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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ValueError
Message:      Invalid string class label GeoX-data@4c78ad83d57fa3189e981f9b11b7d233e5b5486c
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1537, in _prepare_split_single
                  example = self.info.features.encode_example(record) if self.info.features is not None else record
                            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label GeoX-data@4c78ad83d57fa3189e981f9b11b7d233e5b5486c
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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End of preview.

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GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training

💡Github Page • 📃Paper • 🗂Dataset • 🤗Checkpoint • 📖Citation


Introduction to GeoX

GeoX is a multi-modal large model designed for automatic geometric problem solving, utilizing three progressive training stages to enhance diagram understanding and reasoning. In this paper, we validate that the formal vision-language training paradigm is a simple-yet-effective solution for complex mathematical diagram learning.

Data Preparation for GeoX

Step 1. Data for Unimodal Pre-training

You can download our collected diagram images from this link.

Additionally, we use existing geometric text to build a corpus, which is detailed in our paper.

Step 2. Data for Geometry-Language Alignment

To train the GS-Former, please prepare the unified formal annotations and paired images.

Step 3. Data for End-to-End Visual Instruction Tuning

We use the GeoQA, UniGeo, Geometry3K, and PGPS9K datasets for fine-tuning and evaluation:

  1. GeoQA: Follow the instructions here to download the GeoQA dataset.
  2. UniGeo: Follow the instructions here to download the UniGeo dataset.
  3. Geometry3K and PGPS9K: Follow the instructions here to download the PGPS9K datasets. The Geometry3K is also provided in this database.

Note: Due to copyright restrictions, we are currently only providing links for these datasets. Full datasets for tuning and evaluation organized by us will be provided via email. If you need it, please contact us by email.

For more details, please refer to our paper and GitHub repository. If you find our work helpful, please consider starring ⭐ in this repository and citing us:

@article{xia2024geox,
  title={GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training},
  author={Xia, Renqiu and Li, Mingsheng and Ye, Hancheng and Wu, Wenjie and Zhou, Hongbin and Yuan, Jiakang and Peng, Tianshuo and Cai, Xinyu and Yan, Xiangchao and Wang, Bin and others},
  journal={arXiv preprint arXiv:2412.11863},
  year={2024}
}
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