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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-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-seq_bn-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: 0.3078 |
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- Accuracy: 0.8797 |
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- Precision: 0.8572 |
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- Recall: 0.8499 |
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- F1: 0.8534 |
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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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| 0.5593 | 1.0 | 122 | 0.5129 | 0.7318 | 0.6697 | 0.6453 | 0.6532 | |
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| 0.481 | 2.0 | 244 | 0.4831 | 0.7343 | 0.6993 | 0.7295 | 0.7054 | |
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| 0.4234 | 3.0 | 366 | 0.3974 | 0.8221 | 0.7926 | 0.7616 | 0.7740 | |
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| 0.3701 | 4.0 | 488 | 0.3780 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | |
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| 0.3499 | 5.0 | 610 | 0.3612 | 0.8471 | 0.8135 | 0.8268 | 0.8195 | |
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| 0.3165 | 6.0 | 732 | 0.3760 | 0.8271 | 0.7953 | 0.8377 | 0.8072 | |
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| 0.2968 | 7.0 | 854 | 0.3342 | 0.8697 | 0.8438 | 0.8403 | 0.8420 | |
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| 0.2812 | 8.0 | 976 | 0.3311 | 0.8672 | 0.8463 | 0.8260 | 0.8351 | |
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| 0.2682 | 9.0 | 1098 | 0.3269 | 0.8722 | 0.8463 | 0.8446 | 0.8454 | |
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| 0.2596 | 10.0 | 1220 | 0.3145 | 0.8797 | 0.8560 | 0.8524 | 0.8541 | |
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| 0.2464 | 11.0 | 1342 | 0.3138 | 0.8697 | 0.8503 | 0.8278 | 0.8377 | |
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| 0.2415 | 12.0 | 1464 | 0.3126 | 0.8847 | 0.8697 | 0.8459 | 0.8565 | |
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| 0.2354 | 13.0 | 1586 | 0.3136 | 0.8822 | 0.8694 | 0.8392 | 0.8521 | |
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| 0.2303 | 14.0 | 1708 | 0.3172 | 0.8747 | 0.8463 | 0.8563 | 0.8510 | |
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| 0.2172 | 15.0 | 1830 | 0.3120 | 0.8822 | 0.8656 | 0.8442 | 0.8537 | |
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| 0.2159 | 16.0 | 1952 | 0.3116 | 0.8622 | 0.8319 | 0.8400 | 0.8357 | |
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| 0.2192 | 17.0 | 2074 | 0.3123 | 0.8847 | 0.8717 | 0.8434 | 0.8557 | |
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| 0.2124 | 18.0 | 2196 | 0.3150 | 0.8647 | 0.8340 | 0.8467 | 0.8399 | |
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| 0.2077 | 19.0 | 2318 | 0.3084 | 0.8797 | 0.8585 | 0.8474 | 0.8526 | |
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| 0.205 | 20.0 | 2440 | 0.3078 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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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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