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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- indonlu
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metrics:
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- accuracy
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- f1
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model-index:
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- name: indobert-distilled-optimized-for-classification
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: indonlu
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type: indonlu
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args: smsa
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9023809523809524
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- name: F1
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type: f1
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value: 0.9020516403647337
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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-distilled-optimized-for-classification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5991
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- Accuracy: 0.9024
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- F1: 0.9021
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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: 5.262995179171344e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 33
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 1.2938 | 1.0 | 688 | 0.8433 | 0.8484 | 0.8513 |
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| 0.711 | 2.0 | 1376 | 0.6408 | 0.8881 | 0.8878 |
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| 0.4416 | 3.0 | 2064 | 0.7964 | 0.8794 | 0.8793 |
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| 0.2907 | 4.0 | 2752 | 0.7559 | 0.8897 | 0.8900 |
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| 0.2065 | 5.0 | 3440 | 0.6892 | 0.8968 | 0.8974 |
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| 0.1574 | 6.0 | 4128 | 0.6881 | 0.8913 | 0.8906 |
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| 0.1131 | 7.0 | 4816 | 0.6224 | 0.8984 | 0.8982 |
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| 0.0865 | 8.0 | 5504 | 0.6312 | 0.8976 | 0.8970 |
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| 0.0678 | 9.0 | 6192 | 0.6187 | 0.8992 | 0.8989 |
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| 0.0526 | 10.0 | 6880 | 0.5991 | 0.9024 | 0.9021 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.10.0+cu111
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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