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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: belajarner |
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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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# belajarner |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co./google-bert/bert-base-multilingual-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2832 |
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- Precision: 0.8019 |
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- Recall: 0.8379 |
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- F1: 0.8195 |
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- Accuracy: 0.9411 |
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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: 8 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2947 | 1.0 | 1567 | 0.2493 | 0.7364 | 0.7978 | 0.7658 | 0.9276 | |
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| 0.1966 | 2.0 | 3134 | 0.2276 | 0.7717 | 0.8150 | 0.7927 | 0.9351 | |
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| 0.1486 | 3.0 | 4701 | 0.2354 | 0.7773 | 0.8293 | 0.8025 | 0.9366 | |
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| 0.1183 | 4.0 | 6268 | 0.2468 | 0.8001 | 0.8388 | 0.8190 | 0.9396 | |
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| 0.096 | 5.0 | 7835 | 0.2539 | 0.8093 | 0.8296 | 0.8193 | 0.9407 | |
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| 0.0772 | 6.0 | 9402 | 0.2717 | 0.8000 | 0.8382 | 0.8187 | 0.9395 | |
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| 0.0648 | 7.0 | 10969 | 0.2822 | 0.8010 | 0.8400 | 0.8201 | 0.9412 | |
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| 0.0556 | 8.0 | 12536 | 0.2832 | 0.8019 | 0.8379 | 0.8195 | 0.9411 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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