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Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 237, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/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/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
document_id string | year int64 | text string | total_entities int64 | entities list |
|---|---|---|---|---|
1950_1_1008_1018_EN | 1,950 | Patntalal Jankid-:u
v, Mohanlal and
Another,
Pata1tjali S11stri J.
!950
Deo, 21.
SUPREME COURT REPORTS [1950]
of section 14, it seems to me, they would be bringing themselves under the bar of section 18 (2).
The respondents cannot therefore claim that the loss of the goods was explosion damage within the meanin... | 79 | [
{
"text": "Patntalal Jankid-:u",
"label": "OTHER_PERSON",
"start_char": 0,
"end_char": 19,
"source": "ner",
"metadata": {
"in_sentence": "Patntalal Jankid-:u\n\nv, Mohanlal and\n\nAnother,\n\nPata1tjali S11stri J.\n\n!"
}
},
{
"text": "S11",
"label": "PROVISION",
"s... |
1950_1_15_25_EN | 1,950 | S.C.R.
SUPREME COURT REPORTS
RAM KRISHNA RAMNATH AGARWAL
OF KAMPTEE v.
SECRETARY, MUNICIPAL COMMITTEE,
KAMP TEE.
UNION OF INDIA AND GOVERNMENT OF
MAD HY A PRADESH : INTERVENERS.
[SHRI HARILAL KANIA C.J., SAIYID FAZL ALI,
PATANJALI SASTRI, MEHR CHAND MAHAJAN, MuKHERJEA, and S.R. DAS JJ.]
Govl!rnment of India A... | 106 | [
{
"text": "RAM KRISHNA RAMNATH AGARWAL\n\nOF KAMPTEE",
"label": "PETITIONER",
"start_char": 31,
"end_char": 70,
"source": "metadata",
"metadata": {
"canonical_name": "RAM KRISHNA RAMNATH AGARWAL OF KAMPTEE",
"offset_not_found": false
}
},
{
"text": "SECRETARY, MUNICIP... |
1950_1_25_29_EN | 1,950 | S.C.R.
SUPREME COUR1' REPORTS 25
the octroi duty under the Municipal Act continues to be valid. On this point again the appellant's argument is that the levy of a duty at any stage of the manufacture of bidis out of tobacco would be the levy of the excise duty and therefore those provisions were contrary to the provi... | 69 | [
{
"text": "Section 66",
"label": "PROVISION",
"start_char": 698,
"end_char": 708,
"source": "regex",
"metadata": {
"statute": null
}
},
{
"text": "Rajinder Narain",
"label": "LAWYER",
"start_char": 909,
"end_char": 924,
"source": "ner",
"metadata": {
... |
1950_1_30_63_EN | 1,950 | "l[areh li.\n\nSUPREME COURT REPORTS\n\nABDULLA AHMED v.\n\nANIMENDRA KISSEN MITTER.\n\n[SHRI HARILA(...TRUNCATED) | 112 | [{"text":"ABDULLA AHMED","label":"PETITIONER","start_char":35,"end_char":48,"source":"metadata","met(...TRUNCATED) |
1950_1_335_390_EN | 1,950 | "S.C.R.\n\nSUPREME COURT REPORTS 335\n\nCOMMISSIONER OF INCOME-TAX, BOMBAY\n\nAHMEDBHAI UMARBHAI & C(...TRUNCATED) | 308 | [{"text":"335\n\nCOMMISSIONER OF INCOME-TAX, BOMBAY","label":"PETITIONER","start_char":30,"end_char"(...TRUNCATED) |
1950_1_391_434_EN | 1,950 | "S.C.R.\n\n. SUPREME COURT REPORTS 391\n\nNANALAL ZAVER AND ANOTHER\n\nBOMBAY LIFE ASSURANCE CO. LTD(...TRUNCATED) | 186 | [{"text":"391\n\nNANALAL ZAVER AND ANOTHER","label":"PETITIONER","start_char":32,"end_char":62,"sour(...TRUNCATED) |
1950_1_435_452_EN | 1,950 | "S.~.R.\n\nSUPREME COURT REPORTS\n\nCOMMISSIONER OF AGRICULTURAL\n\nINCOME-TAX, BENGAL V.\n\nSRI KES(...TRUNCATED) | 130 | [{"text":"COMMISSIONER OF AGRICULTURAL\n\nINCOME-TAX, BENGAL","label":"PETITIONER","start_char":31,"(...TRUNCATED) |
1950_1_453_459_EN | 1,950 | "S.C.R.\n\nSUPREME COURT REPORTS 453\n\nPRITAM SINGH\n\nTHE STATE\n\n[SAIYID FAZL ALI, PATANJALI SAS(...TRUNCATED) | 65 | [{"text":"453\n\nPRITAM SINGH","label":"PETITIONER","start_char":30,"end_char":47,"source":"metadata(...TRUNCATED) |
1950_1_459_519_EN | 1,950 | "~.C.R.\n\nSUPREME COURT REPORTS 459\n\na sound basis for invoking the discretion of this Court in g(...TRUNCATED) | 321 | [{"text":"S. P. Varma","label":"LAWYER","start_char":640,"end_char":651,"source":"ner","metadata":{"(...TRUNCATED) |
1950_1_519_536_EN | 1,950 | "S.C.R.\n\nSUPREME COURT REPORTS 519\n\nEven assuming for argument's sake that we have got jurisdict(...TRUNCATED) | 147 | [{"text":"article 136","label":"PROVISION","start_char":105,"end_char":116,"source":"regex","metadat(...TRUNCATED) |
indian-sc-judgments-ner-silver
33,000 Indian Supreme Court judgments annotated with silver NER labels.
Covers 1950–2024. Each document contains the full cleaned judgment text and all automatically generated entity spans with verified character offsets. Designed as the training corpus for evolawyer/inlegalbert-sc-ner-silver.
Usage
Load with the HuggingFace datasets library:
from datasets import load_dataset
ds = load_dataset("evolawyer/indian-sc-judgments-ner-silver")
doc = ds["train"][0]
text = doc["text"]
for entity in doc["entities"]:
span = text[entity["start_char"]:entity["end_char"]]
assert span == entity["text"] # always holds
print(f"{entity['label']:<20} {span}")
Or load a specific year directly from the JSONL files:
import json
from pathlib import Path
def iter_year(data_root: str, year: int):
path = Path(data_root) / f"{year}.jsonl"
with open(path, encoding="utf-8") as f:
for line in f:
yield json.loads(line)
for doc in iter_year("data", 2019):
print(doc["document_id"], len(doc["entities"]), "entities")
Coverage
| Property | Value |
|---|---|
| Documents | ~33,000 (25 test-set files withheld — see below) |
| Years | 1950–2024 |
| Source | Supreme Court of India |
| Language | English |
| Annotation | Silver (automatic) |
| Entity types | 13 |
Entity Types
| Label | Description | Primary Source |
|---|---|---|
STATUTE |
Indian Act, Code, or Constitutional document | Regex + gazetteer |
PROVISION |
Section, article, rule, or schedule | Regex |
CASE_CITATION |
Reporter citation for a prior case | Regex |
JUDGE |
Presiding judge name | Header projection |
PETITIONER |
Appellant / petitioner party | Header projection |
RESPONDENT |
Respondent party | Header projection |
LAWYER |
Appearing counsel | OpenNyAI NER |
COURT |
Court name mentioned in the judgment | OpenNyAI NER |
ORG |
Organisation other than a court | OpenNyAI NER |
GPE |
Geopolitical entity | OpenNyAI NER |
DATE |
Date mentioned in the judgment | OpenNyAI NER |
OTHER_PERSON |
Named person not in any other category | OpenNyAI NER |
WITNESS |
Witness name | OpenNyAI NER |
Document Schema
Each file is a self-contained JSON document with the full judgment text embedded:
{
"document_id": "2019_10_961_995_EN",
"year": 2019,
"text": "REPORTABLE\n\nIN THE SUPREME COURT OF INDIA\n\nCIVIL APPELLATE JURISDICTION\n...",
"total_entities": 118,
"entities": [
{
"text": "(2017) 15 SCC 720",
"label": "CASE_CITATION",
"start_char": 4821,
"end_char": 4838,
"source": "regex",
"metadata": {}
},
{
"text": "Consumer Protection Act, 1986",
"label": "STATUTE",
"start_char": 1204,
"end_char": 1233,
"source": "gazetteer",
"metadata": {
"canonical_name": "Consumer Protection Act, 1986",
"act_id": "A1986-68"
}
}
]
}
text is the full cleaned judgment text (OCR-cleaned). All start_char / end_char offsets are character positions into this field.
end_char is exclusive (Python slice convention): doc["text"][entity["start_char"]:entity["end_char"]] == entity["text"] holds for every entity in every document.
source field values: regex · ner · header · gazetteer
Withheld Test Set
The 25 files listed in test_set_ids.json are excluded from this dataset, stratified across eras:
| Era | Files |
|---|---|
| 1950–1969 | 5 |
| 1970–1989 | 5 |
| 1990–2009 | 8 |
| 2010–2024 | 7 |
These files are reserved for final evaluation of the gold-annotated v1.0 model and have never been seen during training or validation.
Annotation Pipeline
Silver labels were produced by four automatic annotation stages merged into a single authoritative span set per document:
- Metadata + header projection — case metadata JSONs (party names, judge names) mapped to character offsets via RapidFuzz fuzzy match for parties and strict regex for judges; confined to the document header zone (<1,500 chars). Produces
JUDGE,PETITIONER,RESPONDENT. - Regex extraction — 14-pattern case citation extractor; statute and provision extractor with hierarchical linking. Produces
CASE_CITATION,STATUTE,PROVISION. - OpenNyAI transformer NER —
en_legal_ner_trf(InLegalBERT-based), run on GPU with character offset correction applied to align spans against the cleaned text. ProducesLAWYER,COURT,ORG,GPE,DATE,OTHER_PERSON,WITNESS. - Gazetteer enrichment — full-corpus fuzzy matching against an alias dictionary built from 858 Central Act JSONs. Adds and confirms
STATUTEspans missed by regex.
Merge authority rules: metadata owns the header zone for party/judge labels; regex overrides NER for overlapping citation and statute spans. All entity offsets verified: doc["text"][start_char:end_char] == entity["text"] passes for 100% of entities.
Related
- Model:
evolawyer/inlegalbert-sc-ner-silver - GitHub: evolawyer/inlegalbert-sc-ner-silver
- Website: evolawyer.com
License
Apache 2.0
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