The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
YAML Metadata Warning:The task_categories "structure-prediction" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
ECHR Annotated Corpus
A corpus of 289 European Court of Human Rights judgments (English) with hierarchical structural annotations produced via an AI-assisted 4-task pipeline.
Contents
Each case includes:
| File | Description |
|---|---|
meta.json |
Case metadata: docname, date, respondent, doctype, ECLI, importance |
paragraphs.json |
Raw paragraphs extracted from HUDOC HTML |
html.html |
Original HUDOC HTML |
state.json |
Annotation state and per-task costs |
task1/ |
L1 heading detection (suggestions, decisions, final) |
task2/ |
Quote and numbered-paragraph detection |
task3/ |
Sub-heading classification |
task4/ |
5-segment mapping (preamble, facts, law, conclusion, post-conclusion) |
The annotation pipeline produces a unified hierarchy:
segments[] → headings[] (L1-L5) → numbered paragraphs[] → quotes[]
Task internals are preserved for reproducibility — anyone can rebuild the final hierarchy or re-run downstream analysis.
Annotation methodology
- L1 heading detection — regex + AI for canonical sections (INTRODUCTION, THE FACTS, THE LAW, FOR THESE REASONS, etc.)
- Quote detection — numbered-paragraph spine + between-spine classification
- Sub-heading classification — Claude Haiku assigns L2-L6 levels per L1
- Segment mapping — deterministic 5-segment mapping from L1 headings
Each task has explicit suggestions (AI/deterministic) → decisions (human
review) → final (committed) provenance.
Loading the dataset
git clone <project-repo> echr-project
cd echr-project
pip install -r requirements.txt
# Pull the corpus from HuggingFace
python scripts/bootstrap.py
# Tell the apps where to read from
export ECHR_DATA_DIR=$(pwd)/data
# Start the viewer
cd experiments/viewer && python server.py
# Open http://127.0.0.1:5092
Schema
meta.json
{
"itemid": "001-249367",
"docname": "CASE OF MAKKI v. DENMARK",
"judgementdate": "2025-12-15",
"respondent": "DNK",
"doctypebranch": "CHAMBER",
"importance": "2",
"ecli": "ECLI:CE:ECHR:2025:1215JUD003161818"
}
paragraphs.json
Array of paragraph objects:
[
{"index": 0, "tag": "p", "text": "...", "char_count": 134},
...
]
task<N>/final.json
Task-specific schemas. See the project repository for details.
Source
Judgments retrieved from HUDOC — the official ECHR case-law database. Original judgment texts are public-domain works of the European Court of Human Rights.
License
MIT — annotations and processing code. ECHR judgments themselves are public-domain works of the European Court of Human Rights.
Acknowledgements
Annotation produced via a Claude-assisted pipeline (Anthropic) with human review.
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