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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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datasets: |
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- indolem_sentiment |
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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: scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased |
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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: indolem_sentiment |
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type: indolem_sentiment |
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config: indolem_sentiment_nusantara_text |
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split: validation |
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args: indolem_sentiment_nusantara_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.899749373433584 |
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- name: F1 |
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type: f1 |
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value: 0.8181818181818181 |
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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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# scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on the indolem_sentiment dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7320 |
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- Accuracy: 0.8997 |
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- F1: 0.8182 |
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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-06 |
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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: 30 |
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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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| No log | 0.44 | 200 | 0.5218 | 0.7343 | 0.2838 | |
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| No log | 0.88 | 400 | 0.4318 | 0.8070 | 0.7138 | |
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| 0.4843 | 1.32 | 600 | 0.4092 | 0.8521 | 0.7281 | |
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| 0.4843 | 1.76 | 800 | 0.3515 | 0.8772 | 0.7803 | |
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| 0.2912 | 2.2 | 1000 | 0.4582 | 0.8697 | 0.7833 | |
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| 0.2912 | 2.64 | 1200 | 0.5148 | 0.8747 | 0.7881 | |
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| 0.2912 | 3.08 | 1400 | 0.5736 | 0.8672 | 0.7837 | |
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| 0.2526 | 3.52 | 1600 | 0.5119 | 0.8797 | 0.7983 | |
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| 0.2526 | 3.96 | 1800 | 0.5242 | 0.8997 | 0.8095 | |
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| 0.1974 | 4.4 | 2000 | 0.5311 | 0.8997 | 0.8182 | |
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| 0.1974 | 4.84 | 2200 | 0.6478 | 0.8797 | 0.7983 | |
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| 0.1974 | 5.27 | 2400 | 0.6219 | 0.8822 | 0.8000 | |
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| 0.1526 | 5.71 | 2600 | 0.6591 | 0.8872 | 0.8178 | |
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| 0.1526 | 6.15 | 2800 | 0.6483 | 0.8947 | 0.8056 | |
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| 0.1159 | 6.59 | 3000 | 0.7075 | 0.8847 | 0.8099 | |
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| 0.1159 | 7.03 | 3200 | 0.7157 | 0.8872 | 0.8 | |
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| 0.1159 | 7.47 | 3400 | 0.7320 | 0.8997 | 0.8182 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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