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--- |
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language: |
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- id |
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--- |
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DENGAN PENGUJIAN TERHADAP 5000 DATASET |
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# Model Card radityapranata/absabert-keluhanpln-v3 |
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<!-- Provide a quick summary of what the model is/does. --> |
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Basic Model ABSA BERT KELUHAN PLN dalam bahasa indonesia merupakan model yang dihasilkan dari : ##bert-base-uncased |
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[this raw template](https://huggingface.co/bert-base-uncased). |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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Dataset yang digunakan disini merupakan kumpulan data keluhan pelanggan |
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dalam bahasa indonesia pada PLN Mobile. |
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Dengan pengukuran aspect yang terbagi meliputi : |
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"Layanan Pelanggan" , "Ketersediaan Produk" , "Kebijakan Usaha" , "Pemulihan Layanan". |
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Hal ini mengutip dari jenis pengukuran yag dilakukan oleh Simon J. Bell di tahun 2006 |
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dari University of Melbourne dalam publikasinya yang berjudul |
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Coping With Customer Complaints |
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### How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>>pip install torch |
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>>>pip install transformers |
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>>>from transformers import BertTokenizer, BertForSequenceClassification |
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>>>import torch |
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model_name = "radityapranata/absabert-keluhanpln-v3" |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = BertForSequenceClassification.from_pretrained(model_name) |
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absa_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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text = "informasi tambah daya" #"Token listrik tidak dapat terisi, kwh meter tulisan periksa." |
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inputs = tokenizer(text, return_tensors="pt") |
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result = absa_pipeline(text) |
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outputs = model(**inputs) |
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logits = outputs.logits |
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get_aspect = torch.argmax(logits, dim=1).item() |
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aspects = ["Layanan Pelanggan", "Ketersediaan Produk", "Kebijakan Usaha", "Pemulihan Layanan"] |
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aspect = aspects[get_aspect] |
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for aspect_result in result: |
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Sentiment = aspect_result["label"] |
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Score = aspect_result["score"] |
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print("Aspect:", aspect) |
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print(f"Sentiment: {Sentiment}, Score: {Score}") |
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``` |
