Datasets:

---

adimnaku

/

llama_3b_gsm8k_single_turn_10_samples_testing_x2

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
TASK_SYSTEM_PROMPT: string
TASK_USER_PROMPT: string
FEEDBACK_LOGITS_PROMPT_TEMPLATE: string
FEEDBACK_LOGITS_SYSTEM_PROMPT: string
vs
idx: int64
rollout_idx: int64
dataset: string
sample: struct<idx: int64, question_idx: int64, question: string, answer: string, numeric_answer: int64, environment_feedback_level: string>
feedback_mode: string
student_temperature: double
feedback_temperature: double
messages_1: list<item: struct<role: string, content: string>>
response_1: string
extracted_1: string
eval_1: struct<valid: bool, has_boxed: bool, error: string, uses_correct_numbers: null, correct_result: null, result: null, numbers_used: null, predicted: string, expected: int64, env_feedback: string>
correct_1: bool
feedback: string
messages_2: list<item: struct<role: string, content: string>>
response_2: string
extracted_2: string
eval_2: struct<valid: bool, has_boxed: bool, error: string, uses_correct_numbers: null, correct_result: null, result: null, numbers_used: null, predicted: string, expected: int64, env_feedback: string>
correct_2: bool
model_name: string
timestamp: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              TASK_SYSTEM_PROMPT: string
              TASK_USER_PROMPT: string
              FEEDBACK_LOGITS_PROMPT_TEMPLATE: string
              FEEDBACK_LOGITS_SYSTEM_PROMPT: string
              vs
              idx: int64
              rollout_idx: int64
              dataset: string
              sample: struct<idx: int64, question_idx: int64, question: string, answer: string, numeric_answer: int64, environment_feedback_level: string>
              feedback_mode: string
              student_temperature: double
              feedback_temperature: double
              messages_1: list<item: struct<role: string, content: string>>
              response_1: string
              extracted_1: string
              eval_1: struct<valid: bool, has_boxed: bool, error: string, uses_correct_numbers: null, correct_result: null, result: null, numbers_used: null, predicted: string, expected: int64, env_feedback: string>
              correct_1: bool
              feedback: string
              messages_2: list<item: struct<role: string, content: string>>
              response_2: string
              extracted_2: string
              eval_2: struct<valid: bool, has_boxed: bool, error: string, uses_correct_numbers: null, correct_result: null, result: null, numbers_used: null, predicted: string, expected: int64, env_feedback: string>
              correct_2: bool
              model_name: string
              timestamp: string