Duplicate from ScaleMath/GeoReasoning

Co-authored-by: Leonard Xin <LeonardXin@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- image-to-text
+language:
+- en
+pretty_name: GeoReasoning
+size_categories:
+- 10K<n<100K
+---
+
+[[💻 Github](https://github.com/MachinePhoenix/GeoReasoning)] [[🌐 Homepage](https://machinephoenix.github.io/GeoReasoning_blog/)]  [[📖 Paper](https://arxiv.org/abs/2509.15217)]
+
+## Dataset Description
+
+GeoReasoning is a vision-language dataset designed for geometric reasoning tasks, containing 10,000 high-quality image-text pairs with detailed geometric reasoning annotations.
+
+
+## 🚀 Data Usage
+
+### Data Loading
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ScaleMath/GeoReasoning")
+print(dataset)
+```
+### Data Training
+ ```python
+from datasets import load_dataset
+from transformers import AutoProcessor, AutoModelForVision2Seq, TrainingArguments, Trainer
+from PIL import Image
+import torch
+
+dataset = load_dataset("ScaleMath/GeoReasoning")
+processor = AutoProcessor.from_pretrained("your_model_path")
+
+def preprocess_function(examples):
+    images = [Image.open(img_path).convert('RGB') for img_path in examples["image"]]
+    
+    texts = []
+    for conv in examples["conversations"]:
+        text = ""
+        for turn in conv:
+            role = "USER" if turn["from"] == "human" else "ASSISTANT"
+            text += f"{role}: {turn['value']}\n"
+        texts.append(text)
+    
+    inputs = processor(
+        text=texts,
+        images=images,
+        padding=True,
+        return_tensors="pt"
+    )
+    
+    inputs["labels"] = inputs["input_ids"].clone()
+    return inputs
+
+tokenized_dataset = dataset.map(preprocess_function, batched=True)
+
+model = AutoModelForVision2Seq.from_pretrained(
+    "your_model_path",
+    torch_dtype=torch.float16
+)
+
+training_args = TrainingArguments(
+    output_dir="your_output_path",
+    per_device_train_batch_size=8,
+    num_train_epochs=3,
+    learning_rate=1e-5,
+    logging_steps=1,
+    save_steps=100,
+    remove_unused_columns=False,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    data_collator=lambda data: dict((key, [d[key] for d in data]) for key in data[0]),
+)
+
+trainer.train()
+```
+
+
+
+## 📑 Citation
+
+If you find this benchmark useful in your research, please consider citing this BibTex:
+
+```
+@misc{xin2025generalizablegeometricimagecaption,
+      title={Generalizable Geometric Image Caption Synthesis}, 
+      author={Yue Xin and Wenyuan Wang and Rui Pan and Ruida Wang and Howard Meng and Renjie Pi and Shizhe Diao and Tong Zhang},
+      year={2025},
+      eprint={2509.15217},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2509.15217}, 
+}
+```

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