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fahrendrakhoirul
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indobert-finetuned-ecommerce-reviews

Text Classification
Transformers
Safetensors
Indonesian
bert
feature-extraction
pytorch_model_hub_mixin
model_hub_mixin
indobert
indobenchmark
indonlu
Inference Endpoints
Model card Files Community
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@@ -26,7 +26,8 @@ Highlight that it's fine-tuned on a dataset of e-commerce reviews, allowing it t
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  Clearly define the three output classes and their corresponding labels:
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  - Produk (Product): Customer satisfaction with product quality, performance, and description accuracy.
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  - Layanan Pelanggan (Customer Service): Interaction with sellers, their responsiveness, and complaint handling.
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- - Pengiriman (Shipping/Delivery): Speed of delivery, item condition upon arrival, and timeliness.
 
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  Optionally, provide brief examples of reviews that would fall into each category to further illustrate how the model interprets sentiment.
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  **How to import in PyTorch:**
 
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  Clearly define the three output classes and their corresponding labels:
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  - Produk (Product): Customer satisfaction with product quality, performance, and description accuracy.
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  - Layanan Pelanggan (Customer Service): Interaction with sellers, their responsiveness, and complaint handling.
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+ - Pengiriman (Shipping/Delivery): Speed of delivery, item condition upon arrival, and timeliness.
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+
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  Optionally, provide brief examples of reviews that would fall into each category to further illustrate how the model interprets sentiment.
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  **How to import in PyTorch:**