Update README.md

README.md

@@ -11,13 +11,50 @@ model-index:
   results: []
 ---
 
-# sticker-query-generator-zh
+Sticker Query Generator (中文)
 
-This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the [StickerQueries (ZH)](https://huggingface.co/datasets/metchee/sticker-queries) dataset.
+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)](https://huggingface.co/metchee/sticker-query-generator-en).
 
-## Model description
+## 🧠 What It Does
 
-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.
+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](https://huggingface.co/datasets/metchee/sticker-queries), 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
 
@@ -38,13 +75,21 @@ The following hyperparameters were used during training:
 - 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
+- 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}},
+}
+```