Update model card for TypeGround paper

README.md

@@ -25,36 +25,9 @@ datasets:
 
 Model weights for the paper **"TypeGround: Fine-Grained Benchmarking for TypeScript Type Inference"**.
 
-> **Abstract:** TypeScript is widely used in web applications and mini-programs, yet real-world projects still contain many variables, function parameters, and return values whose annotations are missing or inferred as coarse-grained types (e.g., `any`). Automatic type inference requires benchmarks with reliable training and evaluation signals. Existing TypeScript datasets (e.g., ManyTypes4TypeScript), however, still contain compiler-uncheckable type expressions, coarse annotations, insufficient context, and evaluation protocols that deviate from compiler semantics, which threatens the reliability of both the training labels and the evaluation signals derived from them. To mitigate the above limitation, we present TypeGround, an automatically constructed TypeScript type inference benchmark. The core idea of TypeGround is to leverage the type-checking capability of the TypeScript Compiler (TSC), together with the generative capability of large language models, to produce compiler-friendly type annotations while avoiding coarse-grained types. TypeGround covers 2,735 projects, 99,209 source files, and 1,428,997 type annotations. Experiments show that training on TypeGround improves overall EM-based MRR@5 by 23.55 percentage points on average over ManyTypes4TypeScript. The user study further confirms the reliability of TypeGround's automatic labelling process.
->
-> **Dataset:** [github.com/fumx66/TypeGround](https://github.com/fumx66/TypeGround)
+> [TypeGround](https://github.com/fumx66/TypeGround)
 
-## 📦 Repository Structure
-
-```
-TypeGround_weight/
-├── CodeT5/                      # Full fine-tune (T5ForConditionalGeneration)
-│   ├── TypeGround/
-│   └── ManyTypes4TypeScrip/
-├── CodeT5+/                     # Full fine-tune (T5ForConditionalGeneration)
-│   ├── TypeGround/
-│   └── ManyTypes4TypeScrip/
-├── UniXcoder/                   # Full fine-tune (UniXcoder)
-│   ├── TypeGround/
-│   └── ManyTypes4TypeScrip/
-├── Llama3-8B/                   # LoRA adapters
-│   ├── ManyTypes4TypeScrip/lora/sft/
-│   └── TypeGround/{slice,snippet}/lora/sft/
-├── Qwen3-14B/                   # LoRA adapters
-│   ├── ManyTypes4TypeScrip/lora/sft/
-│   └── TypeGround/{slice,snippet}/lora/sft/
-├── DeepSeek-Coder-6.7B/         # LoRA adapters
-│   ├── ManyTypes4TypeScrip/lora/sft/
-│   └── TypeGround/{slice,snippet}/lora/sft/
-└── prediction.py                # Batch prediction script
-```
-
-## 🚀 Usage
+## Usage
 
 ### Traditional Models (Full Fine-tune)
 
@@ -90,7 +63,7 @@ vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
 python prediction.py
 ```
 
-## 📊 Models
+## Models
 
 | Model | Architecture | Type | LoRA Config |
 |---|---|---|---|
@@ -101,7 +74,7 @@ python prediction.py
 | Qwen3-14B | CausalLM + LoRA | Adapter | rank=8, α=16 |
 | DeepSeek-Coder-6.7B | CausalLM + LoRA | Adapter | rank=8, α=16 |
 
-## 📝 Citation
+## Citation
 
 ```bibtex
 @inproceedings{typeground,
@@ -113,6 +86,6 @@ python prediction.py
 }
 ```
 
-## 📄 License
+## License
 
 MIT License