0.7126 | 0.7152 | |
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| 0.4345 | 4.0 | 488 | 0.4350 | 0.7920 | 0.7494 | 0.7403 | 0.7445 | |
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| 0.4081 | 5.0 | 610 | 0.4337 | 0.7945 | 0.7565 | 0.7846 | 0.7661 | |
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| 0.3793 | 6.0 | 732 | 0.3923 | 0.8195 | 0.7857 | 0.7673 | 0.7753 | |
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| 0.3665 | 7.0 | 854 | 0.3765 | 0.8296 | 0.7949 | 0.7919 | 0.7934 | |
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| 0.3471 | 8.0 | 976 | 0.3681 | 0.8371 | 0.8089 | 0.7872 | 0.7966 | |
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| 0.3498 | 9.0 | 1098 | 0.3677 | 0.8321 | 0.8024 | 0.7812 | 0.7904 | |
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| 0.3282 | 10.0 | 1220 | 0.3634 | 0.8346 | 0.8074 | 0.7805 | 0.7917 | |
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| 0.3149 | 11.0 | 1342 | 0.3537 | 0.8446 | 0.8180 | 0.7976 | 0.8065 | |
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| 0.3092 | 12.0 | 1464 | 0.3529 | 0.8496 | 0.8202 | 0.8136 | 0.8167 | |
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| 0.3135 | 13.0 | 1586 | 0.3471 | 0.8521 | 0.8332 | 0.7979 | 0.8122 | |
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| 0.3103 | 14.0 | 1708 | 0.3427 | 0.8622 | 0.8430 | 0.8150 | 0.8269 | |
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| 0.2974 | 15.0 | 1830 | 0.3372 | 0.8622 | 0.8385 | 0.8225 | 0.8298 | |
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| 0.2905 | 16.0 | 1952 | 0.3345 | 0.8697 | 0.8488 | 0.8303 | 0.8386 | |
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| 0.2895 | 17.0 | 2074 | 0.3339 | 0.8622 | 0.8430 | 0.8150 | 0.8269 | |
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| 0.2922 | 18.0 | 2196 | 0.3319 | 0.8697 | 0.8488 | 0.8303 | 0.8386 | |
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| 0.2843 | 19.0 | 2318 | 0.3319 | 0.8622 | 0.8430 | 0.8150 | 0.8269 | |
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| 0.287 | 20.0 | 2440 | 0.3312 | 0.8622 | 0.8414 | 0.8175 | 0.8279 | |
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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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