Error code:   ConfigNamesError
Exception:    ValueError
Message:      Feature type 'string' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf', 'Nifti', 'Json']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1029, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 682, in get_module
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "<string>", line 20, in __init__
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 170, in __post_init__
                  self.features = Features.from_dict(self.features)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1983, in from_dict
                  obj = generate_from_dict(dic)
                        ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1564, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1570, in generate_from_dict
                  raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
              ValueError: Feature type 'string' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf', 'Nifti', 'Json']

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

AV-QuantBench

AV-QuantBench is a procedural audio-visual benchmark for evaluating multimodal foundation models on abstract temporal reasoning, cross-modal conflict detection, and synchronized data interpretation across finance, medical, and industrial domains.

This Hugging Face dataset repository is structured as a benchmark-style release. It contains:

split metadata in JSONL format,
question-answer annotations,
audio-visual sample assets,
manifest files by domain,
and documentation for schema and responsible use.

The repository is organized so that newly generated benchmark outputs can be added with minimal restructuring. In particular, the samples/ directory follows the same domain-first layout as the AV-QuantBench generator outputs.

Dataset Summary

AV-QuantBench converts time-series signals into synchronized visual topology and acoustic momentum. Each sample is paired with machine-generated QA derived from deterministic state-machine triggers. The benchmark is designed to evaluate whether a model can jointly reason over audio and video when the two modalities either align or intentionally diverge.

Supported Tasks

Cross-modal conflict detection
Audio-visual temporal reasoning
Multimodal question answering on synthetic data videos
Robustness evaluation under counterfactual splicing

Modalities

Video (.mp4)
Audio (.wav)
Structured annotations (.json, .jsonl)

Domains

Finance
Medical monitoring
Industrial IoT

Repository Structure

AV-QuantBench-HF-Dataset/
├── README.md
├── LICENSE
├── .gitattributes
├── dataset_infos.json
├── metadata/
│   ├── train.jsonl
│   ├── val.jsonl
│   └── test.jsonl
├── qa/
│   ├── train.jsonl
│   ├── val.jsonl
│   └── test.jsonl
├── manifests/
│   ├── finance.jsonl
│   ├── medical.jsonl
│   └── iiot.jsonl
├── samples/
│   ├── finance/
│   │   ├── videos/
│   │   ├── audio/
│   │   ├── qa/
│   │   └── metadata/
│   ├── medical/
│   │   ├── videos/
│   │   ├── audio/
│   │   ├── qa/
│   │   └── metadata/
│   └── iiot/
│       ├── videos/
│       ├── audio/
│       ├── qa/
│       └── metadata/
└── docs/
    ├── schema.md
    └── responsible_use.md

Downloads last month: 359

Total file size:

3.5 GB