Datasets:

---

Tswatery

/

m2geval

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
repo_name: string
file_name: string
inference_info: struct<prefix_code: string, suffix_code: string, middle_code: string, code_description: null, fill_type: string, language_type: string, sub_task_type: string>
context_code: list<item: list<item: string>>
task_instance_info: struct<created_time: timestamp[s], created_task_model: string, code_description: string>
inference_content: struct<inference_model: string, inference_result: string, inference_time: string>
editdistance_info: struct<edit_distance: double, calculate_time: timestamp[s], true_code_clean: string, predict_code_clean: string>
vs
repo_name: string
file_name: string
inference_info: struct<prefix_code: string, suffix_code: string, middle_code: string, code_description: null, fill_type: string, language_type: string, sub_task_type: null>
context_code: list<item: list<item: string>>
task_instance_info: struct<created_time: timestamp[s], created_task_model: string, function_skeleton: string, code_description: string>
inference_content: struct<inference_model: string, inference_result: string, inference_time: string>
editdistance_info: struct<edit_distance: double, calculate_time: timestamp[s], true_code_clean: string, predict_code_clean: 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 531, 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: 
              repo_name: string
              file_name: string
              inference_info: struct<prefix_code: string, suffix_code: string, middle_code: string, code_description: null, fill_type: string, language_type: string, sub_task_type: string>
              context_code: list<item: list<item: string>>
              task_instance_info: struct<created_time: timestamp[s], created_task_model: string, code_description: string>
              inference_content: struct<inference_model: string, inference_result: string, inference_time: string>
              editdistance_info: struct<edit_distance: double, calculate_time: timestamp[s], true_code_clean: string, predict_code_clean: string>
              vs
              repo_name: string
              file_name: string
              inference_info: struct<prefix_code: string, suffix_code: string, middle_code: string, code_description: null, fill_type: string, language_type: string, sub_task_type: null>
              context_code: list<item: list<item: string>>
              task_instance_info: struct<created_time: timestamp[s], created_task_model: string, function_skeleton: string, code_description: string>
              inference_content: struct<inference_model: string, inference_result: string, inference_time: string>
              editdistance_info: struct<edit_distance: double, calculate_time: timestamp[s], true_code_clean: string, predict_code_clean: string>