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Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/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.
⚖️ Indian Legal Knowledge Base (v3.0)
Complete Structured JSON Corpus: 112 Files | 102 Primary Acts & Codes | 12,174 Sections
The definitive, authoritative, machine-readable statutory corpus of India — featuring the complete 2026 Constitution of India, modern 2023 Criminal Codes (BNS, BNSS, BSA), historical repealed codes (IPC, CrPC, IEA), unified labor codes, direct taxation (Income-tax Act, 2025), securities regulations (SEBI & SCRA), telecommunications, and 92 specialized statutes across 13 legal domains.
🔗 Ecosystem Navigation
- 📜 Companion PDF Dataset: For original Gazette of India and India Code source PDF publications, visit Indian Legal Knowledge Base (Official Source PDFs).
- ⚡ Live Model Context Protocol (MCP) Server: Connect this corpus directly to Claude, ChatGPT, Cursor, or any agentic system using the Indian Legal MCP Server.
📚 What is Included?
The repository contains 112 structured data files in raw_acts/ plus the master consolidated file sections.json:
- Master Consolidated File:
sections.json(19.0 MB) containing all 12,174 sections across all 102 statutes with uniform keys (act_code,chapter_number,chapter_title,section_number,section_title,text). - 102 Individual Statute Files: Structured JSON for each statutory enactment with clean Roman chapters and sections.
- 6 Criminal Concordance Mapping Tables: Bi-directional section-to-section crosswalks:
bns_to_ipc.json&ipc_to_bns.json(Bharatiya Nyaya Sanhita $\leftrightarrow$ Indian Penal Code)bnss_to_crpc.json&crpc_to_bnss.json(Bharatiya Nagarik Suraksha Sanhita $\leftrightarrow$ Code of Criminal Procedure)bsa_to_iea.json&iea_to_bsa.json(Bharatiya Sakshya Adhiniyam $\leftrightarrow$ Indian Evidence Act)
- 4 Constitutional Data Files:
CONSTITUTION_DATASET.json&constitution_of_india.jsonconstitution_of_india_articles.jsonl(Articles 1–395 & Preamble)constitution_of_india_schedules.jsonl(All 12 Schedules)constitution_of_india_appendices.jsonl(All 3 Appendices)
🏛️ Domain Breakdown Summary (102 Acts, 12,174 Sections)
| Category / Domain | Statutes Count | Total Sections |
|---|---|---|
| Criminal Law & Penal Statutes | 14 | 2,784 |
| Constitutional Law & Public Administration | 9 | 992 |
| Commercial, Corporate & Financial Law | 14 | 1,731 |
| Banking, Finance & Revenue | 16 | 2,181 |
| Civil Procedure, Contracts & Property | 9 | 1,197 |
| Unified Labor Codes | 5 | 505 |
| Cyber, Technology, Telecom & Identity | 4 | 297 |
| Intellectual Property Rights (IPR) | 5 | 586 |
| Family & Personal Laws | 10 | 661 |
| Environmental & Green Law | 6 | 385 |
| Public Health, Healthcare & Food Safety | 3 | 315 |
| Social Protection, Education & Human Welfare | 6 | 352 |
| Energy, Infrastructure & Public Utilities | 1 | 188 |
| Total | 102 | 12,174 |
📊 Usage in Python
import json
# Load master consolidated corpus (12,174 sections)
with open("sections.json", "r", encoding="utf-8") as f:
corpus = json.load(f)
print(f"Total sections loaded: {len(corpus)}")
# Query provisions for a specific statute (e.g. SEBI Act, 1992)
sebi_sections = [s for s in corpus if s["act_code"] == "SEBI"]
print(f"SEBI Act sections: {len(sebi_sections)}")
⚖️ Legal Disclaimer
Important Notice: This dataset is provided strictly for academic research, legal NLP, RAG benchmarks, and informational purposes. While verified against official government gazettes and India Code repositories, it does not constitute formal legal advice. For active litigation, commercial contracts, or compliance filings, always consult licensed advocates and cross-verify with the official Gazette of India (indiacode.nic.in).
👤 Curator & Citation
Curated with dedication by GSMS-B.
@dataset{indian_legal_knowledge_base_2026,
author = {GSMS-B},
title = {Indian Legal Knowledge Base: Complete Structured JSON Corpus of 102 Indian Acts and Codes (112 Structured Files, 12,174 Sections)},
year = {2026},
publisher = {Hugging Face},
version = {3.0.0},
url = {https://huggingface.co/datasets/GSMS-B/indian-legal-knowledge-base-json}
}
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