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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-pt-pl30-1 |
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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-pt-pl30-1 |
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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: 0.3434 |
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- Accuracy: 0.8722 |
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- Precision: 0.8485 |
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- Recall: 0.8396 |
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- F1: 0.8438 |
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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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| 0.5472 | 1.0 | 122 | 0.4993 | 0.7343 | 0.6726 | 0.6245 | 0.6339 | |
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| 0.4484 | 2.0 | 244 | 0.4157 | 0.7945 | 0.7655 | 0.8096 | 0.7744 | |
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| 0.3338 | 3.0 | 366 | 0.3279 | 0.8596 | 0.8510 | 0.7982 | 0.8179 | |
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| 0.2902 | 4.0 | 488 | 0.3037 | 0.8672 | 0.8449 | 0.8285 | 0.8360 | |
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| 0.2756 | 5.0 | 610 | 0.2922 | 0.8747 | 0.8499 | 0.8463 | 0.8481 | |
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| 0.2514 | 6.0 | 732 | 0.3059 | 0.8672 | 0.8359 | 0.8560 | 0.8446 | |
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| 0.2338 | 7.0 | 854 | 0.2970 | 0.8596 | 0.8278 | 0.8432 | 0.8347 | |
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| 0.2205 | 8.0 | 976 | 0.2967 | 0.8847 | 0.8784 | 0.8359 | 0.8531 | |
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| 0.2153 | 9.0 | 1098 | 0.2982 | 0.8672 | 0.8393 | 0.8410 | 0.8402 | |
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| 0.1969 | 10.0 | 1220 | 0.2943 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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| 0.185 | 11.0 | 1342 | 0.2973 | 0.8647 | 0.8359 | 0.8392 | 0.8376 | |
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| 0.1733 | 12.0 | 1464 | 0.3074 | 0.8672 | 0.8423 | 0.8335 | 0.8377 | |
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| 0.1616 | 13.0 | 1586 | 0.3186 | 0.8697 | 0.8460 | 0.8353 | 0.8404 | |
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| 0.16 | 14.0 | 1708 | 0.3222 | 0.8596 | 0.8278 | 0.8432 | 0.8347 | |
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| 0.1494 | 15.0 | 1830 | 0.3260 | 0.8747 | 0.8523 | 0.8413 | 0.8465 | |
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| 0.1501 | 16.0 | 1952 | 0.3233 | 0.8647 | 0.8359 | 0.8392 | 0.8376 | |
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| 0.1468 | 17.0 | 2074 | 0.3296 | 0.8672 | 0.8412 | 0.8360 | 0.8385 | |
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| 0.1423 | 18.0 | 2196 | 0.3367 | 0.8647 | 0.8398 | 0.8292 | 0.8342 | |
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| 0.1327 | 19.0 | 2318 | 0.3395 | 0.8697 | 0.8438 | 0.8403 | 0.8420 | |
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| 0.1413 | 20.0 | 2440 | 0.3434 | 0.8722 | 0.8485 | 0.8396 | 0.8438 | |
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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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