Update README.md

README.md

@@ -76,102 +76,6 @@ Pre-trained reranker models for dense reranking stage:
 - Purpose: Fine-grained reranking of recalled candidates
 - Format: PyTorch checkpoint files (`.pth`)
 
-## 🚀 Quick Start
-
-### Download Specific Components
-
-#### Using Python Script
-
-```bash
-# Download everything
-python download_features.py --all
-
-# Download only InternVideo2 features for specific datasets
-python download_features.py --features --datasets msrvtt actnet
-
-# Download GRDR checkpoints only
-python download_features.py --grdr
-
-# Download Xpool reranker only
-python download_features.py --xpool --datasets msrvtt
-```
-
-#### Using Hugging Face CLI
-
-```bash
-# Download entire dataset
-huggingface-cli download JasonCoderMaker/GRDR-TVR --repo-type dataset --local-dir ./GRDR-TVR
-
-# Download specific component
-huggingface-cli download JasonCoderMaker/GRDR-TVR InternVideo2/msrvtt --repo-type dataset --local-dir ./features
-
-# Download GRDR checkpoint for MSR-VTT
-huggingface-cli download JasonCoderMaker/GRDR-TVR GRDR/msrvtt --repo-type dataset --local-dir ./checkpoints
-```
-
-### Load Features in Python
-
-```python
-import pickle
-from huggingface_hub import hf_hub_download
-
-# Download and load InternVideo2 features
-feature_file = hf_hub_download(
-    repo_id="JasonCoderMaker/GRDR-TVR",
-    filename="InternVideo2/msrvtt/msrvtt_internvideo2.pkl",
-    repo_type="dataset"
-)
-
-with open(feature_file, 'rb') as f:
-    video_features = pickle.load(f)
-    
-# Access features
-video_id = "video7015"
-embedding = video_features[video_id]  # Shape: (512,)
-print(f"Feature shape: {embedding.shape}")
-```
-
-### Load GRDR Model
-
-```python
-import torch
-from huggingface_hub import hf_hub_download
-
-# Download checkpoint
-checkpoint_path = hf_hub_download(
-    repo_id="JasonCoderMaker/GRDR-TVR",
-    filename="GRDR/msrvtt/best_model/best_model.pt",
-    repo_type="dataset"
-)
-
-# Load model
-checkpoint = torch.load(checkpoint_path, map_location='cpu')
-print(f"Available keys: {checkpoint.keys()}")
-
-# Use with your GRDR model
-from models.grdr import GRDR
-model = GRDR(...)
-model.load_state_dict(checkpoint['model'], strict=False)
-```
-
-### Load Xpool Reranker
-
-```python
-import torch
-from huggingface_hub import hf_hub_download
-
-# Download reranker checkpoint
-reranker_path = hf_hub_download(
-    repo_id="JasonCoderMaker/GRDR-TVR",
-    filename="Xpool/msrvtt9k_model_best.pth",
-    repo_type="dataset"
-)
-
-# Load reranker
-checkpoint = torch.load(reranker_path, map_location='cpu')
-# Use with your Xpool model
-```
-
 ## 📂 Repository Structure
 
 ```
@@ -203,79 +107,6 @@ GRDR-TVR/
     └── msrvtt9k_model_best.pth
 ```
 
-## 🔬 Dataset Statistics
-
-| Dataset | Videos | Train Queries | Test Queries | Feature Size | GRDR Size | Xpool Size |
-|---------|--------|---------------|--------------|--------------|-----------|------------|
-| MSR-VTT | 10,000 | 9,000 | 1,000 | 932 MB | 494 MB | 1.8 GB |
-| ActivityNet | 20,000 | 10,009 | 4,917 | 1.1 GB | 498 MB | 1.8 GB |
-| DiDeMo | 10,464 | 8,395 | 1,065 | 916 MB | 504 MB | 1.8 GB |
-| LSMDC | 118,081 | 118,081 | 1,000 | 424 MB | 478 MB | 1.8 GB |
-| **Total** | - | - | - | **3.4 GB** | **2.0 GB** | **7.2 GB** |
-
-## 🎯 Performance
-
-GRDR achieves competitive accuracy with dense retrievers while being significantly more efficient:
-
-| Dataset | R@1 | R@5 | R@10 | Storage Reduction | Speed-up |
-|---------|-----|-----|------|-------------------|----------|
-| MSR-VTT | 45.2 | 72.1 | 81.3 | 15.6× | 287× |
-| ActivityNet | 41.8 | 76.2 | 86.4 | 12.3× | 310× |
-| DiDeMo | 43.1 | 71.8 | 81.7 | 18.2× | 265× |
-| LSMDC | 24.3 | 48.9 | 59.2 | 22.1× | 298× |
-
-*Compared to CLIP4Clip baseline with exhaustive search*
-
-## 💻 Usage in GRDR Pipeline
-
-### Complete Retrieval Pipeline
-
-```python
-from models.grdr import GRDR
-from reranker.xpool import XpoolReranker
-import torch
-
-# 1. Load video features
-video_features = load_internvideo2_features("msrvtt")
-
-# 2. Load GRDR model for recall
-grdr_model = GRDR.from_pretrained("JasonCoderMaker/GRDR-TVR", dataset="msrvtt")
-
-# 3. Generate candidates (fast generative recall)
-query = "A person playing guitar"
-candidates = grdr_model.generate_candidates(query, top_k=100)
-
-# 4. Load Xpool reranker
-reranker = XpoolReranker.from_pretrained("JasonCoderMaker/GRDR-TVR", dataset="msrvtt")
-
-# 5. Rerank candidates (dense reranking)
-final_results = reranker.rerank(query, candidates, top_k=10)
-```
-
-## 🛠️ Requirements
-
-```bash
-pip install torch torchvision
-pip install transformers>=4.30.0
-pip install huggingface_hub
-pip install sentencepiece
-```
-
-For the full GRDR codebase, see: [GitHub Repository](https://github.com/JasonCoderMaker/GRDR)
-
-## 📜 Citation
-
-If you use this dataset or models in your research, please cite:
-
-```bibtex
-@inproceedings{grdr2026,
-  title={Generative Recall, Dense Reranking: Learning Multi-View Semantic IDs for Efficient Text-to-Video Retrieval},
-  author={Anonymous},
-  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
-  year={2026}
-}
-```
-
 ## 📝 License
 
 This dataset is released under the MIT License. See [LICENSE](LICENSE) for details.
@@ -288,11 +119,6 @@ The video datasets (MSR-VTT, ActivityNet, DiDeMo, LSMDC) are subject to their or
 - **Xpool**: The reranker architecture is based on X-POOL
 - **Datasets**: MSR-VTT, ActivityNet Captions, DiDeMo, and LSMDC benchmark creators
 
-## 📧 Contact
-
-For questions or issues, please open an issue on the [GitHub repository](https://github.com/JasonCoderMaker/GRDR) or contact the authors.
-
----
 
 **Dataset Version**: 1.0  
 **Last Updated**: January 2026