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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_status
string | editor_short_description
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
656449ea771319d93b10fe07 | protectai/prompt-injection-benchmark | protectai | prompt-injection-benchmark | 2023-11-27T07:48:58 | 2024-11-20 17:26:06 | 1 | 13 | [
"judge:auto",
"gradio",
"eval:safety",
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] | {} | null | RUNNING | {
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"title": "Prompt Injection Detection Benchmark"
} | [
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},
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}
} | approved | No data inside table. A benchmark that evaluates different prompt injection detection systems by measuring their ability to identify and prevent malicious prompts. |
65becbc4744da3e639da88d9 | HaizeLabs/red-teaming-resistance-benchmark | HaizeLabs | red-teaming-resistance-benchmark | 2024-02-03T23:27:00 | 2024-06-07 18:34:09 | 1 | 41 | [
"test:public",
"submission:automatic",
"judge:auto",
"eval:safety",
"modality:text",
"static"
] | {} | null | RUNNING | {
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"title": "Redteaming Resistance Leaderboard"
} | [
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},
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}
} | approved | A benchmark that evaluates LLMs' resistance to adversarial prompts and safety violations across multiple categories of harmful content. |
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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"language:spanish",
"language:yoruba",
"language:english",
"language:french",
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"language:chinese",
"machine learning",
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"MMMLU",
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"language:german",
"language:bengali",
"language:swahili",
"language:hindi",
"language:indonesian",
"language:portugese",
"gradio"
] | {} | {
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}
} | RUNNING | {
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},
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}
} | approved | This leaderboard is dedicated to evaluating and comparing the multilingual capabilities. |
670cdeb1dec346e701aa390b | m42-health/MEDIC-Benchmark | m42-health | MEDIC-Benchmark | 2024-10-14T09:04:49 | 2025-01-20 08:03:24 | 1 | 5 | [
"test:public",
"submission:automatic",
"judge:auto",
"gradio",
"leaderboard",
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"domain:medical"
] | {} | null | RUNNING | {
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} | approved | null |
6687e8366d98cab219a29b72 | ttsds/benchmark | ttsds | benchmark | 2024-07-05T12:33:58 | 2024-08-31 19:50:13 | 1 | 20 | [
"modality:audio",
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"judge:auto",
"eval:generation",
"tts",
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] | {} | {
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}
} | RUNNING | {
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"short_description": "Text-To-Speech (TTS) Evaluation using objective metrics.",
"title": "TTSDS Benchmark and Leaderboard"
} | [
"benchmark"
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},
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}
} | approved | 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 | [
"submission:manual",
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"eval:safety",
"gradio"
] | {} | null | RUNNING | {
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} | approved | A benchmark that evaluates how well diffusion models can unlearn specific concepts while maintaining generation quality and prompt alignment. |
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 | [
"language:chinese",
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"gradio"
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} | approved | null |
633581939ac57cf2967be686 | mteb/leaderboard | mteb | leaderboard | 2022-09-29T11:29:23 | 2025-02-05 09:48:47 | 74 | 4,709 | [
"docker",
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}
} | approved | An arena ranking LLMs on retrieval capabilities. |
671bd9d914479a1a119853f0 | argmaxinc/whisperkit-benchmarks | argmaxinc | whisperkit-benchmarks | 2024-10-25T17:48:09 | 2024-12-31 00:07:47 | 0 | 17 | [
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}
} | approved | null |
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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},
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}
} | approved | Compares the inference speed and performance of LLMs using different libraries. |
65f0f612555caedb299e54d9 | DontPlanToEnd/UGI-Leaderboard | DontPlanToEnd | UGI-Leaderboard | 2024-03-13T00:40:50 | 2025-02-07 22:07:10 | 18 | 630 | [
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}
} | approved | null |
6468923b99182de17844bf7b | lmarena-ai/chatbot-arena-leaderboard | lmarena-ai | chatbot-arena-leaderboard | 2023-05-20T09:26:19 | 2025-02-06 19:31:30 | 45 | 3,971 | [
"judge:humans",
"modality:image",
"eval: generation",
"region:us",
"gradio",
"leaderboard",
"modality:text"
] | {} | null | RUNNING | {
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"title": "Chatbot Arena Leaderboard"
} | [
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},
"score": 2
}
} | approved | Chatbot Arena is an open-source platform for evaluating AI through human preference. |
67695a9a4f03e8728cbfb199 | adyen/DABstep | adyen | DABstep | 2024-12-23T12:42:02 | 2025-02-04 15:00:52 | 11 | 15 | [
"test:public",
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"judge:auto",
"eval:generation",
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"gradio",
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] | {} | null | RUNNING | {
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"short_description": "DABstep Reasoning Benchmark Leaderboard",
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} | [
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65af98551501453abf5d8e8d | opencompass/open_vlm_leaderboard | opencompass | open_vlm_leaderboard | 2024-01-23T10:43:33 | 2025-01-20 06:49:40 | 10 | 597 | [
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65650d01a0623adbd7387390 | vectara/leaderboard | vectara | leaderboard | 2023-11-27T21:41:21 | 2025-01-15 17:12:55 | 6 | 94 | [
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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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643d3016d2c1e08a5eca0c22 | open-llm-leaderboard/open_llm_leaderboard | open-llm-leaderboard | open_llm_leaderboard | 2023-04-17T11:40:06 | 2025-01-10 19:24:50 | 65 | 12,416 | [
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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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672b762fd40b55aa6f62e8f2 | elmresearchcenter/open_universal_arabic_asr_leaderboard | elmresearchcenter | open_universal_arabic_asr_leaderboard | 2024-11-06T13:59:11 | 2025-02-04 07:54:52 | 5 | 20 | [
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64943e5108f840ed960f312a | optimum/llm-perf-leaderboard | optimum | llm-perf-leaderboard | 2023-06-22T12:28:01 | 2025-02-03 11:16:07 | 5 | 417 | [
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6662b2c6cc6519da32cd6f4d | bigcode/bigcodebench-leaderboard | bigcode | bigcodebench-leaderboard | 2024-06-07T07:12:06 | 2025-02-04 20:18:34 | 5 | 179 | [
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65e124cf138bd34f8ebc927d | gorilla-llm/berkeley-function-calling-leaderboard | gorilla-llm | berkeley-function-calling-leaderboard | 2024-03-01T00:43:59 | 2024-08-23 06:16:27 | 3 | 84 | [
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} | approved | Evaluates LLMs ability to call functions. |
64bea7e1f671da974e585dcf | bigcode/bigcode-models-leaderboard | bigcode | bigcode-models-leaderboard | 2023-07-24T16:33:37 | 2024-11-11 20:36:38 | 19 | 1,111 | [
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} | approved | Specialized leaderboard for models with coding capabilities π₯οΈ(Evaluates on HumanEval and MultiPL-E) |
65944138a260709928710fb6 | allenai/reward-bench | allenai | reward-bench | 2024-01-02T17:00:40 | 2024-12-11 20:55:17 | 4 | 326 | [
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} | approved | Evaluates reward models across chat, safety and reasoning tasks. |
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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} | approved | Italian capabilities LLM leaderboard. |
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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} | approved | Compares ecommerce embedding models on multimodal product retrieval tasks. |
66918a9ed4d26f854abab9c5 | ParsBench/leaderboard | ParsBench | leaderboard | 2024-07-12T19:57:18 | 2024-11-06 20:27:24 | 3 | 37 | [
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} | approved | Compares Persian language models on diverse NLP tasks like reasoning, generation and understanding. |
6691e2b0804994abfc2d81c4 | mteb/arena | mteb | arena | 2024-07-13T02:13:04 | 2025-01-27 02:22:41 | 2 | 89 | [
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} | approved | Evaluates English ASR model performance and speed on public benchmarks |
655b6e9dc11dee7f7e8e5c09 | hallucinations-leaderboard/leaderboard | hallucinations-leaderboard | leaderboard | 2023-11-20T14:35:09 | 2024-06-12 04:22:31 | 1 | 128 | [
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66fead0f3a221be1070a1ed5 | open-llm-leaderboard/comparator | open-llm-leaderboard | comparator | 2024-10-03T14:41:19 | 2025-01-09 15:13:23 | 2 | 84 | [
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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 | 90 | [
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66f2396eb26f416f21fb41eb | opencompass/openvlm_video_leaderboard | opencompass | openvlm_video_leaderboard | 2024-09-24T04:00:46 | 2024-11-21 13:35:06 | 2 | 98 | [
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65c9a8e7dc38a2858a77ff8d | TIGER-Lab/GenAI-Arena | TIGER-Lab | GenAI-Arena | 2024-02-12T05:13:11 | 2025-02-07 17:00:32 | 2 | 267 | [
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} | approved | Evaluates visual AI models through human preference votes in arena battles. |
659c951ed9a59ad53d6a9a37 | mlabonne/Yet_Another_LLM_Leaderboard | mlabonne | Yet_Another_LLM_Leaderboard | 2024-01-09T00:36:46 | 2024-06-16 22:12:56 | 1 | 185 | [
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65e5e7b2a87482d11980782d | Intel/powered_by_intel_llm_leaderboard | Intel | powered_by_intel_llm_leaderboard | 2024-03-04T15:24:34 | 2025-01-23 12:25:01 | 1 | 39 | [
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65f5da0cde5e636ca24f3083 | hebrew-llm-leaderboard/leaderboard | hebrew-llm-leaderboard | leaderboard | 2024-03-16T17:42:36 | 2025-01-20 16:38:30 | 1 | 30 | [
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65e8f3af5686ed1f5ec30cdc | allenai/WildBench | allenai | WildBench | 2024-03-06T22:52:31 | 2024-08-06 05:40:31 | 1 | 222 | [
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} | approved | Evaluates LLMs on real-world tasks across multiple capabilities. |
6613a26850350afe76d25129 | la-leaderboard/la-leaderboard | la-leaderboard | la-leaderboard | 2024-04-08T07:53:12 | 2024-12-16 10:53:19 | 1 | 67 | [
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} | approved | Evaluates LLM capabilities in Spanish varieties and official languages of Spain through comprehensive automated linguistic benchmarking across multiple regional languages. |
6639befd49238ebdde0dc911 | Intel/low_bit_open_llm_leaderboard | Intel | low_bit_open_llm_leaderboard | 2024-05-07T05:41:17 | 2024-12-23 06:20:55 | 1 | 163 | [
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} | approved | A benchmark that evaluates low-bit quantized LLMs across multiple tasks using standardized test sets, focusing on both model performance and quantization efficiency. |
66129bee0b91dd969671f27f | meval/multilingual-chatbot-arena-leaderboard | meval | multilingual-chatbot-arena-leaderboard | 2024-04-07T13:13:18 | 2024-07-01 14:22:51 | 1 | 17 | [
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} | approved | A crowdsourced platform that evaluates LLMs through human preference votes using the Elo ranking system across multiple languages. |
6507adb704d04d653d0155e3 | opencompass/MMBench | opencompass | MMBench | 2023-09-18T01:53:59 | 2025-01-06 03:18:10 | 1 | 20 | [
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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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65d9bb784b930db33ff69591 | speakleash/open_pl_llm_leaderboard | speakleash | open_pl_llm_leaderboard | 2024-02-24T09:48:40 | 2024-11-29 21:04:00 | 1 | 55 | [
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} | approved | Evaluates Polish Language Models' capabilities across multiple Polish language tasks using zero-shot and few-shot settings. |
66aaf73f50bd6711f39cafb3 | allenai/ZeroEval | allenai | ZeroEval | 2024-08-01T02:47:27 | 2024-11-22 20:40:52 | 1 | 50 | [
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66bd6a9a359d1ee9690153b9 | llm-jp/open-japanese-llm-leaderboard | llm-jp | open-japanese-llm-leaderboard | 2024-08-15T02:40:26 | 2024-12-24 09:03:04 | 1 | 66 | [
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} | approved | The Open Japanese LLM Leaderboard by LLM-jp evaluates the performance of Japanese Large Language Models (LLMs) with more than 16 tasks from classical to modern NLP tasks. |
6690283166f3099d1265f6b7 | allenai/ZebraLogic | allenai | ZebraLogic | 2024-07-11T18:45:05 | 2024-11-05 22:49:28 | 1 | 85 | [
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} | approved | Evaluates LLM logical reasoning on puzzle-solving tasks. |
662a871654a69f2d529d3987 | OALL/Open-Arabic-LLM-Leaderboard | OALL | Open-Arabic-LLM-Leaderboard | 2024-04-25T16:38:46 | 2024-12-23 20:41:05 | 2 | 123 | [
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} | approved | LLM leaderboard comparing Arabic language models' performance across various benchmarks including reasoning, language understanding and cultural alignment, using zero-shot evaluation. |
66fd89b752e2309fb9c19e6c | navidved/open_persian_asr_leaderboard | navidved | open_persian_asr_leaderboard | 2024-10-02T17:58:15 | 2024-11-06 15:55:44 | 1 | 7 | [
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65b3ccae16301f403033baac | logikon/open_cot_leaderboard | logikon | open_cot_leaderboard | 2024-01-26T15:15:58 | 2024-11-02 11:06:00 | 0 | 50 | [
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} | approved | Evaluates Large Language Models' chain-of-thought reasoning performance across multiple logical reasoning tasks. |
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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} | approved | Bias, safety, toxicity, all those things that are important to test when your chatbot actually interacts with users |
66b39fac7fa3c7cdc13f482f | Vikhrmodels/small-shlepa-lb | Vikhrmodels | small-shlepa-lb | 2024-08-07T16:24:12 | 2024-08-08 14:31:15 | 1 | 13 | [
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65dbf64e5ca10be41fcedfe6 | allenai/URIAL-Bench | allenai | URIAL-Bench | 2024-02-26T02:24:14 | 2024-04-02 16:57:43 | 0 | 22 | [
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} | approved | Testing base LLMs instruction-following abilities via ICL. |
6719d6a46937670ca681151e | ArtificialAnalysis/Video-Generation-Arena-Leaderboard | ArtificialAnalysis | Video-Generation-Arena-Leaderboard | 2024-10-24T05:09:56 | 2024-11-24 20:14:48 | 2 | 45 | [
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6697c434e8ec15fafe46b5b7 | MIMIC-CDM/leaderboard | MIMIC-CDM | leaderboard | 2024-07-17T13:16:36 | 2024-07-31 10:12:29 | 0 | 5 | [
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65f42c08e364a7d45b73f76c | sparse-generative-ai/open-moe-llm-leaderboard | sparse-generative-ai | open-moe-llm-leaderboard | 2024-03-15T11:07:52 | 2024-08-13 09:30:40 | 0 | 32 | [
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} | approved | A leaderboard evaluating performance and efficiency metrics of open-source Mixture of Experts (MoE) LLMs across multiple benchmarks. |
65a5a7c26145ebc6e7e39243 | TTS-AGI/TTS-Arena | TTS-AGI | TTS-Arena | 2024-01-15T21:46:42 | 2025-01-30 16:45:06 | 11 | 624 | [
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66a102fb7ba13bb9bb641f7c | ai-forever/LIBRA-Leaderboard | ai-forever | LIBRA-Leaderboard | 2024-07-24T13:34:51 | 2024-11-18 08:26:17 | 0 | 7 | [
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65fdbb08b9d70ef8298cd350 | antoinelouis/decouvrir | antoinelouis | decouvrir | 2024-03-22T17:08:24 | 2024-09-03 13:11:14 | 0 | 10 | [
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6670f4cffc615a6257ab35dd | ksort/K-Sort-Arena | ksort | K-Sort-Arena | 2024-06-18T02:45:35 | 2025-02-06 19:51:10 | 0 | 45 | [
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65e58abff4a700ec2ddb2533 | Pendrokar/TTS-Spaces-Arena | Pendrokar | TTS-Spaces-Arena | 2024-03-04T08:47:59 | 2025-02-02 19:08:13 | 10 | 285 | [
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648b40be34fee97b500a7975 | ml-energy/leaderboard | ml-energy | leaderboard | 2023-06-15T16:47:58 | 2024-10-04 17:57:22 | 0 | 8 | [
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} | approved | Evaluates GenAI models' energy consumption and inference performance |
651f831f128d26b399db9ea5 | dylanebert/3d-arena | dylanebert | 3d-arena | 2023-10-06T03:46:39 | 2025-02-06 05:38:44 | 6 | 250 | [
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} | approved | The 3D Arena leaderboard evaluates generative 3D models. |
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 | 176 | [
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} | approved | Evaluates small LLMs through human preference battles. |
662e0e6445eb426d06590b55 | speakleash/mt-bench-pl | speakleash | mt-bench-pl | 2024-04-28T08:52:52 | 2024-10-25 19:54:43 | 0 | 20 | [
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} | approved | Evaluates Polish LLM capabilities across diverse linguistic and cognitive tasks using a specialized, culturally-adapted benchmarking methodology. |
674eea98c6a6ef2849b4a0ac | bgsys/background-removal-arena | bgsys | background-removal-arena | 2024-12-03T11:25:12 | 2025-01-31 09:20:11 | 2 | 60 | [
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} | approved | Background removal leaderboard. |
672bad068540c5e11ee6b822 | AtlaAI/judge-arena | AtlaAI | judge-arena | 2024-11-06T17:53:10 | 2025-01-31 19:15:47 | 2 | 88 | [
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663288d87700d0f6454230ac | andrewrreed/closed-vs-open-arena-elo | andrewrreed | closed-vs-open-arena-elo | 2024-05-01T18:24:24 | 2025-01-09 00:30:14 | 1 | 146 | [
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} | approved | Visualizes LLM progress through LMSYS Arena ELO ratings over time. |
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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} | approved | Text to Image (Anime/Illustration) Generation Arena |
66ab49f13db4dafee596a198 | Vikhrmodels/arenahardlb | Vikhrmodels | arenahardlb | 2024-08-01T08:40:17 | 2024-11-01 15:28:31 | 1 | 41 | [
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670387ebc04fc0fe267222ad | LLM360/de-arena | LLM360 | de-arena | 2024-10-07T07:04:11 | 2024-11-20 22:49:45 | 1 | 24 | [
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} | approved | null |
6766c507d7a63c4852cd8a03 | atlasia/darija-chatbot-arena | atlasia | darija-chatbot-arena | 2024-12-21T13:39:19 | 2025-02-07 10:16:27 | 1 | 11 | [
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} | approved | null |
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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} | approved | null |
65a2d7dcb4f188a4db12dc94 | NPHardEval/NPHardEval-leaderboard | NPHardEval | NPHardEval-leaderboard | 2024-01-13T18:35:08 | 2024-02-05 22:44:01 | 0 | 52 | [
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} | approved | Evaluates LLM reasoning on computational complexity problems. |
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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66c853076058eb4e6bb491b9 | speakleash/polish_medical_leaderboard | speakleash | polish_medical_leaderboard | 2024-08-23T09:14:47 | 2024-09-15 19:43:45 | 0 | 7 | [
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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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657b23848e7790a347c7e4ea | JetBrains-Research/long-code-arena | JetBrains-Research | long-code-arena | 2023-12-14T15:47:16 | 2024-06-19 08:14:50 | 0 | 32 | [
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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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670ed70fd75f1143525d9a33 | latticeflow/compl-ai-board | latticeflow | compl-ai-board | 2024-10-15T20:56:47 | 2024-12-02 14:06:52 | 0 | 24 | [
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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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65eac0b7e36ad838b863ebae | atomind/mlip-arena | atomind | mlip-arena | 2024-03-08T07:39:35 | 2025-01-30 07:39:54 | 0 | 11 | [
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6759454b7ce296e512352de2 | nyunai/edge-llm-leaderboard | nyunai | edge-llm-leaderboard | 2024-12-11T07:54:51 | 2024-12-16 12:50:13 | 0 | 18 | [
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660bb9ccb75880c7c71ca46c | ZhangYuhan/3DGen-Arena | ZhangYuhan | 3DGen-Arena | 2024-04-02T07:54:52 | 2024-12-10 08:56:26 | 0 | 93 | [
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6646ab59c02ec140e973a6b7 | openreviewer/reviewer-arena | openreviewer | reviewer-arena | 2024-05-17T00:56:57 | 2024-05-22 22:03:59 | 0 | 9 | [
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664885ecb5e5f95dc65dc3d9 | Auto-Arena/Leaderboard | Auto-Arena | Leaderboard | 2024-05-18T10:41:48 | 2024-10-07 02:37:00 | 0 | 21 | [
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665596eb33fc74415352f86b | yanolja/arena | yanolja | arena | 2024-05-28T08:33:47 | 2024-11-21 06:14:19 | 0 | 7 | [
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6740fb25f8883755e071e583 | Steveeeeeeen/Open_ASR_Leaderboard | Steveeeeeeen | Open_ASR_Leaderboard | 2024-11-22T21:44:05 | 2025-01-30 13:27:02 | 0 | 5 | [
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67587a5be374d9f14aa57a02 | kz-transformers/kaz-llm-lb | kz-transformers | kaz-llm-lb | 2024-12-10T17:28:59 | 2024-12-24 16:50:17 | 0 | 5 | [
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665e7241f8cb81b0a476eccb | ArtificialAnalysis/Text-to-Image-Leaderboard | ArtificialAnalysis | Text-to-Image-Leaderboard | 2024-06-04T01:47:45 | 2024-06-16 20:06:00 | 10 | 339 | [
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65b0a64db233ea8ce65f0bc5 | echo840/ocrbench-leaderboard | echo840 | ocrbench-leaderboard | 2024-01-24T05:55:25 | 2025-01-16 14:01:43 | 3 | 119 | [
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6738561119cbbe30918d6435 | PartAI/open-persian-llm-leaderboard | PartAI | open-persian-llm-leaderboard | 2024-11-16T08:21:37 | 2024-12-07 00:38:28 | 5 | 52 | [
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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 | [
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64c7f2911f9614c3e88fc0e1 | hf-vision/object_detection_leaderboard | hf-vision | object_detection_leaderboard | 2023-07-31T17:42:41 | 2024-07-16 20:25:01 | 2 | 152 | [
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667b29b383f9e85330f260fa | vidore/vidore-leaderboard | vidore | vidore-leaderboard | 2024-06-25T20:33:55 | 2024-12-05 10:28:29 | 3 | 108 | [
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6477a87af911e9e76c68efc9 | qiantong-xu/toolbench-leaderboard | qiantong-xu | toolbench-leaderboard | 2023-05-31T20:05:14 | 2023-11-13 18:07:56 | 2 | 65 | [
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} | approved | Evaluates LLMs on API function calling tasks. |
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