Datasets:

judicialmind
/

BharatTableScribe-Data

---
license: cc-by-nc-sa-4.0
task_categories:
- table-to-text
- text-generation
language:
- en
pretty_name: BharatTableScribe-Data
tags:
- india
- government-data
- table-cleaning
- data-standardisation
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/dev.parquet
  - split: test
    path: data/test.parquet
- config_name: sft
  data_files:
  - split: train
    path: sft/train.jsonl
  - split: validation
    path: sft/dev.jsonl
extra_gated_prompt: 'This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence
  (CC BY-NC-SA 4.0): research, academic, personal and other non-commercial use is permitted with attribution; adaptations
  must be shared under the same licence; commercial use is not permitted. The original public source data remains under the
  Government Open Data License - India (GODL-India). Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.'
extra_gated_fields:
  Name: text
  Email: text
  Organization: text
  I accept the CC BY-NC-SA licence terms (research and non-commercial use only): checkbox
extra_gated_button_content: Accept and request access
---
# BharatTableScribe-Data

<!-- jm:top -->
Licence: CC BY-NC-SA 4.0 — research and non-commercial use only; derivatives must be shared under the same licence. Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai

Access is approved automatically when the email you enter matches your Hugging Face account email and belongs to the organisation you name; other requests are reviewed manually.
<!-- /jm:top -->

Raw Indian government tables paired with a clean, standardised long-format version and an executable cleaning
program, built from data.gov.in (OGD) and NITI Aayog's NDAP. It is both the training data for
[BharatTableScribe-4B](https://huggingface.co/judicialmind/BharatTableScribe-4B) and a held-out-ministry benchmark: a model
reads a messy table (flattened 'A - B - C' headers, merged header rows, unit clauses such as '(Rs. in lakh)', Indian
digit grouping, total and sub-total rows, serial-number columns, footnotes, transposed layouts) and writes a JSON
object with a `schema` (finest place level, time kind, unit scales) and a `program` in a small DSL (`header`,
`skip`, `drop_rows`, `fill_down`, `transpose`, `melt`, `number`, `geo`, `time`, `const`, ...). The program is
executed on the full raw table and the result is compared with the gold table cell by cell.

**Headline result:** [BharatTableScribe-4B](https://huggingface.co/judicialmind/BharatTableScribe-4B) reaches cell F1 0.946 on
synthetic and 0.974 on NDAP-relabelled test tables, against 0.792 and 0.535 for the rule baseline.

## Quickstart

```python
from datasets import load_dataset
import json
ds = load_dataset("judicialmind/BharatTableScribe-Data", split="test")   # train / validation / test
item = ds.filter(lambda x: x["kind"] == "synthetic")[0]
raw = json.loads(item["raw"])     # {"title", "ministry", "source", "rows": [[cell, ...], ...], "meta"}
gold = json.loads(item["gold"])   # see Schema
print(item["kind"], item["title"])
for r in raw["rows"][:3]:
    print(r)
```

Or with pandas / DuckDB on a local copy:

```python
import pandas as pd, duckdb
df = pd.read_parquet("data/test.parquet")
print(df.groupby("kind").size())
print(duckdb.sql("SELECT kind, count(*) FROM 'data/test.parquet' GROUP BY kind"))
```

The `tablescribe/` package in this repository (pure Python, no dependencies) renders the model prompt, executes
programs and scores outputs:

```python
from tablescribe import RawTable, messages, parse_output, execute, target_text
raw_t = RawTable.from_json(raw)
chat = messages(raw_t)                                  # [system, user] exactly as BharatTableScribe-4B sees them
reply = target_text(gold["schema"], gold["program"])    # the gold answer in the model's reply format
schema, program, err = parse_output(reply)
clean = execute(raw_t.rows, program)                    # CleanTable: .keys, .rows (keys + value), .roles
print(clean.keys + ["value"])
print(clean.rows[:3])
```

### Evaluate a model

`evaluate.py` scores replies by executing them (needs `pyarrow`; run from a local copy of this repo, e.g.
`huggingface-cli download judicialmind/BharatTableScribe-Data --repo-type dataset --local-dir BharatTableScribe-Data`):

```bash
cd BharatTableScribe-Data
python evaluate.py --all-predictions predictions/          # reproduces the Results table below
python evaluate.py --predictions my_model.jsonl           # your replies: JSONL {"id": ..., "response": ...}
python evaluate.py --solver                               # rule baseline, re-run from scratch
python evaluate.py --model judicialmind/BharatTableScribe-4B --backend vllm --save tablescribe.jsonl   # GPU
```

**Expected output of the first command: test split, 2,149 items**

| model | synthetic cell F1 | synthetic schema acc. | NDAP-relabelled cell F1 | exec ok |
|---|---|---|---|---|
| qwen3-30b-a3b-zeroshot | 0.009 | 0.644 | 0.000 | 0.118 |
| qwen3-4b-zeroshot | 0.001 | 0.487 | 0.000 | 0.034 |
| rule-solver | 0.792 | 0.890 | 0.535 | 0.976 |
| tablescribe-4b | 0.946 | 0.945 | 0.974 | 0.978 |

To evaluate your own model, prompt it with `tablescribe.prompts.messages(raw)` for every test item, save one JSON
line `{"id", "response"}` per item and run `--predictions`. A missing or unparseable reply scores 0.

## Item kinds

| kind | gold | scoring |
|---|---|---|
| `real` | NDAP's standardised values for the same table, verified by layout-consistent value agreement with the raw OGD table | anchored cell F1 on (place, time, value) |
| `synthetic` | a simple OGD table re-rendered messy with the inverse program known | full cell F1 + schema accuracy |
| `ndap_synth` | NDAP slices (place, LGD, year, dimensions) relabelled with matched OGD headers, re-rendered messy | full cell F1 |
| `simple` | an OGD table as published + its rule-derived program | full cell F1 |

## Splits

Held-out ministries: whole ministries (OGD or NDAP side; Parliament answer tables by title topic) are held out for
test (~8%) and dev (~3%); train items that share a source table or NDAP dataset with any held-out item are removed.

| split | ndap_synth | real | simple | synthetic |
|---|---|---|---|---|
| train | 1268 | 1402 | 760 | 20420 |
| dev | 6 | 7 | 34 | 759 |
| test | 61 | 103 | 68 | 1917 |
## Files
| path | contents |
|---|---|
| `data/{train,dev,test}.parquet` | benchmark items (schema below) |
| `sft/{train,dev}.jsonl` | chat-format examples in the BharatTableScribe-4B prompt format: `id, kind, split, messages` (system + user), `completion` (target JSON) |
| `predictions/*.jsonl` | test-split replies of the four models in the Results table: `id, response` |
| `tablescribe/` | prompt rendering, program executor (DSL), scorer, rule baseline |
| `evaluate.py` | evaluation harness |
## Schema
`data/*.parquet`:
| column | type | description |
|---|---|---|
| `id` | string | item id (`scribe-<kind>-<hash>`) |
| `kind` | string | `real`, `synthetic`, `ndap_synth` or `simple` |
| `split` | string | `train`, `dev`, `test` |
| `ministry` | string | publishing ministry / department (the held-out unit) |
| `title` | string | table title as published |
| `source_uuid` | string | data.gov.in resource id of the source table (empty when the source is NDAP-only) |
| `ndap_id` | int | NDAP dataset id (real and ndap_synth items) |
| `raw` | string (JSON) | the raw grid: `{"title", "ministry", "source", "rows": [[str, ...], ...], "meta"}`; every cell is text, header rows are ordinary rows |
| `gold` | string (JSON) | `real`: `{"anchors": [[place, time, value], ...], "attributable", "geo_level", "schema", "program"}`; other kinds: `{"clean": {"keys", "roles", "rows"}, "schema", "program"}` |
| `corruptions` | list[string] | messiness applied to synthetic items (e.g. `wide`, `total_row`, `unit_scale`, `transposed`, `title_rows`) |
| `attribution` | string | GODL-India / NDAP attribution of the source table |

`schema` keys: `geo_level` (national, state, district, subdistrict, block, village, city or none), `time` (year,
financial_year, agri_year, year_month, month, date, quarter or none), `scales` ({variable or "*": multiplier}).
## Results
Test split (held-out ministries):
| model | synthetic cell F1 | synthetic schema acc. | NDAP-relabelled cell F1 | exec ok |
|---|---|---|---|---|
| Qwen3-30B-A3B, zero-shot (`qwen3-30b-a3b-zeroshot`) | 0.009 | 0.644 | 0.000 | 0.118 |
| Qwen3-4B, zero-shot (`qwen3-4b-zeroshot`) | 0.001 | 0.487 | 0.000 | 0.034 |
| rule baseline (`rule-solver`) | 0.792 | 0.890 | 0.535 | 0.976 |
| BharatTableScribe-4B (`tablescribe-4b`) | 0.946 | 0.945 | 0.974 | 0.978 |
Cell F1 compares executed outputs with gold tables. Exec ok is over all 2,149 test items. `evaluate.py` also scores the
real NDAP-anchored items.
## Coverage
26,805 items (train 23,850, dev 806, test 2,149) from Indian central-ministry, Parliament and State government
tables on data.gov.in and NDAP; English titles and headers. Real pairs: 1546 verified pairs over 666 NDAP datasets
(744 with at least 60% of the raw table's informative cells matched).
## Source data
Derived from 9,554 resources published on the Open Government Data Platform India (data.gov.in), snapshot May 2026, under the [Government Open Data License - India](https://data.gov.in/government-open-data-license-india).
Per-resource attribution (provider, title, resource URL) is in the `attribution` column.
GODL-India does not cover personal information; tables with personal fields were excluded.
Providers: Andhra Pradesh, Central Electricity Authority, Comptroller And Auditor General of India(CAG), Department of Agriculture and Farmers Welfare, Department of Atomic Energy, Department of School Education and Literacy, Department of States, Department of Telecommunications (DOT), Election Commission of India, Gujarat, Haryana, Himachal Pradesh, Home (Police) Department, Govt of Meghalaya, Indian Council of Agricultural Research (ICAR), International Labour Organization (ILO), Karnataka, Karnataka,Law and Justice Department, Karnataka,High Court of Karnataka, Karnataka,Urban Development Department Karnataka,Karnataka Municipal Data Society, Kerala, Madhya Pradesh, and 70 more.
- Real pairs pair a raw data.gov.in table with an NDAP (NITI Aayog) dataset whose numbers agree cell by cell.
- Source tables with personal fields (names, phone numbers, e-mail addresses) are excluded.
NDAP data: NITI Aayog, National Data and Analytics Platform (https://ndap.niti.gov.in); per-item attribution in the `attribution` column.
<!-- jm:source -->
Original data: published by the ministries, departments, State governments and Parliament named per table in the dataset's `attribution` column on the Open Government Data Platform India ([data.gov.in](https://data.gov.in)) and, where marked, NITI Aayog's National Data and Analytics Platform ([NDAP](https://ndap.niti.gov.in)), under the [Government Open Data License - India](https://data.gov.in/government-open-data-license-india) (GODL-India). The original public data remains under GODL-India; the CC BY-NC-SA 4.0 licence of this repository covers JudicialMind's compilation, derived fields, annotations, benchmarks and models.
<!-- /jm:source -->
## Citation
```bibtex
@misc{judicialmind2026tablescribedata,
  title={BharatTableScribe-Data: Raw Indian Government Tables with Clean Long Tables and Executable Cleaning Programs},
  author={JudicialMind},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/judicialmind/BharatTableScribe-Data}}
}
```
<!-- jm:license -->
## License
[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (see `LICENSE`): research, academic, personal and other non-commercial use, with attribution to JudicialMind and to the original data publishers; adaptations must be shared under the same licence. Commercial use requires a separate licence.
Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.
<!-- /jm:license -->

	---
	license: cc-by-nc-sa-4.0
	task_categories:
	- table-to-text
	- text-generation
	language:
	- en
	pretty_name: BharatTableScribe-Data
	tags:
	- india
	- government-data
	- table-cleaning
	- data-standardisation
	- benchmark
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train.parquet
	- split: validation
	path: data/dev.parquet
	- split: test
	path: data/test.parquet
	- config_name: sft
	data_files:
	- split: train
	path: sft/train.jsonl
	- split: validation
	path: sft/dev.jsonl
	extra_gated_prompt: 'This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence
	(CC BY-NC-SA 4.0): research, academic, personal and other non-commercial use is permitted with attribution; adaptations
	must be shared under the same licence; commercial use is not permitted. The original public source data remains under the
	Government Open Data License - India (GODL-India). Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.'
	extra_gated_fields:
	Name: text
	Email: text
	Organization: text
	I accept the CC BY-NC-SA licence terms (research and non-commercial use only): checkbox
	extra_gated_button_content: Accept and request access
	---

	# BharatTableScribe-Data

	<!-- jm:top -->
	Licence: CC BY-NC-SA 4.0 — research and non-commercial use only; derivatives must be shared under the same licence. Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai

	Access is approved automatically when the email you enter matches your Hugging Face account email and belongs to the organisation you name; other requests are reviewed manually.
	<!-- /jm:top -->

	Raw Indian government tables paired with a clean, standardised long-format version and an executable cleaning
	program, built from data.gov.in (OGD) and NITI Aayog's NDAP. It is both the training data for
	[BharatTableScribe-4B](https://huggingface.co/judicialmind/BharatTableScribe-4B) and a held-out-ministry benchmark: a model
	reads a messy table (flattened 'A - B - C' headers, merged header rows, unit clauses such as '(Rs. in lakh)', Indian
	digit grouping, total and sub-total rows, serial-number columns, footnotes, transposed layouts) and writes a JSON
	object with a `schema` (finest place level, time kind, unit scales) and a `program` in a small DSL (`header`,
	`skip`, `drop_rows`, `fill_down`, `transpose`, `melt`, `number`, `geo`, `time`, `const`, ...). The program is
	executed on the full raw table and the result is compared with the gold table cell by cell.

	Headline result: [BharatTableScribe-4B](https://huggingface.co/judicialmind/BharatTableScribe-4B) reaches cell F1 0.946 on
	synthetic and 0.974 on NDAP-relabelled test tables, against 0.792 and 0.535 for the rule baseline.

	## Quickstart

	```python
	from datasets import load_dataset
	import json

	ds = load_dataset("judicialmind/BharatTableScribe-Data", split="test") # train / validation / test
	item = ds.filter(lambda x: x["kind"] == "synthetic")[0]
	raw = json.loads(item["raw"]) # {"title", "ministry", "source", "rows": [[cell, ...], ...], "meta"}
	gold = json.loads(item["gold"]) # see Schema
	print(item["kind"], item["title"])
	for r in raw["rows"][:3]:
	print(r)
	```

	Or with pandas / DuckDB on a local copy:

	```python
	import pandas as pd, duckdb
	df = pd.read_parquet("data/test.parquet")
	print(df.groupby("kind").size())
	print(duckdb.sql("SELECT kind, count(*) FROM 'data/test.parquet' GROUP BY kind"))
	```

	The `tablescribe/` package in this repository (pure Python, no dependencies) renders the model prompt, executes
	programs and scores outputs:

	```python
	from tablescribe import RawTable, messages, parse_output, execute, target_text
	raw_t = RawTable.from_json(raw)
	chat = messages(raw_t) # [system, user] exactly as BharatTableScribe-4B sees them
	reply = target_text(gold["schema"], gold["program"]) # the gold answer in the model's reply format
	schema, program, err = parse_output(reply)
	clean = execute(raw_t.rows, program) # CleanTable: .keys, .rows (keys + value), .roles
	print(clean.keys + ["value"])
	print(clean.rows[:3])
	```

	### Evaluate a model

	`evaluate.py` scores replies by executing them (needs `pyarrow`; run from a local copy of this repo, e.g.
	`huggingface-cli download judicialmind/BharatTableScribe-Data --repo-type dataset --local-dir BharatTableScribe-Data`):

	```bash
	cd BharatTableScribe-Data
	python evaluate.py --all-predictions predictions/ # reproduces the Results table below
	python evaluate.py --predictions my_model.jsonl # your replies: JSONL {"id": ..., "response": ...}
	python evaluate.py --solver # rule baseline, re-run from scratch
	python evaluate.py --model judicialmind/BharatTableScribe-4B --backend vllm --save tablescribe.jsonl # GPU
	```

	Expected output of the first command: test split, 2,149 items

	\| model \| synthetic cell F1 \| synthetic schema acc. \| NDAP-relabelled cell F1 \| exec ok \|
	\|---\|---\|---\|---\|---\|
	\| qwen3-30b-a3b-zeroshot \| 0.009 \| 0.644 \| 0.000 \| 0.118 \|
	\| qwen3-4b-zeroshot \| 0.001 \| 0.487 \| 0.000 \| 0.034 \|
	\| rule-solver \| 0.792 \| 0.890 \| 0.535 \| 0.976 \|
	\| tablescribe-4b \| 0.946 \| 0.945 \| 0.974 \| 0.978 \|

	To evaluate your own model, prompt it with `tablescribe.prompts.messages(raw)` for every test item, save one JSON
	line `{"id", "response"}` per item and run `--predictions`. A missing or unparseable reply scores 0.

	## Item kinds

	\| kind \| gold \| scoring \|
	\|---\|---\|---\|
	\| `real` \| NDAP's standardised values for the same table, verified by layout-consistent value agreement with the raw OGD table \| anchored cell F1 on (place, time, value) \|
	\| `synthetic` \| a simple OGD table re-rendered messy with the inverse program known \| full cell F1 + schema accuracy \|
	\| `ndap_synth` \| NDAP slices (place, LGD, year, dimensions) relabelled with matched OGD headers, re-rendered messy \| full cell F1 \|
	\| `simple` \| an OGD table as published + its rule-derived program \| full cell F1 \|

	## Splits

	Held-out ministries: whole ministries (OGD or NDAP side; Parliament answer tables by title topic) are held out for
	test (~8%) and dev (~3%); train items that share a source table or NDAP dataset with any held-out item are removed.

	\| split \| ndap_synth \| real \| simple \| synthetic \|
	\|---\|---\|---\|---\|---\|
	\| train \| 1268 \| 1402 \| 760 \| 20420 \|
	\| dev \| 6 \| 7 \| 34 \| 759 \|
	\| test \| 61 \| 103 \| 68 \| 1917 \|

	## Files

	\| path \| contents \|
	\|---\|---\|
	\| `data/{train,dev,test}.parquet` \| benchmark items (schema below) \|
	\| `sft/{train,dev}.jsonl` \| chat-format examples in the BharatTableScribe-4B prompt format: `id, kind, split, messages` (system + user), `completion` (target JSON) \|
	\| `predictions/*.jsonl` \| test-split replies of the four models in the Results table: `id, response` \|
	\| `tablescribe/` \| prompt rendering, program executor (DSL), scorer, rule baseline \|
	\| `evaluate.py` \| evaluation harness \|

	## Schema

	`data/*.parquet`:

	\| column \| type \| description \|
	\|---\|---\|---\|
	\| `id` \| string \| item id (`scribe-<kind>-<hash>`) \|
	\| `kind` \| string \| `real`, `synthetic`, `ndap_synth` or `simple` \|
	\| `split` \| string \| `train`, `dev`, `test` \|
	\| `ministry` \| string \| publishing ministry / department (the held-out unit) \|
	\| `title` \| string \| table title as published \|
	\| `source_uuid` \| string \| data.gov.in resource id of the source table (empty when the source is NDAP-only) \|
	\| `ndap_id` \| int \| NDAP dataset id (real and ndap_synth items) \|
	\| `raw` \| string (JSON) \| the raw grid: `{"title", "ministry", "source", "rows": [[str, ...], ...], "meta"}`; every cell is text, header rows are ordinary rows \|
	\| `gold` \| string (JSON) \| `real`: `{"anchors": [[place, time, value], ...], "attributable", "geo_level", "schema", "program"}`; other kinds: `{"clean": {"keys", "roles", "rows"}, "schema", "program"}` \|
	\| `corruptions` \| list[string] \| messiness applied to synthetic items (e.g. `wide`, `total_row`, `unit_scale`, `transposed`, `title_rows`) \|
	\| `attribution` \| string \| GODL-India / NDAP attribution of the source table \|

	`schema` keys: `geo_level` (national, state, district, subdistrict, block, village, city or none), `time` (year,
	financial_year, agri_year, year_month, month, date, quarter or none), `scales` ({variable or "*": multiplier}).

	## Results

	Test split (held-out ministries):

	\| model \| synthetic cell F1 \| synthetic schema acc. \| NDAP-relabelled cell F1 \| exec ok \|
	\|---\|---\|---\|---\|---\|
	\| Qwen3-30B-A3B, zero-shot (`qwen3-30b-a3b-zeroshot`) \| 0.009 \| 0.644 \| 0.000 \| 0.118 \|
	\| Qwen3-4B, zero-shot (`qwen3-4b-zeroshot`) \| 0.001 \| 0.487 \| 0.000 \| 0.034 \|
	\| rule baseline (`rule-solver`) \| 0.792 \| 0.890 \| 0.535 \| 0.976 \|
	\| BharatTableScribe-4B (`tablescribe-4b`) \| 0.946 \| 0.945 \| 0.974 \| 0.978 \|

	Cell F1 compares executed outputs with gold tables. Exec ok is over all 2,149 test items. `evaluate.py` also scores the
	real NDAP-anchored items.

	## Coverage

	26,805 items (train 23,850, dev 806, test 2,149) from Indian central-ministry, Parliament and State government
	tables on data.gov.in and NDAP; English titles and headers. Real pairs: 1546 verified pairs over 666 NDAP datasets
	(744 with at least 60% of the raw table's informative cells matched).

	## Source data

	Derived from 9,554 resources published on the Open Government Data Platform India (data.gov.in), snapshot May 2026, under the [Government Open Data License - India](https://data.gov.in/government-open-data-license-india).
	Per-resource attribution (provider, title, resource URL) is in the `attribution` column.
	GODL-India does not cover personal information; tables with personal fields were excluded.

	Providers: Andhra Pradesh, Central Electricity Authority, Comptroller And Auditor General of India(CAG), Department of Agriculture and Farmers Welfare, Department of Atomic Energy, Department of School Education and Literacy, Department of States, Department of Telecommunications (DOT), Election Commission of India, Gujarat, Haryana, Himachal Pradesh, Home (Police) Department, Govt of Meghalaya, Indian Council of Agricultural Research (ICAR), International Labour Organization (ILO), Karnataka, Karnataka,Law and Justice Department, Karnataka,High Court of Karnataka, Karnataka,Urban Development Department Karnataka,Karnataka Municipal Data Society, Kerala, Madhya Pradesh, and 70 more.

	- Real pairs pair a raw data.gov.in table with an NDAP (NITI Aayog) dataset whose numbers agree cell by cell.
	- Source tables with personal fields (names, phone numbers, e-mail addresses) are excluded.

	NDAP data: NITI Aayog, National Data and Analytics Platform (https://ndap.niti.gov.in); per-item attribution in the `attribution` column.

	<!-- jm:source -->
	Original data: published by the ministries, departments, State governments and Parliament named per table in the dataset's `attribution` column on the Open Government Data Platform India ([data.gov.in](https://data.gov.in)) and, where marked, NITI Aayog's National Data and Analytics Platform ([NDAP](https://ndap.niti.gov.in)), under the [Government Open Data License - India](https://data.gov.in/government-open-data-license-india) (GODL-India). The original public data remains under GODL-India; the CC BY-NC-SA 4.0 licence of this repository covers JudicialMind's compilation, derived fields, annotations, benchmarks and models.
	<!-- /jm:source -->

	## Citation

	```bibtex
	@misc{judicialmind2026tablescribedata,
	title={BharatTableScribe-Data: Raw Indian Government Tables with Clean Long Tables and Executable Cleaning Programs},
	author={JudicialMind},
	year={2026},
	publisher={Hugging Face},
	howpublished={\url{https://huggingface.co/datasets/judicialmind/BharatTableScribe-Data}}
	}
	```

	<!-- jm:license -->
	## License

	[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (see `LICENSE`): research, academic, personal and other non-commercial use, with attribution to JudicialMind and to the original data publishers; adaptations must be shared under the same licence. Commercial use requires a separate licence.

	Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.
	<!-- /jm:license -->