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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: indobert-base-uncased-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-base-uncased-twitter-indonesia-sarcastic |
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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: 0.4202 |
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- Accuracy: 0.8030 |
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- F1: 0.6467 |
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- Precision: 0.5843 |
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- Recall: 0.7239 |
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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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| 0.5531 | 1.0 | 59 | 0.4977 | 0.7724 | 0.4078 | 0.5833 | 0.3134 | |
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| 0.4992 | 2.0 | 118 | 0.4785 | 0.7724 | 0.3441 | 0.6154 | 0.2388 | |
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| 0.44 | 3.0 | 177 | 0.4819 | 0.7799 | 0.3656 | 0.6538 | 0.2537 | |
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| 0.3815 | 4.0 | 236 | 0.4524 | 0.8097 | 0.6623 | 0.5952 | 0.7463 | |
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| 0.3104 | 5.0 | 295 | 0.4547 | 0.8172 | 0.5421 | 0.725 | 0.4328 | |
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| 0.2592 | 6.0 | 354 | 0.4058 | 0.8172 | 0.5664 | 0.6957 | 0.4776 | |
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| 0.2083 | 7.0 | 413 | 0.4358 | 0.8060 | 0.5738 | 0.6364 | 0.5224 | |
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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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