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README.md ADDED
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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-pt-pl10-3
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+ results: []
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+ ---
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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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+
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+ # sentiment-pt-pl10-3
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+
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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: 0.2824
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+ - Accuracy: 0.8897
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+ - Precision: 0.8710
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+ - Recall: 0.8595
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+ - F1: 0.8649
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.5526 | 1.0 | 122 | 0.5089 | 0.7143 | 0.6432 | 0.6178 | 0.6246 |
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+ | 0.4578 | 2.0 | 244 | 0.4194 | 0.7995 | 0.7620 | 0.7906 | 0.7718 |
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+ | 0.3632 | 3.0 | 366 | 0.3468 | 0.8371 | 0.8222 | 0.7672 | 0.7867 |
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+ | 0.3063 | 4.0 | 488 | 0.2975 | 0.8822 | 0.8564 | 0.8617 | 0.8590 |
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+ | 0.2771 | 5.0 | 610 | 0.3195 | 0.8722 | 0.8409 | 0.8696 | 0.8524 |
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+ | 0.2463 | 6.0 | 732 | 0.2897 | 0.8772 | 0.8496 | 0.8581 | 0.8537 |
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+ | 0.2317 | 7.0 | 854 | 0.2718 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
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+ | 0.2182 | 8.0 | 976 | 0.2683 | 0.8847 | 0.8556 | 0.8784 | 0.8654 |
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+ | 0.2065 | 9.0 | 1098 | 0.2773 | 0.8697 | 0.8411 | 0.8478 | 0.8443 |
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+ | 0.2012 | 10.0 | 1220 | 0.2841 | 0.8822 | 0.8674 | 0.8417 | 0.8529 |
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+ | 0.1944 | 11.0 | 1342 | 0.2733 | 0.8847 | 0.8599 | 0.8634 | 0.8616 |
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+ | 0.176 | 12.0 | 1464 | 0.2709 | 0.8972 | 0.8849 | 0.8623 | 0.8724 |
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+ | 0.168 | 13.0 | 1586 | 0.2651 | 0.8947 | 0.8718 | 0.8755 | 0.8737 |
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+ | 0.1644 | 14.0 | 1708 | 0.2711 | 0.8922 | 0.8657 | 0.8813 | 0.8728 |
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+ | 0.1541 | 15.0 | 1830 | 0.2790 | 0.8922 | 0.8734 | 0.8637 | 0.8683 |
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+ | 0.1574 | 16.0 | 1952 | 0.2767 | 0.8897 | 0.8695 | 0.8620 | 0.8656 |
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+ | 0.1508 | 17.0 | 2074 | 0.2826 | 0.8897 | 0.8659 | 0.8695 | 0.8676 |
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+ | 0.1463 | 18.0 | 2196 | 0.2824 | 0.8872 | 0.8687 | 0.8552 | 0.8615 |
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+ | 0.1467 | 19.0 | 2318 | 0.2876 | 0.8847 | 0.8679 | 0.8484 | 0.8573 |
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+ | 0.1399 | 20.0 | 2440 | 0.2824 | 0.8897 | 0.8710 | 0.8595 | 0.8649 |
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+
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+
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+ ### Framework versions
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+
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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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