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metchee/
sticker-query-generator-en

Name: sticker-query-generator-en
Availability: InStock

generated_from_trainer

llava-hf/llava-1.5-7b-hf

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Sticker Query Generator (English) 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 Chinese, see here.

🧠 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"

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

python

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

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}},
}