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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-seq_bn-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-seq_bn-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: 0.3129 |
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- Accuracy: 0.8747 |
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- Precision: 0.8479 |
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- Recall: 0.8513 |
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- F1: 0.8496 |
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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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| 0.5551 | 1.0 | 122 | 0.4975 | 0.7168 | 0.6485 | 0.6271 | 0.6337 | |
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| 0.4753 | 2.0 | 244 | 0.4682 | 0.7419 | 0.7118 | 0.7474 | 0.7171 | |
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| 0.412 | 3.0 | 366 | 0.3882 | 0.8271 | 0.7994 | 0.7676 | 0.7804 | |
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| 0.3498 | 4.0 | 488 | 0.3691 | 0.8421 | 0.8098 | 0.8083 | 0.8091 | |
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| 0.3361 | 5.0 | 610 | 0.3795 | 0.8145 | 0.7789 | 0.8113 | 0.7897 | |
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| 0.3081 | 6.0 | 732 | 0.4142 | 0.7970 | 0.7665 | 0.8089 | 0.7761 | |
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| 0.2918 | 7.0 | 854 | 0.3555 | 0.8471 | 0.8130 | 0.8393 | 0.8235 | |
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| 0.2739 | 8.0 | 976 | 0.3317 | 0.8647 | 0.8439 | 0.8217 | 0.8315 | |
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| 0.2586 | 9.0 | 1098 | 0.3596 | 0.8571 | 0.8243 | 0.8464 | 0.8336 | |
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| 0.2509 | 10.0 | 1220 | 0.3299 | 0.8622 | 0.8326 | 0.8375 | 0.8349 | |
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| 0.2468 | 11.0 | 1342 | 0.3224 | 0.8672 | 0.8393 | 0.8410 | 0.8402 | |
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| 0.2372 | 12.0 | 1464 | 0.3294 | 0.8571 | 0.8251 | 0.8389 | 0.8314 | |
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| 0.2305 | 13.0 | 1586 | 0.3134 | 0.8697 | 0.8449 | 0.8378 | 0.8412 | |
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| 0.2249 | 14.0 | 1708 | 0.3225 | 0.8697 | 0.8399 | 0.8528 | 0.8458 | |
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| 0.2193 | 15.0 | 1830 | 0.3188 | 0.8747 | 0.8471 | 0.8538 | 0.8503 | |
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| 0.2061 | 16.0 | 1952 | 0.3392 | 0.8521 | 0.8186 | 0.8404 | 0.8278 | |
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| 0.21 | 17.0 | 2074 | 0.3122 | 0.8797 | 0.8560 | 0.8524 | 0.8541 | |
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| 0.2112 | 18.0 | 2196 | 0.3332 | 0.8546 | 0.8216 | 0.8422 | 0.8303 | |
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| 0.2002 | 19.0 | 2318 | 0.3121 | 0.8772 | 0.8524 | 0.8506 | 0.8515 | |
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| 0.2041 | 20.0 | 2440 | 0.3129 | 0.8747 | 0.8479 | 0.8513 | 0.8496 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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