Datasets:

datht
/

vlegal

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
instruction: string
question: string
answers: string
ground_truth: string
_task: string
id: string
description: string
content: string
document: string
answer: list<item: list<item: string>>
  child 0, item: list<item: string>
      child 0, item: string
court_judgement: string
grounding: string
to
{'instruction': Value('string'), 'question': Value('string'), 'answers': Value('string'), 'ground_truth': Value('string'), '_task': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              instruction: string
              question: string
              answers: string
              ground_truth: string
              _task: string
              id: string
              description: string
              content: string
              document: string
              answer: list<item: list<item: string>>
                child 0, item: list<item: string>
                    child 0, item: string
              court_judgement: string
              grounding: string
              to
              {'instruction': Value('string'), 'question': Value('string'), 'answers': Value('string'), 'ground_truth': Value('string'), '_task': Value('string')}
              because column names don't match

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VLegal — Vietnamese Legal Benchmark (Evaluation Only)

Reformatted version of VLegal-Bench for per-task evaluation of Vietnamese Legal LLMs.

This dataset is for EVALUATION ONLY. Do NOT use it for training. VLegal-Bench is a benchmark test set. Training on this data contaminates benchmark scores.

Usage

from datasets import load_dataset

# Load full benchmark (all 22 tasks)
benchmark = load_dataset("datht/vlegal", split="test")

# Load specific task
task_1_1 = load_dataset("datht/vlegal", "task_1_1", split="test")
task_4_2 = load_dataset("datht/vlegal", "task_4_2", split="test")

Tasks (22 total, 10,467 samples)

Category 1: Recognition & Recall (3,520 samples)

Task	Name	Samples	Type
1.1	Legal Entity Recognition	748	MC
1.2	Legal Topic Classification	683	MC
1.3	Legal Concept Recall	300	MC
1.4	Article Recall	968	MC
1.5	Legal Schema Recall	821	MC

Category 2: Understanding & Structuring (2,837 samples)

Task	Name	Samples	Type
2.1	Relation Extraction	253	MC
2.2	Legal Element Recognition	300	MC
2.3	Legal Graph Structuring	326	MC
2.4	Judgement Verification	599	MC
2.5	User Intent Understanding	1,359	MC

Category 3: Reasoning & Inference (2,017 samples)

Task	Name	Samples	Type
3.1	Article/Clause Prediction	600	MC
3.2	Legal Court Decision Prediction	600	MC
3.3	Multi-hop Graph Reasoning	292	MC
3.4	Conflict & Consistency Detection	166	MC
3.5	Penalty/Remedy Estimation	359	MC