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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-seq_bn-2 |
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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-seq_bn-2 |
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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: 0.3205 |
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- Accuracy: 0.8772 |
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- Precision: 0.8609 |
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- Recall: 0.8356 |
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- F1: 0.8467 |
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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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| 0.5517 | 1.0 | 122 | 0.5131 | 0.7168 | 0.6513 | 0.6371 | 0.6424 | |
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| 0.4833 | 2.0 | 244 | 0.4657 | 0.7519 | 0.7088 | 0.7295 | 0.7159 | |
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| 0.4318 | 3.0 | 366 | 0.4056 | 0.8120 | 0.7729 | 0.7845 | 0.7781 | |
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| 0.3905 | 4.0 | 488 | 0.3811 | 0.8421 | 0.8092 | 0.8108 | 0.8100 | |
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| 0.3626 | 5.0 | 610 | 0.3652 | 0.8496 | 0.8186 | 0.8186 | 0.8186 | |
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| 0.3331 | 6.0 | 732 | 0.3646 | 0.8546 | 0.8214 | 0.8497 | 0.8325 | |
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| 0.3134 | 7.0 | 854 | 0.3440 | 0.8672 | 0.8412 | 0.8360 | 0.8385 | |
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| 0.2927 | 8.0 | 976 | 0.3412 | 0.8647 | 0.8359 | 0.8392 | 0.8376 | |
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| 0.2833 | 9.0 | 1098 | 0.3353 | 0.8647 | 0.8352 | 0.8417 | 0.8383 | |
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| 0.2672 | 10.0 | 1220 | 0.3296 | 0.8672 | 0.8367 | 0.8510 | 0.8432 | |
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| 0.2641 | 11.0 | 1342 | 0.3270 | 0.8772 | 0.8576 | 0.8406 | 0.8484 | |
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| 0.2549 | 12.0 | 1464 | 0.3352 | 0.8697 | 0.8558 | 0.8203 | 0.8350 | |
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| 0.2534 | 13.0 | 1586 | 0.3402 | 0.8697 | 0.8602 | 0.8153 | 0.8330 | |
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| 0.2389 | 14.0 | 1708 | 0.3208 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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| 0.2203 | 15.0 | 1830 | 0.3279 | 0.8747 | 0.8605 | 0.8288 | 0.8422 | |
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| 0.2298 | 16.0 | 1952 | 0.3175 | 0.8747 | 0.8552 | 0.8363 | 0.8448 | |
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| 0.2227 | 17.0 | 2074 | 0.3218 | 0.8747 | 0.8586 | 0.8313 | 0.8431 | |
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| 0.2225 | 18.0 | 2196 | 0.3178 | 0.8772 | 0.8524 | 0.8506 | 0.8515 | |
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| 0.2192 | 19.0 | 2318 | 0.3199 | 0.8772 | 0.8609 | 0.8356 | 0.8467 | |
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| 0.2229 | 20.0 | 2440 | 0.3205 | 0.8772 | 0.8609 | 0.8356 | 0.8467 | |
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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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