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--- |
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language: |
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- id |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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base_model: |
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- indobenchmark/indobert-base-p1 |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- NLP |
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- indobert |
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- sentimen |
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--- |
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# IndoBERT Sentiment Analysis Model |
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Model ini adalah hasil fine-tuning model IndoBERT base untuk tugas klasifikasi sentimen bahasa Indonesia. |
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## Penggunaan |
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### Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="Ha1dir/sentimen-indobert") |
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### Load model directly |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Ha1dir/sentimen-indobert") |
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model = AutoModelForSequenceClassification.from_pretrained("Ha1dir/sentimen-indobert") |
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## Label Kelas |
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- 0: Positive |
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- 1: Negative |
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- 2: Neutral |
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## Tentang Model |
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- Base Model: indobenchmark/indobert-base-p1 |
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- Training Epochs: 5 |
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- Optimizer: Adam, LR = 3e-6 |
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## Hasil Training |
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(Epoch 1) TRAIN LOSS: 0.2962 |
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Acc: 0.8896 |
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Precision: 0.8893 |
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Recall: 0.8896 |
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F1: 0.8876 |
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(Epoch 2) TRAIN LOSS: 0.1450 |
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Acc: 0.9514 |
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Precision: 0.9513 |
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Recall: 0.9514 |
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F1: 0.9513 |
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(Epoch 3) TRAIN LOSS: 0.1053 |
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Acc: 0.9646 |
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Precision: 0.9646 |
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Recall: 0.9646 |
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F1: 0.9646 |
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(Epoch 4) TRAIN LOSS: 0.0722 |
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Acc: 0.9781 |
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Precision: 0.9781 |
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Recall: 0.9781 |
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F1: 0.9781 |
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(Epoch 5) TRAIN LOSS: 0.0468 |
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Acc: 0.9874 |
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Precision: 0.9874 |
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Recall: 0.9874 |
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F1: 0.9874 |
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## Validasi Mode |
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VAL LOSS: 0.0234 |
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Acc: 0.9955 |
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Precision: 0.9955 |
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Recall: 0.9955 |
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F1: 0.9954 |
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## Evaluasi |
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VAL LOSS: 0.0234 |
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Acc: 0.9982 |
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Precision: 0.9982 |
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Recall: 0.9982 |
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F1: 0.9982 |
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--- |
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