Datasets:

Valen-Team
/

Valen-Training-General-100k

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<record_id: string, source: string, source_repo: string, source_revision: string, source_id: string, source_split: string, split: string, domain: string, modality: string, language_bucket: string, license: string, label_source: string, label_semantics: string, generator_version: string, human_audit: string, question_type: string, answer_type: string, gqa_types: struct<structural: string, semantic: string, detailed: string>>
to
{'record_id': Value('string'), 'source': Value('string'), 'source_repo': Value('string'), 'source_revision': Value('string'), 'source_id': Value('string'), 'source_split': Value('string'), 'split': Value('string'), 'domain': Value('string'), 'modality': Value('string'), 'language_bucket': Value('string'), 'license': Value('string'), 'label_source': Value('string'), 'label_semantics': Value('string'), 'generator_version': Value('string'), 'human_audit': Value('string'), 'gqa_types': {'structural': Value('string'), 'semantic': Value('string'), 'detailed': Value('string')}}
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<record_id: string, source: string, source_repo: string, source_revision: string, source_id: string, source_split: string, split: string, domain: string, modality: string, language_bucket: string, license: string, label_source: string, label_semantics: string, generator_version: string, human_audit: string, question_type: string, answer_type: string, gqa_types: struct<structural: string, semantic: string, detailed: string>>
              to
              {'record_id': Value('string'), 'source': Value('string'), 'source_repo': Value('string'), 'source_revision': Value('string'), 'source_id': Value('string'), 'source_split': Value('string'), 'split': Value('string'), 'domain': Value('string'), 'modality': Value('string'), 'language_bucket': Value('string'), 'license': Value('string'), 'label_source': Value('string'), 'label_semantics': Value('string'), 'generator_version': Value('string'), 'human_audit': Value('string'), 'gqa_types': {'structural': Value('string'), 'semantic': Value('string'), 'detailed': Value('string')}}
              
              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 1694, 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 1880, 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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System One Model, now with vision.

Valen-Training-General-100k

GitHub · Preview model · General evaluation · Technical notes

✨ Introduction

Valen brings visual perception to System One decision-making: text, images or video in, candidate probabilities out. This dataset provides 100,000 image-based decision records for supervised training and decision-head learning in Valen, spanning visual question answering, interfaces, games and documents.

Each record contains one decision question, a target probability distribution, local image references and provenance metadata. All records are marked as English in the supplied metadata. Use Valen-Eval-General-5k for companion evaluation; this repository is the training split.

📥 Dataset downloads

Dataset	Role	Download
Valen-Training-General-100k	100k training records	🤗 This repository
Valen-Eval-General-5k	5k evaluation records	🤗 Hugging Face
Valen-Eval-Game	Sokoban training and evaluation	🤗 Hugging Face

Images are packaged in assets.zip. Extract it before loading records; the relative image paths and data_manifest.json refer to the extracted files.

📊 Dataset composition

Records / questions: 100,000 / 100,000.
Referenced image files: 50,203; unique image SHA-256 hashes: 48,445.
Distinct groups: 48,445. Multiple questions can share an image or group.
Domains: vqa: 33,334, ui: 28,572, game: 19,047, document: 19,047.
Local split metadata: meta.split = "train".

Question type	Records
`noul`	8,109
`choice`	91,409
`score`	482

noul is a binary judgment with implicit candidate keys true and false. choice maps candidate IDs to answer descriptions in criteria. score uses an ordered list of descriptions; target keys are the zero-based index strings ("0", "1", ...). All supplied targets are one-hot distributions; these are finite-candidate decisions rather than free-form text targets.

🚀 Quick start

Download the dataset and extract its image archive:

hf download Valen-Team/Valen-Training-General-100k \
  --repo-type dataset --local-dir data/Valen-Training-General-100k
unzip -n data/Valen-Training-General-100k/assets.zip -d data/Valen-Training-General-100k

After downloading the repository with its image files, use Python's JSON reader. This preserves the heterogeneous criteria field, which is a mapping for choice questions, a list for score questions and absent for binary questions. Automatic Arrow/JSON loading may require normalization of that field.

import json
from pathlib import Path
from PIL import Image

root = Path("data/Valen-Training-General-100k")
with (root / "train.jsonl").open(encoding="utf-8") as stream:
    record = json.loads(next(stream))

request = record["request"]
question = request["questions"]["decision"]
probabilities = record["targets"]["decision"]["probabilities"]
correct_candidate = max(probabilities, key=probabilities.get)
with Image.open(root / record["assets"][0]["path"]) as image:
    print(image.size, question["type"], correct_candidate)

For candidate-selection evaluation, compare the predicted candidate ID against the target distribution, and report overall accuracy together with per-source and per-question-type accuracy. Score questions also allow ordered-error metrics when explicitly defined; noul outputs may be evaluated as probabilities when calibration is relevant.

For training with this dataset, follow the Valen training guide. Set the training config's data to the downloaded train.jsonl; keep its extracted assets/ directory alongside it.

Record format

Field	Meaning
`request.state.messages`	Visible text and image inputs, with relative `image_url.url` paths
`request.questions.decision`	Question type, instructions and optional candidate criteria
`targets.decision.probabilities`	Target probabilities keyed by candidate ID
`assets`	Media path, SHA-256, source and original asset identifier
`group_id`	Grouping key for split isolation
`meta`	Record ID, provenance, domain, modality, language, split and audit metadata

Only request is model input. Keep targets, grouping identifiers and provenance outside the model prompt.

Repository layout

train.jsonl           # Complete 100,000-record training file
<source>.jsonl        # Ten per-source subsets of train.jsonl
assets/<source>/...   # Images referenced by relative path
dataset_info.json     # Counts, source metadata and validation results
data_manifest.json    # SHA-256 and byte size of every supplied data file
README.md
card-assets/         # Dataset-card branding; not part of the training data

The original filename train.jsonl is preserved for compatibility. It is the training split. The complete train.jsonl is the canonical entry point: its group IDs have been normalized, while some per-source files retain original grouping prefixes. The number of such differences is recorded in dataset_info.json; all other record fields match. Per-source files contain the same examples as the complete file: read either train.jsonl or the source subsets, not both.

All image paths are relative to the repository root. The image files are stored in the ZIP archives listed below; extract them before using these relative paths. The dataset payload occupies approximately 6.32 GiB, including both the complete JSONL and its per-source copies.

Sources and provenance

The table reproduces source identifiers and license strings from the supplied record metadata; it is not an independent verification of upstream licensing or label quality.

Source	Records	Recorded source repository	Recorded license / terms
gqa	11,428	lmms-lab-encoder/GQA	MIT
vqav2	9,524	Multimodal-Fatima/VQAv2_train	upstream_VQAv2_terms
textvqa	7,619	lmms-lab-encoder/textvqa	upstream_TextVQA_terms
rico_screenqa	21,905	bevaya/RICO-ScreenQA	CC-BY-4.0
showui_desktop	6,667	showlab/ShowUI-desktop	upstream_ShowUI_OmniAct_terms
gameqa	19,047	OpenMOSS-Team/GameQA-140K	MIT
docvqa	9,524	pixparse/docvqa-single-page-questions	MIT_mirror_and_upstream_DocVQA_terms
chartqa	9,523	HuggingFaceM4/ChartQA	GPL-3.0
clevr	2,857	laion/clevr-webdataset	CC-BY-4.0_upstream
programmatic	1,906	visionjev/programmatic	project-owned

Upstream identifiers, revisions, source splits and label provenance are retained in each record's meta fields where provided. meta.source_split describes the upstream source partition; meta.split describes this assembled dataset partition. They need not have the same name.

Historical identifiers such as visionjev/programmatic and Visual-Jev-Training-General-100k remain in the original provenance and metadata files. The project and Hub repository are now named Valen; the data itself has not been renamed internally or relabeled.

Integrity and split isolation

Packaging validated all record schemas, unique record IDs, target distributions, referenced image paths and image SHA-256 hashes. Each per-source subset was checked against the corresponding records in the complete JSONL, allowing only the documented group-ID normalization difference. Across the 100k training and 5k evaluation repositories, overlap is 0 record IDs, 0 group IDs and 0 exact image SHA-256 hashes. This does not establish semantic or near-duplicate independence.

The complete file SHA-256 is:

5762c39f4440dcaa2443cc07773bb26e36ac499a68e87eeb35e609a91032788b

Counts and audit details are in dataset_info.json; data-file checksums are in data_manifest.json. The original JSONL records and images are preserved without relabeling or rewriting.

Image archives

Images are distributed as ZIP archives. Each archive preserves its folder prefix and all nested paths. The original image directories have been removed after verifying their contents against the archives. Extract the ZIP files to restore them. Record paths are unchanged; after downloading archives, extract them from the repository root before loading the JSONL data.

Archive	Image files	ZIP size
`assets.zip`	50,203	5,731.97 MiB

unzip -n assets.zip -d .

unzip -n preserves existing extracted files. image_archives.json records archive SHA-256 hashes, source sizes and file counts. Every member was decompressed and checked against the original data manifest by SHA-256; ZIP CRC checks also passed. ZIP64 is enabled for large archives.

🤝 Contributions

Contributions to Valen are welcome. Open an issue to report a problem, share a use case or discuss experimental results. Submit a pull request to improve the code or documentation, contribute training data or add evaluation tasks.

Scan the QR code below to join the Valen WeChat group, discuss the project and share your experiments.

QR code for the Valen WeChat discussion group

📄 Annotation status and terms

The supplied metadata marks 99,047 records as human_audit: pending and 953 as not_required. Packaging checks establish structural and file integrity, not independent semantic verification of every answer.

The repository retains its existing Apache-2.0 metadata tag. This does not replace the source-specific terms recorded above for third-party images and annotations. Source attribution and license metadata remain attached to the records.

Valen is built on Qwen3.5, with its decision interface inspired by TypeSafe's Jev. Project documentation and evaluation results are available in the GitHub README.

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Models trained or fine-tuned on Valen-Team/Valen-Training-General-100k

Valen-Team/Valen-Preview-0923

Image-Text-to-Text • Updated 12 days ago • 683 • 9

request dict	targets dict	assets list	group_id string	meta dict
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2376294-f79fe94a8637.jpg", "sha256": "f79fe94a863709de406500e49fe4f7576c228f214735f311d0407bac0ef337db", "source": "gqa", "parent_asset_id": "2376294" } ]	image:f79fe94a863709de406500e49fe4f7576c228f214735f311d0407bac0ef337db	{ "record_id": "gqa:12727996", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "12727996", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2339750-326942ecb1b9.jpg", "sha256": "326942ecb1b911ad39b31aeead51b0364a4acbf62e1dc5412942ea916254d085", "source": "gqa", "parent_asset_id": "2339750" } ]	image:326942ecb1b911ad39b31aeead51b0364a4acbf62e1dc5412942ea916254d085	{ "record_id": "gqa:10325890", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "10325890", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2374805-ce14343f2c59.jpg", "sha256": "ce14343f2c590b615f7cb0b72d28c964af4cf84ac2435e1d4266abb7d7828e91", "source": "gqa", "parent_asset_id": "2374805" } ]	image:ce14343f2c590b615f7cb0b72d28c964af4cf84ac2435e1d4266abb7d7828e91	{ "record_id": "gqa:00398553", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "00398553", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2377989-728a4e797dd4.jpg", "sha256": "728a4e797dd4447fd3727482675bad7eac728516f74ef83efc35b9f92529c351", "source": "gqa", "parent_asset_id": "2377989" } ]	image:728a4e797dd4447fd3727482675bad7eac728516f74ef83efc35b9f92529c351	{ "record_id": "gqa:04871653", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "04871653", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2328299-382ad7f3bf71.jpg", "sha256": "382ad7f3bf71bebff4ad1bd09cf1429d48ef4a3edcc5e9d10726524ade0e37e2", "source": "gqa", "parent_asset_id": "2328299" } ]	image:382ad7f3bf71bebff4ad1bd09cf1429d48ef4a3edcc5e9d10726524ade0e37e2	{ "record_id": "gqa:05348167", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "05348167", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2407444-6e8d368f1538.jpg", "sha256": "6e8d368f15382db3993da15f4e6375d326e0aedc5101906972c03665e3a98bed", "source": "gqa", "parent_asset_id": "2407444" } ]	image:6e8d368f15382db3993da15f4e6375d326e0aedc5101906972c03665e3a98bed	{ "record_id": "gqa:02478301", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "02478301", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2326514-53a6ce63b9ae.jpg", "sha256": "53a6ce63b9aeb8e9f2b82198b0ad94bbd1b1ae66db36e56c46516b133ac3795f", "source": "gqa", "parent_asset_id": "2326514" } ]	image:53a6ce63b9aeb8e9f2b82198b0ad94bbd1b1ae66db36e56c46516b133ac3795f	{ "record_id": "gqa:1027957", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "1027957", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT", ...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2415821-c2b94f96aed0.jpg", "sha256": "c2b94f96aed0386d1bff3408301d8e941f5b61df2bae451c6c2b843cfdc36e02", "source": "gqa", "parent_asset_id": "2415821" } ]	image:c2b94f96aed0386d1bff3408301d8e941f5b61df2bae451c6c2b843cfdc36e02	{ "record_id": "gqa:0716746", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "0716746", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT", ...
{ "state": { "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Answer using only the visible image and the stated question." }, { "type": "image_url", "image_url": { "url": "asse...	{ "decision": { "probabilities": { "true": 1, "false": 0 } } }	[ { "path": "assets/gqa/2358327-86c5da02fb3f.jpg", "sha256": "86c5da02fb3f62f273895e7cca1f30f9e0f7f675a9112480eed191c727656261", "source": "gqa", "parent_asset_id": "2358327" } ]	image:86c5da02fb3f62f273895e7cca1f30f9e0f7f675a9112480eed191c727656261	{ "record_id": "gqa:17669085", "source": "gqa", "source_repo": "lmms-lab-encoder/GQA", "source_revision": "a6e72d6e1b912da88af8b2f9eba05d5ea8ec2dd8", "source_id": "17669085", "source_split": "train", "split": "train", "domain": "vqa", "modality": "image", "language_bucket": "en", "license": "MIT",...
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