sticker-query-generator-zh

Sticker Query Generator (中文)

The Sticker Query Generator is a vision-language model that generates culturally and emotionally resonant search queries given a sticker image. These queries are typically used in chat apps to retrieve and recommend stickers during conversations. For English, check out Sticker Query Generator (Engish).

🧠 What It Does

Given a sticker image (e.g., a cartoon character shrugging, laughing, or making a gesture), the model outputs search queries that people might use to find or express the intent behind that sticker—such as:

• "whatever"
• "ugh not again"
• "mood"
• "shrug emoji"

It captures subtle social, emotional, and contextual cues—something that traditional vision-language models often fail to represent due to lack of cultural grounding.

🔍 Use Cases

• Improving sticker search and retrieval in chat apps
• Enhancing semantic understanding in multimodal recommendation systems
• Cultural and emotional alignment in vision-language modeling
• Dataset pre-labeling or enrichment

🗂 Dataset

This model was trained on StickerQueries, a multilingual dataset of over 60 hours of human-annotated sticker-query pairs in English and Chinese. Each annotation was reviewed by at least two people to ensure quality and consistency.

🚀 Inference

from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests

# Load model
processor = AutoProcessor.from_pretrained("metchee/sticker-query-generator-en")
model = AutoModelForVision2Seq.from_pretrained("metchee/sticker-query-generator-en")

# Run inference
image = Image.open("sticker.png")
inputs = processor(images=image, return_tensors="pt")
output = model.generate(**inputs)
query = processor.decode(output[0], skip_special_tokens=True)
print(query)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• num_epochs: 4.0

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.52.1
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1

Citations

@misc{huggingface-sticker-queries,
  author = {Heng Er Metilda Chee, et al.},
  title = {Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/metchee/sticker-queries}},
}