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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-lora-r16a0d0.1-0 |
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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-lora-r16a0d0.1-0 |
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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: 0.2912 |
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- Accuracy: 0.8647 |
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- Precision: 0.8346 |
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- Recall: 0.8442 |
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- F1: 0.8391 |
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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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| 0.559 | 1.0 | 122 | 0.5023 | 0.7268 | 0.6671 | 0.6592 | 0.6627 | |
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| 0.4818 | 2.0 | 244 | 0.4518 | 0.7569 | 0.7248 | 0.7605 | 0.7318 | |
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| 0.4129 | 3.0 | 366 | 0.3967 | 0.8246 | 0.7876 | 0.7959 | 0.7914 | |
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| 0.3519 | 4.0 | 488 | 0.3626 | 0.8496 | 0.8193 | 0.8161 | 0.8177 | |
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| 0.3191 | 5.0 | 610 | 0.3720 | 0.8471 | 0.8130 | 0.8393 | 0.8235 | |
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| 0.2977 | 6.0 | 732 | 0.3482 | 0.8546 | 0.8216 | 0.8422 | 0.8303 | |
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| 0.2861 | 7.0 | 854 | 0.3343 | 0.8672 | 0.8363 | 0.8535 | 0.8439 | |
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| 0.2662 | 8.0 | 976 | 0.3213 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2618 | 9.0 | 1098 | 0.3246 | 0.8697 | 0.8399 | 0.8528 | 0.8458 | |
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| 0.2556 | 10.0 | 1220 | 0.3065 | 0.8596 | 0.8307 | 0.8307 | 0.8307 | |
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| 0.2353 | 11.0 | 1342 | 0.3148 | 0.8722 | 0.8416 | 0.8621 | 0.8505 | |
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| 0.24 | 12.0 | 1464 | 0.3098 | 0.8747 | 0.8451 | 0.8613 | 0.8524 | |
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| 0.2346 | 13.0 | 1586 | 0.2989 | 0.8772 | 0.8524 | 0.8506 | 0.8515 | |
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| 0.2367 | 14.0 | 1708 | 0.3001 | 0.8697 | 0.8399 | 0.8528 | 0.8458 | |
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| 0.2248 | 15.0 | 1830 | 0.3040 | 0.8722 | 0.8420 | 0.8596 | 0.8498 | |
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| 0.2174 | 16.0 | 1952 | 0.3016 | 0.8697 | 0.8386 | 0.8603 | 0.8479 | |
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| 0.2112 | 17.0 | 2074 | 0.2887 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.2162 | 18.0 | 2196 | 0.2980 | 0.8722 | 0.8416 | 0.8621 | 0.8505 | |
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| 0.2124 | 19.0 | 2318 | 0.2892 | 0.8697 | 0.8411 | 0.8478 | 0.8443 | |
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| 0.2118 | 20.0 | 2440 | 0.2912 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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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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