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README.md
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example_title: "Contoh 3"
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---
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## EN:
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The project of this pretrained_model involves a series of complex steps that begin with leveraging the indolem/indobertweet-base-uncased pretrained model. Through a meticulous fine-tuning process, this model has been enhanced by utilizing the optimizer.pt from the aforementioned pretrained model.
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The primary objective behind the development of this model is to address the bias frequently encountered in product reviews on e-commerce platforms. One of the classic issues on such platforms is the disconnect between the language used in reviews and the ratings given by users. This model was conceived with a focus on mitigating this problem.
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## ID:
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Proyek pretrained_model ini melibatkan serangkaian langkah kompleks yang dimulai dengan pemanfaatan pretrained model indolem/indobertweet-base-uncased. Melalui proses fine-tuning yang cermat, model ini berhasil disempurnakan dengan memanfaatkan optimizer.pt yang ada dalam pretrained model tersebut.
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Tujuan utama di balik pengembangan model ini adalah mengatasi bias yang sering muncul dalam ulasan produk di platform e-commerce. Salah satu masalah klasik di platform semacam ini adalah ketidaksesuaian antara kata-kata dalam ulasan dan peringkat yang diberikan oleh pengguna. Model ini diciptakan dengan fokus pada penyelesaian masalah ini.
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example_title: "Contoh 3"
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# E-Commerce Rating's Review Classification: Women's Beauty Product
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## EN:
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The project of this pretrained_model involves a series of complex steps that begin with leveraging the [IndoBERTweet](https://huggingface.co/indolem/indobertweet-base-uncased) pretrained model. Through a meticulous fine-tuning process, this model has been enhanced by utilizing the optimizer.pt from the aforementioned pretrained model.
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The primary objective behind the development of this model is to address the bias frequently encountered in product reviews on e-commerce platforms. One of the classic issues on such platforms is the disconnect between the language used in reviews and the ratings given by users. This model was conceived with a focus on mitigating this problem.
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# Klasifikasi Rating Ulasan E-Commerce: Produk Kecantikan Wanita
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## ID:
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Proyek pretrained_model ini melibatkan serangkaian langkah kompleks yang dimulai dengan pemanfaatan pretrained model [IndoBERTweet](https://huggingface.co/indolem/indobertweet-base-uncased). Melalui proses fine-tuning yang cermat, model ini berhasil disempurnakan dengan memanfaatkan optimizer.pt yang ada dalam pretrained model tersebut.
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Tujuan utama di balik pengembangan model ini adalah mengatasi bias yang sering muncul dalam ulasan produk di platform e-commerce. Salah satu masalah klasik di platform semacam ini adalah ketidaksesuaian antara kata-kata dalam ulasan dan peringkat yang diberikan oleh pengguna. Model ini diciptakan dengan fokus pada penyelesaian masalah ini.
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