ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
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Hi ! `datasets` uses Arrow under the hood which expects each column and array to have fixed types that don't change across rows of a dataset, which is why we get this error. This dataset in particular doesn't have a format compatible with Arrow unfortunately. Don't hesitate to open a discussion or PR on HF to fix the dataset
2025-01-03T15:48:54Z
https://github.com/huggingface/datasets/issues/7322#issuecomment-2569445988
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@lhoestq Is it correct to assume that most multimodal datasets with variable number of images across conversations are not compatible with Arrow ? 

I’m running into a problem while trying to format multimodal datasets (image + text) using the Hugging Face datasets library. Specifically, I’m working with a structure where conversations include both images and text messages. When I convert my dataset from a Python dict to a Hugging Face Dataset, I’m seeing unexpected None values being inserted for some fields that aren’t relevant for a given message (e.g., "text": None in image messages). Here’s what seems to be happening:

🔍 What’s going wrong

Hugging Face datasets (backed by Apache Arrow) tries to flatten the schema across all samples. That means it enforces a fixed set of fields across the dataset – even if some of them are None for a given entry. Since my dataset contains heterogeneous conversation messages (some with images, others with text), Arrow is injecting None for whichever attributes don’t exist in each message type to preserve a unified schema.

This results in a lot of meaningless or misleading Nones across the dataset, and breaks logic further down the pipeline – in particular, it causes the qwen_vl_utils functions (like process_vision_info) to crash or misbehave.


🤔 Workarounds considered

I could stick with raw JSON, which preserves the heterogeneous structure properly. But that means giving up all the nice features of datasets and Arrow (e.g. streaming, map/filter, etc.), which feels like a shame.

❓My question

Do you know of a clean way to define a flexible schema with Hugging Face Datasets – maybe using nested structures or dynamic fields – so that each conversation message doesn’t get forced into a flat structure with irrelevant keys? Or should I just stick with JSON for this kind of multimodal case?

Let me know if you’ve run into this before or have any tips!

2025-07-15T12:17:33Z
https://github.com/huggingface/datasets/issues/7322#issuecomment-3073377211
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Datasets without fixed types are harder to use in many data frameworks unfortunately. You will have to handle the case with None values if you want to use Arrow/`datasets` (or any other framework using Arrow like spark, ray, dask). **IMO the short term solution is to fix qwen_vl_utils / process_vision_info.**

Alternatively we can explore adding the Arrow `Json` type to `datasets`, but doesn't allow having image types in the Json object so I don't think this is the right solution.
2025-07-15T12:58:31Z
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I totally share the analysis, I am happy to try to help with this! I am currently using Unsloth so it is the `UnslothVisionDataCollator`  which has almost the same logic as `qwen_vl_utils.process_vision_info`. Is there a Huggingface class I could patch too ? 
2025-07-15T13:06:39Z
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2025-07-15T13:06:55Z
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### Describe the bug

Encountering an error while loading the ```liuhaotian/LLaVA-Instruct-150K dataset```.

### Steps to reproduce the bug

```
from datasets import load_dataset
fw =load_dataset("liuhaotian/LLaVA-Instruct-150K")
```
Error:
```
ArrowInvalid                              Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py](https://localhost:8080/#) in _generate_tables(self, files)
    136                                 try:
--> 137                                     pa_table = paj.read_json(
    138                                         io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)

20 frames
ArrowInvalid: JSON parse error: Column() changed from object to array in row 0

During handling of the above exception, another exception occurred:

ArrowTypeError                            Traceback (most recent call last)
ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column id with type object')

The above exception was the direct cause of the following exception:

DatasetGenerationError                    Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
   1895             if isinstance(e, DatasetGenerationError):
   1896                 raise
-> 1897             raise DatasetGenerationError("An error occurred while generating the dataset") from e
   1898 
   1899         yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)

DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior

I have tried loading the dataset both on my own server and on Colab, and encountered errors in both instances.

### Environment info

```
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.3
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
```
