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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-4 |
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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-4 |
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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: 0.2917 |
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- Accuracy: 0.8847 |
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- Precision: 0.8648 |
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- Recall: 0.8534 |
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- F1: 0.8588 |
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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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| 0.5556 | 1.0 | 122 | 0.5148 | 0.7343 | 0.6852 | 0.6970 | 0.6899 | |
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| 0.476 | 2.0 | 244 | 0.4507 | 0.7870 | 0.7577 | 0.6918 | 0.7095 | |
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| 0.4238 | 3.0 | 366 | 0.4003 | 0.8195 | 0.7958 | 0.7473 | 0.7644 | |
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| 0.3735 | 4.0 | 488 | 0.3799 | 0.8371 | 0.8089 | 0.7872 | 0.7966 | |
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| 0.3548 | 5.0 | 610 | 0.3634 | 0.8471 | 0.8153 | 0.8168 | 0.8160 | |
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| 0.3213 | 6.0 | 732 | 0.3584 | 0.8421 | 0.8077 | 0.8208 | 0.8136 | |
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| 0.3085 | 7.0 | 854 | 0.3318 | 0.8622 | 0.8448 | 0.8125 | 0.8259 | |
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| 0.2981 | 8.0 | 976 | 0.3429 | 0.8672 | 0.8722 | 0.7985 | 0.8238 | |
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| 0.2788 | 9.0 | 1098 | 0.3304 | 0.8797 | 0.8795 | 0.8224 | 0.8439 | |
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| 0.259 | 10.0 | 1220 | 0.3076 | 0.8772 | 0.8535 | 0.8481 | 0.8507 | |
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| 0.2587 | 11.0 | 1342 | 0.3025 | 0.8747 | 0.8471 | 0.8538 | 0.8503 | |
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| 0.2391 | 12.0 | 1464 | 0.2990 | 0.8847 | 0.8697 | 0.8459 | 0.8565 | |
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| 0.2443 | 13.0 | 1586 | 0.2919 | 0.8797 | 0.8600 | 0.8449 | 0.8518 | |
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| 0.237 | 14.0 | 1708 | 0.3040 | 0.8772 | 0.8483 | 0.8631 | 0.8550 | |
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| 0.2176 | 15.0 | 1830 | 0.2937 | 0.8897 | 0.8649 | 0.8720 | 0.8683 | |
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| 0.2202 | 16.0 | 1952 | 0.2920 | 0.8822 | 0.8610 | 0.8517 | 0.8561 | |
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| 0.2203 | 17.0 | 2074 | 0.2923 | 0.8822 | 0.8585 | 0.8567 | 0.8575 | |
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| 0.2204 | 18.0 | 2196 | 0.2927 | 0.8847 | 0.8621 | 0.8584 | 0.8602 | |
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| 0.2124 | 19.0 | 2318 | 0.2920 | 0.8872 | 0.8672 | 0.8577 | 0.8622 | |
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| 0.2108 | 20.0 | 2440 | 0.2917 | 0.8847 | 0.8648 | 0.8534 | 0.8588 | |
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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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