T5 Product Category & Subcategory Classifier
This model is fine-tuned on T5-base for product category and subcategory classification.
Model Description
- Model Type: T5 (Text-to-Text Transfer Transformer)
- Language: English
- Task: Product Classification
- Training Data: 10,172 categorized products
- Input Format: "Predict the product category and subcategory in the following format: 'Category: | Subcategory: '. Product: {product_name}"
- Output Format: "Category: {category} | Subcategory: {subcategory}"
Usage
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("{repo_id}")
tokenizer = T5Tokenizer.from_pretrained("{repo_id}")
def predict(text):
prompt = f"Predict the product category and subcategory in the following format: 'Category: <CATEGORY> | Subcategory: <SUBCATEGORY>'. Product: {text}"
inputs = tokenizer(prompt, return_tensors="pt", max_length=128, truncation=True)
outputs = model.generate(**inputs, max_length=32, num_beams=4)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example
result = predict("Pantene Suave & Liso Shampoo")
print(result)
Training Details
- Base Model: t5-base
- Training Type: Fine-tuning
- Epochs: 5
- Batch Size: 8
- Learning Rate: 3e-5
- Weight Decay: 0.01
Limitations
- The model works best with product names in English
- Performance may vary for products outside the training categories
- Requires clear and specific product descriptions
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