Title: IndoBERT-EcommerceReview
Short Summary:
A fine-tuned IndoBERT model for multi-label classification of customer reviews in e-commerce, focusing on product quality, customer service, and shipping/delivery.
Detailed Description: Explain that the model is based on IndoBERT-base-p1, a pre-trained IndoBERT model specifically designed for Indonesian text. Highlight that it's fine-tuned on a dataset of e-commerce reviews, allowing it to understand the nuances of customer sentiment in this domain. Clearly define the three output classes and their corresponding labels:
- Produk (Product): Customer satisfaction with product quality, performance, and description accuracy.
- Layanan Pelanggan (Customer Service): Interaction with sellers, their responsiveness, and complaint handling.
- Pengiriman (Shipping/Delivery): Speed of delivery, item condition upon arrival, and timeliness.
Optionally, provide brief examples of reviews that would fall into each category to further illustrate how the model interprets sentiment.
How to import in PyTorch:
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
from transformers import BertModel, AutoTokenizer
class IndoBertEcommerceReview(nn.Module, PyTorchModelHubMixin):
def __init__(self, bert):
super().__init__()
self.bert = bert
self.sigmoid = nn.Sigmoid()
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
probabilities = self.sigmoid(logits)
return probabilities
bert = BertModel.from_pretrained("indobenchmark/indobert-base-p1")
tokenizer = AutoTokenizer.from_pretrained("fahrendrakhoirul/indobert-finetuned-ecommerce-reviews")
model = IndoBertEcommerceReview.from_pretrained("fahrendrakhoirul/indobert-finetuned-ecommerce-reviews", bert=bert)
This model has been pushed to the Hub using the PytorchModelHubMixin integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
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