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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'config' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 620, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'config' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1886, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 639, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'config' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1420, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1052, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
uid
string | id
string | organization
string | name
string | created_at
timestamp[us] | last_modified
string | trending_score
int64 | likes
int64 | tags
sequence | config
dict | results
dict | runtime_stage
string | card_data
dict | sources
sequence | enriched
dict | approval
bool | consolidated_notes
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
65becbc4744da3e639da88d9 | HaizeLabs/red-teaming-resistance-benchmark | HaizeLabs | red-teaming-resistance-benchmark | 2024-02-03T23:27:00 | 2024-06-07 18:34:09 | 1 | 41 | [
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} | true | A benchmark that evaluates LLMs' resistance to adversarial prompts and safety violations across multiple categories of harmful content. |
647848ca9c1f42c1f4d7e033 | gaia-benchmark/leaderboard | gaia-benchmark | leaderboard | 2023-06-01T07:29:14 | 2025-01-30 07:53:25 | 15 | 246 | [
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} | true | A leaderboard for tool augmented LLMs! |
6687e8366d98cab219a29b72 | ttsds/benchmark | ttsds | benchmark | 2024-07-05T12:33:58 | 2024-08-31 19:50:13 | 1 | 20 | [
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} | RUNNING | {
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} | true | Compares the quality of speech generation by text-to-speech models using automated metrics. |
666f8193d148ca0bcfbca2ed | Intel/UnlearnDiffAtk-Benchmark | Intel | UnlearnDiffAtk-Benchmark | 2024-06-17T00:21:39 | 2025-02-04 15:58:57 | 1 | 7 | [
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} | true | A benchmark that evaluates how well diffusion models can unlearn specific concepts while maintaining generation quality and prompt alignment. |
66b4896bcc8441dc730567e5 | panuthept/thai_sentence_embedding_benchmark | panuthept | thai_sentence_embedding_benchmark | 2024-08-08T09:01:31 | 2024-08-08 16:22:44 | 1 | 12 | [
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} | true | A benchmark that evaluates Thai sentence embedding models across multiple tasks including semantic similarity, classification, and retrieval. |
656449ea771319d93b10fe07 | protectai/prompt-injection-benchmark | protectai | prompt-injection-benchmark | 2023-11-27T07:48:58 | 2024-11-20 17:26:06 | 1 | 13 | [
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} | true | No data inside table. A benchmark that evaluates different prompt injection detection systems by measuring their ability to identify and prevent malicious prompts. |
670cdeb1dec346e701aa390b | m42-health/MEDIC-Benchmark | m42-health | MEDIC-Benchmark | 2024-10-14T09:04:49 | 2025-01-20 08:03:24 | 1 | 5 | [
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6730c9858b5a645918504e5b | StarscreamDeceptions/Multilingual-MMLU-Benchmark-Leaderboard | StarscreamDeceptions | Multilingual-MMLU-Benchmark-Leaderboard | 2024-11-10T14:56:05 | 2024-11-25 07:51:11 | 1 | 10 | [
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} | true | This leaderboard is dedicated to evaluating and comparing the multilingual capabilities. |
66e7fad2f0053c645b8107df | Inferless/LLM-Inference-Benchmark | Inferless | LLM-Inference-Benchmark | 2024-09-16T09:30:58 | 2024-10-03 08:38:24 | 0 | 8 | [
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} | true | Compares the inference speed and performance of LLMs using different libraries. |
65a5a7c26145ebc6e7e39243 | TTS-AGI/TTS-Arena | TTS-AGI | TTS-Arena | 2024-01-15T21:46:42 | 2025-01-30 16:45:06 | 12 | 621 | [] | {} | null | RUNNING | {
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} | true | |
671bd9d914479a1a119853f0 | argmaxinc/whisperkit-benchmarks | argmaxinc | whisperkit-benchmarks | 2024-10-25T17:48:09 | 2024-12-31 00:07:47 | 0 | 17 | [
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} | true | |
65bf93c6ba3ff03d686c3667 | utrobinmv/TREX_benchmark_en_ru_zh | utrobinmv | TREX_benchmark_en_ru_zh | 2024-02-04T13:40:22 | 2024-11-04 20:37:05 | 0 | 6 | [
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} | true | |
65e58abff4a700ec2ddb2533 | Pendrokar/TTS-Spaces-Arena | Pendrokar | TTS-Spaces-Arena | 2024-03-04T08:47:59 | 2025-02-02 19:08:13 | 11 | 282 | [] | {} | null | RUNNING | {
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} | [
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} | true | |
6691e2b0804994abfc2d81c4 | mteb/arena | mteb | arena | 2024-07-13T02:13:04 | 2025-01-27 02:22:41 | 2 | 88 | [
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674eea98c6a6ef2849b4a0ac | bgsys/background-removal-arena | bgsys | background-removal-arena | 2024-12-03T11:25:12 | 2025-01-31 09:20:11 | 9 | 58 | [
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6710c75984a75320ee25e5aa | k-mktr/gpu-poor-llm-arena | k-mktr | gpu-poor-llm-arena | 2024-10-17T08:14:17 | 2025-01-29 20:31:14 | 3 | 175 | [
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651f831f128d26b399db9ea5 | dylanebert/3d-arena | dylanebert | 3d-arena | 2023-10-06T03:46:39 | 2025-01-24 19:44:25 | 4 | 247 | [
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669d17bbe99ea743cfde99b3 | SUSTech/ChineseSafe-Benchmark | SUSTech | ChineseSafe-Benchmark | 2024-07-21T14:14:19 | 2024-12-28 06:53:49 | 0 | 11 | [
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6719d6a46937670ca681151e | ArtificialAnalysis/Video-Generation-Arena-Leaderboard | ArtificialAnalysis | Video-Generation-Arena-Leaderboard | 2024-10-24T05:09:56 | 2024-11-24 20:14:48 | 1 | 44 | [
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66fd3b0a8600fb45c8ce43b2 | nyanko7/text-to-anime-arena | nyanko7 | text-to-anime-arena | 2024-10-02T12:22:34 | 2024-10-03 05:47:40 | 1 | 5 | [
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672b47b91b5f7a5e97a0e631 | Marqo/Ecommerce-Embedding-Benchmarks | Marqo | Ecommerce-Embedding-Benchmarks | 2024-11-06T10:40:57 | 2024-11-11 15:57:46 | 0 | 17 | [
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} | true | Compares ecommerce embedding models on multimodal product retrieval tasks. |
66ab49f13db4dafee596a198 | Vikhrmodels/arenahardlb | Vikhrmodels | arenahardlb | 2024-08-01T08:40:17 | 2024-11-01 15:28:31 | 1 | 41 | [
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64873060328076a994947df6 | lmarena-ai/chatbot-arena | lmarena-ai | chatbot-arena | 2023-06-12T14:49:04 | 2024-09-20 07:41:07 | 0 | 187 | [
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6766c507d7a63c4852cd8a03 | atlasia/darija-chatbot-arena | atlasia | darija-chatbot-arena | 2024-12-21T13:39:19 | 2025-02-03 09:31:27 | 1 | 11 | [
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} | true | |
677b478c59e4ac32ab8a4939 | Vchitect/VBench_Video_Arena | Vchitect | VBench_Video_Arena | 2025-01-06T03:01:32 | 2025-01-14 07:51:18 | 1 | 12 | [
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65c9a8e7dc38a2858a77ff8d | TIGER-Lab/GenAI-Arena | TIGER-Lab | GenAI-Arena | 2024-02-12T05:13:11 | 2025-02-04 17:00:34 | 2 | 267 | [
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} | true | Evaluates visual AI models through human preference votes in arena battles. |
65a2cd9890b5e87bcdf9f2e2 | yutohub/japanese-chatbot-arena-leaderboard | yutohub | japanese-chatbot-arena-leaderboard | 2024-01-13T17:51:20 | 2024-03-08 11:16:07 | 0 | 34 | [
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} | true | Evaluates Japanese Large Language Models through crowdsourced pairwise comparison in a chat arena format. |
659421c8c0a266442687437c | yhavinga/dutch-tokenizer-arena | yhavinga | dutch-tokenizer-arena | 2024-01-02T14:46:32 | 2024-11-07 14:38:12 | 0 | 7 | [
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6752eb667be42a104ca5841b | Oriserve/ASR_arena | Oriserve | ASR_arena | 2024-12-06T12:17:42 | 2024-12-27 07:40:30 | 1 | 5 | [
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64ef26d33831f23491562014 | xu-song/tokenizer-arena | xu-song | tokenizer-arena | 2023-08-30T11:24:03 | 2025-02-05 04:39:17 | 0 | 59 | [
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64f9e6dd59eae6df399ba1e9 | hf-audio/open_asr_leaderboard | hf-audio | open_asr_leaderboard | 2023-09-07T15:06:05 | 2024-11-22 23:31:43 | 5 | 608 | [
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65a7b7a7feb385935219fd72 | mii-llm/open_ita_llm_leaderboard | mii-llm | open_ita_llm_leaderboard | 2024-01-17T11:19:03 | 2024-12-09 10:21:36 | 2 | 68 | [
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66231fbfd323727f81a5bbec | SeaLLMs/LLM_Leaderboard_for_SEA | SeaLLMs | LLM_Leaderboard_for_SEA | 2024-04-20T01:51:59 | 2024-12-10 12:29:34 | 2 | 18 | [] | {} | null | RUNNING | {
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64fad4e58d50404bc4ee667f | opencompass/opencompass-llm-leaderboard | opencompass | opencompass-llm-leaderboard | 2023-09-08T08:01:41 | 2024-02-08 03:03:58 | 1 | 89 | [] | {} | null | RUNNING | {
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656e4128896aeb1168858cf5 | Nexusflow/Nexus_Function_Calling_Leaderboard | Nexusflow | Nexus_Function_Calling_Leaderboard | 2023-12-04T21:14:16 | 2024-03-22 00:27:33 | 1 | 89 | [
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6687c3599ea2b2e70cce52a5 | openGPT-X/european-llm-leaderboard | openGPT-X | european-llm-leaderboard | 2024-07-05T09:56:41 | 2024-09-14 15:30:01 | 1 | 88 | [
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66742a00ccd71b5bb784b85f | m42-health/clinical_ner_leaderboard | m42-health | clinical_ner_leaderboard | 2024-06-20T13:09:20 | 2024-10-14 10:06:19 | 1 | 19 | [
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Named Clinical Entity Recognition Leaderboard. |
660a51922862c0cea449bbb3 | Cognitive-Lab/indic_llm_leaderboard | Cognitive-Lab | indic_llm_leaderboard | 2024-04-01T06:17:54 | 2024-04-19 14:48:17 | 1 | 22 | [
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667b29b383f9e85330f260fa | vidore/vidore-leaderboard | vidore | vidore-leaderboard | 2024-06-25T20:33:55 | 2024-12-05 10:28:29 | 1 | 106 | [] | {} | null | RUNNING | {
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65af75895c9e7ad7bb512d22 | BAAI/open_cn_llm_leaderboard | BAAI | open_cn_llm_leaderboard | 2024-01-23T08:15:05 | 2025-01-02 04:21:39 | 1 | 107 | [] | {} | null | RUNNING | {
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664ff04e5a78417f01edc41c | yentinglin/open-tw-llm-leaderboard | yentinglin | open-tw-llm-leaderboard | 2024-05-24T01:41:34 | 2024-06-08 03:34:27 | 1 | 19 | [
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659d762f50c1bbee5be20c63 | AI-Secure/llm-trustworthy-leaderboard | AI-Secure | llm-trustworthy-leaderboard | 2024-01-09T16:37:03 | 2024-11-22 05:50:44 | 1 | 87 | [
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66f44fc2304dc814adaffe0a | JMMMU/JMMMU_Leaderboard | JMMMU | JMMMU_Leaderboard | 2024-09-25T18:00:34 | 2024-11-03 13:11:52 | 1 | 13 | [
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6692b45de32997bdf71eeb24 | AssistantBench/leaderboard | AssistantBench | leaderboard | 2024-07-13T17:07:41 | 2025-01-16 06:23:12 | 1 | 9 | [
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677f99fe8d5985fec9dcaea3 | omlab/open-agent-leaderboard | omlab | open-agent-leaderboard | 2025-01-09T09:42:22 | 2025-01-24 07:18:13 | 1 | 11 | [
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} | true | Compares the math reasoning capabilities and performance of conversational agents. |
654dc45a956e2f124cdfba5a | mesolitica/malay-llm-leaderboard | mesolitica | malay-llm-leaderboard | 2023-11-10T05:49:14 | 2024-06-15 10:17:46 | 0 | 8 | [
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655e4654fcbe7329b92cf372 | mesolitica/malaysian-embedding-leaderboard | mesolitica | malaysian-embedding-leaderboard | 2023-11-22T18:20:04 | 2024-09-22 14:46:15 | 0 | 6 | [
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} | true | Evaluates Malay language embedding models on text retrieval tasks using public datasets. |
64dc410a96f0f217e44772d8 | AILab-CVC/SEED-Bench_Leaderboard | AILab-CVC | SEED-Bench_Leaderboard | 2023-08-16T03:22:50 | 2025-01-21 09:07:19 | 0 | 81 | [
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} | true | Evaluates multimodal LLMs on text/image/video understanding. |
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