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--- |
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license: mit |
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base_model: indobenchmark/indobert-large-p1 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: indobert-large-p1-twitter-indonesia-sarcastic |
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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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# indobert-large-p1-twitter-indonesia-sarcastic |
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This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3207 |
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- Accuracy: 0.8643 |
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- F1: 0.7160 |
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- Precision: 0.7480 |
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- Recall: 0.6866 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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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: cosine |
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- num_epochs: 100.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.5836 | 1.0 | 59 | 0.4153 | 0.8060 | 0.5738 | 0.6364 | 0.5224 | |
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| 0.3766 | 2.0 | 118 | 0.3353 | 0.8433 | 0.5962 | 0.8378 | 0.4627 | |
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| 0.2476 | 3.0 | 177 | 0.3114 | 0.8619 | 0.6942 | 0.7778 | 0.6269 | |
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| 0.1356 | 4.0 | 236 | 0.3279 | 0.8694 | 0.7328 | 0.75 | 0.7164 | |
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| 0.0536 | 5.0 | 295 | 0.4265 | 0.8582 | 0.7164 | 0.7164 | 0.7164 | |
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| 0.0157 | 6.0 | 354 | 0.6448 | 0.8619 | 0.6667 | 0.8409 | 0.5522 | |
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| 0.0076 | 7.0 | 413 | 0.5739 | 0.8619 | 0.7218 | 0.7273 | 0.7164 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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