Datasets:

HaolinRPI
/

medmcqa

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 839, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 7: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                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
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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MedMCQA

由 openlifescienceai/medmcqa 的 train split 清洗、去重后划分得到。上游许可证为 Apache-2.0。

默认配置 sft 可直接用于监督微调。answer 是完整解答加正确选项字母，不是单独的选项标签。

from datasets import load_dataset

sft = load_dataset("HaolinRPI/medmcqa", "sft")
train = load_dataset("HaolinRPI/medmcqa", "train", split="train")
validation = load_dataset("HaolinRPI/medmcqa", "validation", split="validation")
members = load_dataset("HaolinRPI/medmcqa", "mia_members", split="train")
nonmembers = load_dataset("HaolinRPI/medmcqa", "mia_nonmembers", split="train")
filtered = load_dataset("HaolinRPI/medmcqa", "filtered", split="train")

划分

Training、MIA Non-member 和 Validation 的规范化 Question 互不重叠。MIA Member 是 Training 的子集。随机种子是 42。完整统计见 split_manifest.json。

文件	条数	用途
`sft/sft_train_20000.jsonl`	20,000	SFT 训练集，只含 `prompt` 和 `answer`
`sft/sft_validation_200.jsonl`	200	SFT 验证集，只含 `prompt` 和 `answer`
`train_20000.jsonl`	20,000	训练样本，保留原始字段
`mia_members_2000.jsonl`	2,000	Training 内部的 MIA Member
`mia_nonmembers_2000.jsonl`	2,000	不参与训练的 MIA Non-member
`validation_200.jsonl`	200	独立验证样本
`medmcqa_filtered.parquet`	84,403	全部合格记录，含未使用样本

SFT 文本

prompt 由题干、A–D 选项和指令 “Answer with the letter of the correct option.” 组成。answer 由解释和正确选项字母组成，字母单独占最后一行。

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