Datasets:

---

evalitahf

/

evalita_llm_results

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 4 was different: 
average_CPS: double
config: struct<model_name: string, num_fewshot: string, batch_size: int64, model: string, base_model: string, revision: string, multimodal: bool, submitted_time: timestamp[s], num_params_billion: double, language: string>
tasks: struct<admission-test: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, faq: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, hate-speech-detection: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, lexical-substitution: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, evalita NER: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, relation-extraction: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, sentiment-analysis: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, summarization-fanpage: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, text-entailment: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, word-in-context: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-MC: struct<prompts: list<item: struct<prompt: string, metric: string, value: null, stderr: null>>, average_accuracy: null, best_prompt: null, prompt_id: null, CPS: null, std_accuracy: null>, MAIA-GEN: struct<prompts: list<item: struct<prompt: string, metric: string, value: null, stderr: null>>, average_accuracy: null, best_prompt: null, prompt_id: null, CPS: null, std_accuracy: null>>
vs
average_CPS: double
config: struct<model_name: string, num_fewshot: string, batch_size: int64, model: string, base_model: string, revision: string, multimodal: bool, submitted_time: timestamp[s], num_params_billion: double, language: string>
tasks: struct<admission-test: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, faq: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, hate-speech-detection: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, lexical-substitution: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, evalita NER: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, relation-extraction: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, sentiment-analysis: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, summarization-fanpage: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, text-entailment: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, word-in-context: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-MC: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-GEN: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>>
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 4 was different: 
              average_CPS: double
              config: struct<model_name: string, num_fewshot: string, batch_size: int64, model: string, base_model: string, revision: string, multimodal: bool, submitted_time: timestamp[s], num_params_billion: double, language: string>
              tasks: struct<admission-test: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, faq: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, hate-speech-detection: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, lexical-substitution: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, evalita NER: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, relation-extraction: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, sentiment-analysis: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, summarization-fanpage: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, text-entailment: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, word-in-context: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-MC: struct<prompts: list<item: struct<prompt: string, metric: string, value: null, stderr: null>>, average_accuracy: null, best_prompt: null, prompt_id: null, CPS: null, std_accuracy: null>, MAIA-GEN: struct<prompts: list<item: struct<prompt: string, metric: string, value: null, stderr: null>>, average_accuracy: null, best_prompt: null, prompt_id: null, CPS: null, std_accuracy: null>>
              vs
              average_CPS: double
              config: struct<model_name: string, num_fewshot: string, batch_size: int64, model: string, base_model: string, revision: string, multimodal: bool, submitted_time: timestamp[s], num_params_billion: double, language: string>
              tasks: struct<admission-test: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, faq: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, hate-speech-detection: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, lexical-substitution: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, evalita NER: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, relation-extraction: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, sentiment-analysis: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, summarization-fanpage: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, text-entailment: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, word-in-context: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: double>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-MC: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>, MAIA-GEN: struct<prompts: list<item: struct<prompt: string, metric: string, value: double, stderr: null>>, average_accuracy: double, best_prompt: double, prompt_id: string, CPS: double, std_accuracy: double>>