File size: 2,717 Bytes
a43649e 1665a27 ad3ee8a 5424d1f fe71522 a43649e e5333ab 17fdcf7 e5333ab 17fdcf7 b2fdd5e 17fdcf7 ad3ee8a fd4298f ad3ee8a 1665a27 fe71522 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
- indobert
- indobenchmark
- indonlu
datasets:
- fahrendrakhoirul/ecommerce-reviews-multilabel-dataset
language:
- id
metrics:
- f1
- recall
- precision
library_name: transformers
pipeline_tag: text-classification
---
**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:**
```python
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
from transformers import AutoModelForSequenceClassification, 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 = AutoModelForSequenceClassification.from_pretrained("indobenchmark/indobert-base-p1",
num_labels=3,
problem_type="multi_label_classification")
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](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed] |