Add pipeline tag, library name and link to paper

README.md

@@ -1,8 +1,12 @@
 ---
+library_name: peft
 license: apache-2.0
-library_name: pytorch
+base_model: ibm-granite/granite-vision-4.1-4b
+pipeline_tag: image-text-to-text
 tags:
 - pytorch
+- lora
+- chart-understanding
 ---
 
 <a id="top"></a>
@@ -26,9 +30,9 @@ tags:
   </p>
 </div>
 
-These are the official implementation resources, model weights, and prediction files for **ChartLens**, our champion solution for **DataMFM Challenge Track 2: Chart Understanding** at CVPR 2026.
+These are the official implementation resources, model weights, and prediction files for **ChartLens**, the champion solution for **DataMFM Challenge Track 2: Chart Understanding** at CVPR 2026.
 
-🔗 **Paper:** [Arxiv](https://arxiv.org/pdf/2606.10640)  
+🔗 **Paper:** [ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement](https://huggingface.co/papers/2606.10640)  
 🔗 **GitHub Repository:** [iLearnLab/CVPRW26-ChartLens](https://github.com/iLearnLab/CVPRW26-ChartLens)  
 🔗 **Challenge Page:** [DataMFM Challenge](https://datamfm.github.io/challenge.html)
 
@@ -114,104 +118,6 @@ python code/infer_granite_with_lora.py \
 
 Use `code/infer_chartnet_granite.py` for base Granite Vision inference without a LoRA adapter.
 
-### Step 4: SAVC CSV Correction
-
-```bash
-export OPENAI_API_KEY="..."
-
-python code/calibrate_baseline_with_ai.py \
-  --split all \
-  --baseline_root /path/to/baseline_predictions \
-  --image_root /path/to/data \
-  --output_root /path/to/savc_output \
-  --base_url "https://your-openai-compatible-endpoint" \
-  --model gemini-3.5-flash \
-  --threshold 85
-```
-
-`--baseline_root` should contain split directories such as `real/` and `synthetic/`, each with `chart2csv_predictions.jsonl` and `chart2summary_predictions.jsonl`.
-
-### Step 5: TRSR Summary Refinement
-
-```bash
-python code/ocr.py \
-  --real_images /path/to/data/real/images \
-  --synthetic_images /path/to/data/synthetic/images \
-  --real_summary /path/to/baseline/real/chart2summary_predictions.jsonl \
-  --synthetic_summary /path/to/baseline/synthetic/chart2summary_predictions.jsonl \
-  --output_dir /path/to/ocr_text_copy_coverage \
-  --threshold 0.8
-
-export AIGCBEST_API_KEY="..."
-
-python code/repair_summary.py \
-  --split all \
-  --workers 20 \
-  --ocr_eval_root /path/to/ocr_text_copy_coverage \
-  --output_root /path/to/trsr_output
-```
-
-### Step 6: Training (Optional)
-
-Train the LoRA adapter on the prepared ChartNet SFT data:
-
-```bash
-python code/train_lora_chartnet.py \
-  --model_path /path/to/granite-vision-4.1-4b \
-  --train_jsonl Fine-tuning/Dataset/sft/train.jsonl \
-  --val_jsonl Fine-tuning/Dataset/sft/val.jsonl \
-  --output_dir Fine-tuning/FT/model/granite_chartnet_lora_bs2 \
-  --gpu_id 0 \
-  --epochs 2 \
-  --batch_size 1 \
-  --grad_accum 8
-```
-
----
-
-## 📦 Submission Format
-
-For DataMFM Track 2, organize the final predictions as:
-
-```bash
-submission.zip
-├── real/
-│   ├── chart2csv_predictions.jsonl
-│   └── chart2summary_predictions.jsonl
-└── synthetic/
-    ├── chart2csv_predictions.jsonl
-    └── chart2summary_predictions.jsonl
-```
-
-Each CSV prediction line:
-
-```json
-{"imagename": "example.png", "predicted_csv": "Header A,Header B\nA,1\nB,2"}
-```
-
-Each summary prediction line:
-
-```json
-{"imagename": "example.png", "predicted_summary": "One paragraph summary grounded in the chart."}
-```
-
----
-
-## ⚠️ Limitations & Notes
-
-**Disclaimer:** This framework and its model weights are intended for **academic research purposes only**.
-
-- Chart-to-CSV extraction may still struggle with dense layouts, asymmetric legends, or adjacent semantic-column misalignment.
-- Summary refinement depends on OCR quality; OCR errors can affect text-retention scoring and repair decisions.
-- GPU execution is expected for Granite Vision inference and LoRA training.
-- API-backed correction scripts require valid credentials and an OpenAI-compatible endpoint.
-
----
-
-## 🤝 Acknowledgements & Contact
-
-- **Contact:** If you have any questions or encounter issues, feel free to contact Hao Liu at liuh90210@gmail.com or Ruping Cao at caoruping657@gmail.com.
-
 ---
 
 ## 📝⭐️ Citation