Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
config_general: struct<model_name: string, model_dtype: string, model_size: int64>
results: struct<CMMMU: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU_Pro_standard: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU_Pro_vision: struct<accuracy: double, acc_stderr: int64, acc: double>, MmvetV2: struct<accuracy: double, capability_scores: struct<math: double, ocr: double, spat: double, rec: double, know: double, gen: double, seq: double>, capability_detail_scores: struct<math_ocr: double, math_ocr_spat: double, math_rec_ocr_spat: double, rec_spat: double, ocr_spat: double, rec_ocr_spat: double, know_ocr_spat: double, rec_ocr: double, rec_know_spat: double, ocr: double, rec: double, rec_know: double, rec_know_gen: double, rec_know_ocr_gen: double, rec_spat_ocr_gen: double, spat_ocr_gen: double, math_gen_seq_ocr_spat: double, math_rec_seq_ocr_spat: double, rec_spat_gen: double, math_gen_ocr_spat: double, seq_rec_spat: double, seq_rec_ocr_spat: double, rec_know_spat_gen: double, rec_gen: double, ocr_rec_know_spat: double, gen_rec_know_ocr_spat: double, math_rec_ocr: double, rec_ocr_gen: double, seq_rec_ocr_gen: double, ocr_gen: double, seq_rec_gen: double, seq_rec: double, seq_rec_spat_gen: double, seq_rec_know: double, seq_rec_know_gen: double, gen_rec_seq_ocr_spat: double, gen_rec_seq_know_ocr: double, math_rec_know: double, seq_rec_ocr: double, rec_know_ocr_spat: double>, acc_stderr: int64, acc: double>, MathVerse: struct<Text Dominant: struct<accuracy: double, correct: int64, total: int64>, Total: struct<accuracy: double, correct: int64, total: int64>, Text Lite: struct<accuracy: double, correct: int64, total: int64>, Vision Intensive: struct<accuracy: double, correct: int64, total: int64>, Vision Dominant: struct<accuracy: double, correct: int64, total: int64>, Vision Only: struct<accuracy: double, correct: int64, total: int64>, accuracy: double, acc_stderr: int64, acc: double>, Ocrlite: struct<final_score: list<item: int64>, accuracy: double, Key Information Extraction-Bookshelf: list<item: int64>, Scene Text-centric VQA-diet_constraints: list<item: int64>, Doc-oriented VQA-Control: list<item: int64>, Doc-oriented VQA: list<item: int64>, Scene Text-centric VQA-Fake_logo: list<item: int64>, Handwritten Mathematical Expression Recognition: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA-Control: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, Irregular Text Recognition: list<item: int64>, Non-Semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, acc_stderr: int64, acc: double>, OcrliteZh: struct<final_score: list<item: int64>, accuracy: double, Docvqa: list<item: int64>, Chartqa-human: list<item: int64>, Chartqa-au: list<item: int64>, infographic: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, IrRegular Text Recognition: list<item: int64>, Non-semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, Handwriting_CN: list<item: int64>, Chinese Unlimited: list<item: int64>, acc_stderr: int64, acc: double>, CharXiv: struct<descriptive: struct<Overall Score: double, By Question: struct<Q1: double, Q2: double, Q3: double, Q4: double, Q5: double, Q6: double, Q7: double, Q8: double, Q9: double, Q10: double, Q11: double, Q12: double, Q13: double, Q14: double, Q15: double, Q16: double, Q17: double, Q18: double, Q19: double>, By Category: struct<Information Extraction: double, Enumeration: double, Pattern Recognition: double, Counting: double, Compositionality: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, N_valid: int64, N_invalid: int64, Question Type: string>, reasoning: struct<Overall Score: double, By Answer Type: struct<Text-in-Chart: double, Text-in-General: double, Number-in-Chart: double, Number-in-General: double>, By Source: struct<GPT-Sourced: double, GPT-Inspired: double, Completely Human: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, N_valid: int64, N_invalid: int64, Question Type: string>, accuracy: double, acc_stderr: int64, acc: double>, MathVision: struct<accuracy: double, acc_stderr: int64, acc: double>, CII-Bench: struct<accuracy: double, domain_score: struct<Life: double, Art: double, CTC: double, Society: double, Env.: double, Politics: double>, emotion_score: struct<Neutral: double, Negative: double, Positive: double>, acc_stderr: int64, acc: double>, Blink: struct<accuracy: double, Art Style: double, Counting: double, Forensic Detection: double, Functional Correspondence: double, IQ Test: double, Jigsaw: double, Multi-view Reasoning: double, Object Localization: double, Relative Depth: double, Relative Reflectance: double, Semantic Correspondence: double, Spatial Relation: double, Visual Correspondence: double, Visual Similarity: double, acc_stderr: int64, acc: double>>
vs
config_general: struct<model_name: string, model_dtype: string, model_size: int64>
results: struct<CMMMU: struct<艺术与设计: struct<num: int64, correct: int64, accuracy: double>, overall: struct<num: int64, correct: int64, accuracy: double>, 商业: struct<num: int64, correct: int64, accuracy: double>, 科学: struct<num: int64, correct: int64, accuracy: double>, 健康与医学: struct<num: int64, correct: int64, accuracy: double>, 人文社会科学: struct<num: int64, correct: int64, accuracy: double>, 技术与工程: struct<num: int64, correct: int64, accuracy: double>, accuracy: double, acc_stderr: int64, acc: double>, MMMU: struct<accuracy: double, subject_score: struct<Accounting: double, Agriculture: double, Architecture: double, Art: double, Basic: double, Biology: double, Chemistry: double, Clinical: double, Computer: double, Design: double, Diagnostics: double, Economics: double, Electronics: double, Energy: double, Finance: double, Geography: double, History: double, Literature: double, Manage: double, Marketing: double, Materials: double, Math: double, Mechanical: double, Music: double, Pharmacy: double, Physics: double, Psychology: double, Public: double, Sociology: double>, difficulty_score: struct<Medium: double, Easy: double, Hard: double>, acc_stderr: int64, acc: double>, MMMU_Pro_standard: struct<accuracy: double, subject_score: struct<History: double, Art: double, Design: double, Literature: double, Agriculture: double, Finance: double, Sociology: double, Accounting: double, Energy_and_Power: double, Pharmacy: double, Architecture_and_Engineering: double, Clinical_Medicine: double, Public_Health: double, Physics: double, Art_Theory: double, Electronics: double, Psychology: double, Biology: double, Manage: double, Economics: double, Mechanical_Engineering: double, Diagnostics_and_Laboratory_Medicine: double, Basic_Medical_Science: double, Computer_Science: double, Math: double, Music: double, Materials: double, Marketing: double, Chemistry: double, Geography: double>, difficulty_score: struct<Medium: double, Easy: double, Hard: double>, acc_stderr: int64, acc: double>, MMMU_Pro_vision: struct<accuracy: double, subject_score: struct<History: double, Art: double, Design: double, Literature: double, Agriculture: double, Finance: double, Sociology: double, Accounting: double, Energy_and_Power: double, Pharmacy: double, Architecture_and_Engineering: double, Clinical_Medicine: double, Public_Health: double, Physics: double, Art_Theory: double, Electronics: double, Psychology: double, Biology: double, Manage: double, Economics: double, Mechanical_Engineering: double, Diagnostics_and_Laboratory_Medicine: double, Basic_Medical_Science: double, Computer_Science: double, Math: double, Music: double, Materials: double, Marketing: double, Chemistry: double, Geography: double>, acc_stderr: int64, acc: double>, MmvetV2: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, capability_scores: struct<ocr: double, math: double, spat: double, rec: double, know: double, gen: double, seq: double>, capability_detail_scores: struct<ocr_math: double, ocr_spat_math: double, rec_ocr_spat_math: double, rec_spat: double, ocr_spat: double, rec_ocr_spat: double, know_ocr_spat: double, rec_ocr: double, rec_know_spat: double, ocr: double, rec: double, rec_know: double, rec_know_gen: double, rec_ocr_know_gen: double, rec_ocr_spat_gen: double, ocr_spat_gen: double, ocr_spat_math_gen_seq: double, rec_ocr_spat_math_seq: double, rec_spat_gen: double, ocr_spat_math_gen: double, rec_seq_spat: double, rec_ocr_seq_spat: double, rec_know_spat_gen: double, rec_gen: double, rec_ocr_know_spat: double, know_rec_ocr_gen_spat: double, rec_ocr_math: double, rec_ocr_gen: double, rec_ocr_seq_gen: double, ocr_gen: double, rec_seq_gen: double, rec_seq: double, rec_spat_seq_gen: double, rec_know_seq: double, rec_know_seq_gen: double, rec_seq_spat_gen: double, rec_ocr_spat_gen_seq: double, know_rec_ocr_gen_seq: double, rec_know_math: double, rec_ocr_seq: double>, acc_stderr: int64, acc: double>, MathVerse: struct<Text Dominant: struct<accuracy: double, correct: int64, total: int64>, Total: struct<accuracy: double, correct: int64, total: int64>, Text Lite: struct<accuracy: double, correct: int64, total: int64>, Vision Intensive: struct<accuracy: double, correct: int64, total: int64>, Vision Dominant: struct<accuracy: double, correct: int64, total: int64>, Vision Only: struct<accuracy: double, correct: int64, total: int64>, accuracy: double, acc_stderr: int64, acc: double>, Ocrlite: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, final_score: list<item: int64>, accuracy: double, Key Information Extraction-Bookshelf: list<item: int64>, Scene Text-centric VQA-diet_constraints: list<item: int64>, Doc-oriented VQA-Control: list<item: int64>, Doc-oriented VQA: list<item: int64>, Scene Text-centric VQA-Fake_logo: list<item: int64>, Handwritten Mathematical Expression Recognition: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA-Control: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, Irregular Text Recognition: list<item: int64>, Non-Semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, acc_stderr: int64, acc: double>, OcrliteZh: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, final_score: list<item: int64>, accuracy: double, Docvqa: list<item: int64>, Chartqa-human: list<item: int64>, Chartqa-au: list<item: int64>, infographic: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, IrRegular Text Recognition: list<item: int64>, Non-semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, Handwriting_CN: list<item: int64>, Chinese Unlimited: list<item: int64>, acc_stderr: int64, acc: double>, CharXiv: struct<descriptive: struct<Overall Score: double, By Question: struct<Q1: double, Q2: double, Q3: double, Q4: double, Q5: double, Q6: double, Q7: double, Q8: double, Q9: double, Q10: double, Q11: double, Q12: double, Q13: double, Q14: double, Q15: double, Q16: double, Q17: double, Q18: double, Q19: double>, By Category: struct<Information Extraction: double, Enumeration: double, Pattern Recognition: double, Counting: double, Compositionality: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, N_valid: int64, N_invalid: int64, Question Type: string>, reasoning: struct<Overall Score: double, By Answer Type: struct<Text-in-Chart: double, Text-in-General: double, Number-in-Chart: double, Number-in-General: double>, By Source: struct<GPT-Sourced: double, GPT-Inspired: double, Completely Human: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, N_valid: int64, N_invalid: int64, Question Type: string>, accuracy: double, acc_stderr: int64, acc: double>, MathVision: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, acc_stderr: int64, acc: double>, CII-Bench: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, domain_score: struct<Life: double, Art: double, CTC: double, Society: double, Env.: double, Politics: double>, emotion_score: struct<Neutral: double, Negative: double, Positive: double>, acc_stderr: int64, acc: double>, Blink: struct<accuracy: double, Art Style: double, Counting: double, Forensic Detection: double, Functional Correspondence: double, IQ Test: double, Jigsaw: double, Multi-view Reasoning: double, Object Localization: double, Relative Depth: double, Relative Reflectance: double, Semantic Correspondence: double, Spatial Relation: double, Visual Correspondence: double, Visual Similarity: double, acc_stderr: int64, acc: double>>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 504, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
config_general: struct<model_name: string, model_dtype: string, model_size: int64>
results: struct<CMMMU: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU_Pro_standard: struct<accuracy: double, acc_stderr: int64, acc: double>, MMMU_Pro_vision: struct<accuracy: double, acc_stderr: int64, acc: double>, MmvetV2: struct<accuracy: double, capability_scores: struct<math: double, ocr: double, spat: double, rec: double, know: double, gen: double, seq: double>, capability_detail_scores: struct<math_ocr: double, math_ocr_spat: double, math_rec_ocr_spat: double, rec_spat: double, ocr_spat: double, rec_ocr_spat: double, know_ocr_spat: double, rec_ocr: double, rec_know_spat: double, ocr: double, rec: double, rec_know: double, rec_know_gen: double, rec_know_ocr_gen: double, rec_spat_ocr_gen: double, spat_ocr_gen: double, math_gen_seq_ocr_spat: double, math_rec_seq_ocr_spat: double, rec_spat_gen: double, math_gen_ocr_spat: double, seq_rec_spat: double, seq_rec_ocr_spat: double, rec_know_spat_gen: double, rec_gen: double, ocr_rec_know_spat: double, gen_rec_know_ocr_spat: double, math_rec_ocr: double, rec_ocr_gen: double, seq_rec_ocr_gen: double, ocr_gen: double, seq_rec_gen: double, seq_rec: double, seq_rec_spat_gen: double, seq_rec_know: double, seq_rec_know_gen: double, gen_rec_seq_ocr_spat: double, gen_rec_seq_know_ocr: double, math_rec_know: double, seq_rec_ocr: double, rec_know_ocr_spat: double>, acc_stderr: int64, acc: double>, MathVerse: struct<Text Dominant: struct<accuracy: double, correct: int64, total: int64>, Total: struct<accuracy: double, correct: int64, total: int64>, Text Lite: struct<accuracy: double, correct: int64, total: int64>, Vision Intensive: struct<accuracy: double, correct: int64, total: int64>, Vision Dominant: struct<accuracy: double, correct: int64, total: int64>, Vision Only: struct<accuracy: double, correct: int64, total: int64>, accuracy: double, acc_stderr: int64, acc: double>, Ocrlite: struct<final_score: list<item: int64>, accuracy: double, Key Information Extraction-Bookshelf: list<item: int64>, Scene Text-centric VQA-diet_constraints: list<item: int64>, Doc-oriented VQA-Control: list<item: int64>, Doc-oriented VQA: list<item: int64>, Scene Text-centric VQA-Fake_logo: list<item: int64>, Handwritten Mathematical Expression Recognition: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA-Control: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, Irregular Text Recognition: list<item: int64>, Non-Semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, acc_stderr: int64, acc: double>, OcrliteZh: struct<final_score: list<item: int64>, accuracy: double, Docvqa: list<item: int64>, Chartqa-human: list<item: int64>, Chartqa-au: list<item: int64>, infographic: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, IrRegular Text Recognition: list<item: int64>, Non-semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, Handwriting_CN: list<item: int64>, Chinese Unlimited: list<item: int64>, acc_stderr: int64, acc: double>, CharXiv: struct<descriptive: struct<Overall Score: double, By Question: struct<Q1: double, Q2: double, Q3: double, Q4: double, Q5: double, Q6: double, Q7: double, Q8: double, Q9: double, Q10: double, Q11: double, Q12: double, Q13: double, Q14: double, Q15: double, Q16: double, Q17: double, Q18: double, Q19: double>, By Category: struct<Information Extraction: double, Enumeration: double, Pattern Recognition: double, Counting: double, Compositionality: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, N_valid: int64, N_invalid: int64, Question Type: string>, reasoning: struct<Overall Score: double, By Answer Type: struct<Text-in-Chart: double, Text-in-General: double, Number-in-Chart: double, Number-in-General: double>, By Source: struct<GPT-Sourced: double, GPT-Inspired: double, Completely Human: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, N_valid: int64, N_invalid: int64, Question Type: string>, accuracy: double, acc_stderr: int64, acc: double>, MathVision: struct<accuracy: double, acc_stderr: int64, acc: double>, CII-Bench: struct<accuracy: double, domain_score: struct<Life: double, Art: double, CTC: double, Society: double, Env.: double, Politics: double>, emotion_score: struct<Neutral: double, Negative: double, Positive: double>, acc_stderr: int64, acc: double>, Blink: struct<accuracy: double, Art Style: double, Counting: double, Forensic Detection: double, Functional Correspondence: double, IQ Test: double, Jigsaw: double, Multi-view Reasoning: double, Object Localization: double, Relative Depth: double, Relative Reflectance: double, Semantic Correspondence: double, Spatial Relation: double, Visual Correspondence: double, Visual Similarity: double, acc_stderr: int64, acc: double>>
vs
config_general: struct<model_name: string, model_dtype: string, model_size: int64>
results: struct<CMMMU: struct<艺术与设计: struct<num: int64, correct: int64, accuracy: double>, overall: struct<num: int64, correct: int64, accuracy: double>, 商业: struct<num: int64, correct: int64, accuracy: double>, 科学: struct<num: int64, correct: int64, accuracy: double>, 健康与医学: struct<num: int64, correct: int64, accuracy: double>, 人文社会科学: struct<num: int64, correct: int64, accuracy: double>, 技术与工程: struct<num: int64, correct: int64, accuracy: double>, accuracy: double, acc_stderr: int64, acc: double>, MMMU: struct<accuracy: double, subject_score: struct<Accounting: double, Agriculture: double, Architecture: double, Art: double, Basic: double, Biology: double, Chemistry: double, Clinical: double, Computer: double, Design: double, Diagnostics: double, Economics: double, Electronics: double, Energy: double, Finance: double, Geography: double, History: double, Literature: double, Manage: double, Marketing: double, Materials: double, Math: double, Mechanical: double, Music: double, Pharmacy: double, Physics: double, Psychology: double, Public: double, Sociology: double>, difficulty_score: struct<Medium: double, Easy: double, Hard: double>, acc_stderr: int64, acc: double>, MMMU_Pro_standard: struct<accuracy: double, subject_score: struct<History: double, Art: double, Design: double, Literature: double, Agriculture: double, Finance: double, Sociology: double, Accounting: double, Energy_and_Power: double, Pharmacy: double, Architecture_and_Engineering: double, Clinical_Medicine: double, Public_Health: double, Physics: double, Art_Theory: double, Electronics: double, Psychology: double, Biology: double, Manage: double, Economics: double, Mechanical_Engineering: double, Diagnostics_and_Laboratory_Medicine: double, Basic_Medical_Science: double, Computer_Science: double, Math: double, Music: double, Materials: double, Marketing: double, Chemistry: double, Geography: double>, difficulty_score: struct<Medium: double, Easy: double, Hard: double>, acc_stderr: int64, acc: double>, MMMU_Pro_vision: struct<accuracy: double, subject_score: struct<History: double, Art: double, Design: double, Literature: double, Agriculture: double, Finance: double, Sociology: double, Accounting: double, Energy_and_Power: double, Pharmacy: double, Architecture_and_Engineering: double, Clinical_Medicine: double, Public_Health: double, Physics: double, Art_Theory: double, Electronics: double, Psychology: double, Biology: double, Manage: double, Economics: double, Mechanical_Engineering: double, Diagnostics_and_Laboratory_Medicine: double, Basic_Medical_Science: double, Computer_Science: double, Math: double, Music: double, Materials: double, Marketing: double, Chemistry: double, Geography: double>, acc_stderr: int64, acc: double>, MmvetV2: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, capability_scores: struct<ocr: double, math: double, spat: double, rec: double, know: double, gen: double, seq: double>, capability_detail_scores: struct<ocr_math: double, ocr_spat_math: double, rec_ocr_spat_math: double, rec_spat: double, ocr_spat: double, rec_ocr_spat: double, know_ocr_spat: double, rec_ocr: double, rec_know_spat: double, ocr: double, rec: double, rec_know: double, rec_know_gen: double, rec_ocr_know_gen: double, rec_ocr_spat_gen: double, ocr_spat_gen: double, ocr_spat_math_gen_seq: double, rec_ocr_spat_math_seq: double, rec_spat_gen: double, ocr_spat_math_gen: double, rec_seq_spat: double, rec_ocr_seq_spat: double, rec_know_spat_gen: double, rec_gen: double, rec_ocr_know_spat: double, know_rec_ocr_gen_spat: double, rec_ocr_math: double, rec_ocr_gen: double, rec_ocr_seq_gen: double, ocr_gen: double, rec_seq_gen: double, rec_seq: double, rec_spat_seq_gen: double, rec_know_seq: double, rec_know_seq_gen: double, rec_seq_spat_gen: double, rec_ocr_spat_gen_seq: double, know_rec_ocr_gen_seq: double, rec_know_math: double, rec_ocr_seq: double>, acc_stderr: int64, acc: double>, MathVerse: struct<Text Dominant: struct<accuracy: double, correct: int64, total: int64>, Total: struct<accuracy: double, correct: int64, total: int64>, Text Lite: struct<accuracy: double, correct: int64, total: int64>, Vision Intensive: struct<accuracy: double, correct: int64, total: int64>, Vision Dominant: struct<accuracy: double, correct: int64, total: int64>, Vision Only: struct<accuracy: double, correct: int64, total: int64>, accuracy: double, acc_stderr: int64, acc: double>, Ocrlite: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, final_score: list<item: int64>, accuracy: double, Key Information Extraction-Bookshelf: list<item: int64>, Scene Text-centric VQA-diet_constraints: list<item: int64>, Doc-oriented VQA-Control: list<item: int64>, Doc-oriented VQA: list<item: int64>, Scene Text-centric VQA-Fake_logo: list<item: int64>, Handwritten Mathematical Expression Recognition: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA-Control: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, Irregular Text Recognition: list<item: int64>, Non-Semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, acc_stderr: int64, acc: double>, OcrliteZh: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, final_score: list<item: int64>, accuracy: double, Docvqa: list<item: int64>, Chartqa-human: list<item: int64>, Chartqa-au: list<item: int64>, infographic: list<item: int64>, Key Information Extraction: list<item: int64>, Scene Text-centric VQA: list<item: int64>, Artistic Text Recognition: list<item: int64>, IrRegular Text Recognition: list<item: int64>, Non-semantic Text Recognition: list<item: int64>, Regular Text Recognition: list<item: int64>, Handwriting_CN: list<item: int64>, Chinese Unlimited: list<item: int64>, acc_stderr: int64, acc: double>, CharXiv: struct<descriptive: struct<Overall Score: double, By Question: struct<Q1: double, Q2: double, Q3: double, Q4: double, Q5: double, Q6: double, Q7: double, Q8: double, Q9: double, Q10: double, Q11: double, Q12: double, Q13: double, Q14: double, Q15: double, Q16: double, Q17: double, Q18: double, Q19: double>, By Category: struct<Information Extraction: double, Enumeration: double, Pattern Recognition: double, Counting: double, Compositionality: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, N_valid: int64, N_invalid: int64, Question Type: string>, reasoning: struct<Overall Score: double, By Answer Type: struct<Text-in-Chart: double, Text-in-General: double, Number-in-Chart: double, Number-in-General: double>, By Source: struct<GPT-Sourced: double, GPT-Inspired: double, Completely Human: double>, By Subject: struct<Computer Science: double, Economics: double, Electrical Engineering and Systems Science: double, Mathematics: double, Physics: double, Quantitative Biology: double, Quantitative Finance: double, Statistics: double>, By Year: struct<2020: double, 2021: double, 2022: double, 2023: double>, By Subplot: struct<1 Subplot: double, 2-4 Subplots: double, 5+ Subplots: double>, N_valid: int64, N_invalid: int64, Question Type: string>, accuracy: double, acc_stderr: int64, acc: double>, MathVision: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, acc_stderr: int64, acc: double>, CII-Bench: struct<reject_info: struct<reject_rate: double, reject_number: int64, total_question: int64>, accuracy: double, domain_score: struct<Life: double, Art: double, CTC: double, Society: double, Env.: double, Politics: double>, emotion_score: struct<Neutral: double, Negative: double, Positive: double>, acc_stderr: int64, acc: double>, Blink: struct<accuracy: double, Art Style: double, Counting: double, Forensic Detection: double, Functional Correspondence: double, IQ Test: double, Jigsaw: double, Multi-view Reasoning: double, Object Localization: double, Relative Depth: double, Relative Reflectance: double, Semantic Correspondence: double, Spatial Relation: double, Visual Correspondence: double, Visual Similarity: double, acc_stderr: int64, acc: double>>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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