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"region:us"
] | 2024-02-15T12:34:06+00:00 | {} | 2024-02-15T12:41:12+00:00 |
|
6517c306f12aa79682c5160edcee533a425db182 | Recag/Rp_CommonC_643_1 | [
"region:us"
] | 2024-02-15T12:34:23+00:00 | {} | 2024-02-15T12:44:05+00:00 |
|
2429241b027debaed01c3bfa820d0556ba02d7cb | Recag/Rp_CommonC_643_2 | [
"region:us"
] | 2024-02-15T12:34:28+00:00 | {} | 2024-02-15T12:42:20+00:00 |
|
628e80cfeb4515fc0260e165338ace46cde7a218 | Recag/Rp_CommonC_644_1 | [
"region:us"
] | 2024-02-15T12:36:52+00:00 | {} | 2024-02-15T12:44:12+00:00 |
|
a667f98ee88117648bc947e070536159ad3af88a | Recag/Rp_CommonC_644_2 | [
"region:us"
] | 2024-02-15T12:36:58+00:00 | {} | 2024-02-15T12:43:19+00:00 |
|
ca5d9fbf7e05f84c04bd0342f402d21e1745dfb6 | Recag/Rp_CommonC_730_2 | [
"region:us"
] | 2024-02-15T12:40:34+00:00 | {} | 2024-02-15T12:54:22+00:00 |
|
ff786f140106ac1360bd41505a4dbaf549269d54 | Recag/Rp_CommonC_730_3 | [
"region:us"
] | 2024-02-15T12:46:44+00:00 | {} | 2024-02-15T12:57:48+00:00 |
|
9bdcb1172eaad5cc0ad5ab03f3be27cdf3ba9f1e | Recag/Rp_CommonC_645_1 | [
"region:us"
] | 2024-02-15T12:47:39+00:00 | {} | 2024-02-15T12:58:06+00:00 |
|
9b8b911ea0b91a382c43b225178a0664bfb02eef | Recag/Rp_CommonC_645_2 | [
"region:us"
] | 2024-02-15T12:47:45+00:00 | {} | 2024-02-15T12:54:51+00:00 |
|
1fa58a20e62c3f8b7b1a1e2f2a5b5cb8feba7f1e | Recag/Rp_CommonC_646_1 | [
"region:us"
] | 2024-02-15T12:47:55+00:00 | {} | 2024-02-15T12:58:13+00:00 |
|
9a60fd32cd2a7b8356e40c1f039526fab7cb5a6b | Recag/Rp_CommonC_646_2 | [
"region:us"
] | 2024-02-15T12:48:06+00:00 | {} | 2024-02-15T13:00:03+00:00 |
|
d56fc03aae388640e617698d1095c92dcdcc501b | Recag/Rp_CommonC_647_1 | [
"region:us"
] | 2024-02-15T12:48:22+00:00 | {} | 2024-02-15T13:00:10+00:00 |
|
2b7f6a8f3b94506195ea914ec5ef7b078683a3bc | Recag/Rp_CommonC_647_2 | [
"region:us"
] | 2024-02-15T12:48:32+00:00 | {} | 2024-02-15T12:59:00+00:00 |
|
47ebd35f35e7cb722f1a16f376b85983970dcab3 | Recag/Rp_CommonC_648_1 | [
"region:us"
] | 2024-02-15T12:48:44+00:00 | {} | 2024-02-15T12:59:44+00:00 |
|
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"region:us"
] | 2024-02-15T12:48:52+00:00 | {} | 2024-02-15T12:59:53+00:00 |
|
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"region:us"
] | 2024-02-15T12:55:58+00:00 | {} | 2024-02-15T13:03:57+00:00 |
|
df514ff8f0d6676791657bb38de82fc704d2ed41 | Recag/Rp_CommonC_731_2 | [
"region:us"
] | 2024-02-15T12:57:00+00:00 | {} | 2024-02-15T13:06:59+00:00 |
|
00baa3a534c7c125c8c7877c67781426b09573f7 | Recag/Rp_CommonC_731_3 | [
"region:us"
] | 2024-02-15T12:57:34+00:00 | {} | 2024-02-15T13:10:05+00:00 |
|
dc4552fb66646087c68ff5095278e9f115c67f27 | Recag/Rp_CommonC_732_1 | [
"region:us"
] | 2024-02-15T12:57:56+00:00 | {} | 2024-02-15T13:13:29+00:00 |
|
c9320cd7bd35b529bcada6f94461da3334da1df9 | Recag/Rp_CommonC_732_2 | [
"region:us"
] | 2024-02-15T12:58:07+00:00 | {} | 2024-02-15T13:21:31+00:00 |
|
d90909d43d1f6d163cc73928358ba42bddbc4f4c | Recag/Rp_CommonC_732_3 | [
"region:us"
] | 2024-02-15T12:58:19+00:00 | {} | 2024-02-15T13:18:28+00:00 |
|
8c1aafc9eafc9e5262322eb419666db8f5d6b6cd | Recag/Rp_CommonC_733_1 | [
"region:us"
] | 2024-02-15T12:58:32+00:00 | {} | 2024-02-15T13:24:32+00:00 |
|
83289fb676081955814fde60e7ae009aaf8e14ab | wuchiyongshi/sentiment | [
"region:us"
] | 2024-02-15T12:58:44+00:00 | {} | 2024-02-15T13:00:17+00:00 |
|
13a72c0340980e93c40feaadd6301e36bdd463fd | Recag/Rp_CommonC_733_2 | [
"region:us"
] | 2024-02-15T12:58:45+00:00 | {} | 2024-02-15T13:27:53+00:00 |
|
ee881d8474e7c97d2898be7cf1a62b19be16b6f0 | Recag/Rp_CommonC_733_3 | [
"region:us"
] | 2024-02-15T12:58:56+00:00 | {} | 2024-02-15T13:31:17+00:00 |
|
79d6fd77b333cb7b9c20d32b47542507be26cfb6 | Recag/Rp_CommonC_734_1 | [
"region:us"
] | 2024-02-15T12:59:13+00:00 | {} | 2024-02-15T13:36:05+00:00 |
|
72e539e63f9846b0f533f5bba739ef87ba0e1703 | Recag/Rp_CommonC_734_2 | [
"region:us"
] | 2024-02-15T12:59:31+00:00 | {} | 2024-02-15T13:39:12+00:00 |
|
1de175acf09b6f0b4f147af4125eb09ef1dd430a | Recag/Rp_CommonC_734_3 | [
"region:us"
] | 2024-02-15T12:59:43+00:00 | {} | 2024-02-15T13:41:54+00:00 |
|
ef3dc0a4c66fafd866d0840bf05c1249d6ca9db2 | Recag/Rp_CommonC_735_1 | [
"region:us"
] | 2024-02-15T12:59:56+00:00 | {} | 2024-02-15T13:44:26+00:00 |
|
5dd967266b52be7db53e39e9de0a0b229b4fa6e8 | Recag/Rp_CommonC_735_2 | [
"region:us"
] | 2024-02-15T13:00:14+00:00 | {} | 2024-02-15T13:49:43+00:00 |
|
88a47d0e3f98cf9bd66545af02a815072aa195b0 | Recag/Rp_CommonC_735_3 | [
"region:us"
] | 2024-02-15T13:00:25+00:00 | {} | 2024-02-15T13:49:13+00:00 |
|
a1662e98c19a9db2c03ffb2dce93749f6af63513 | shkocs/uploads | [
"region:us"
] | 2024-02-15T13:07:10+00:00 | {} | 2024-02-15T13:07:10+00:00 |
|
d0d9c31310f20e269bb2c4ac7d13e16a312144ce | Recag/Rp_CommonC_649_1 | [
"region:us"
] | 2024-02-15T13:18:17+00:00 | {} | 2024-02-15T13:31:39+00:00 |
|
eccb355b0c8c8ddd876f0dfe7c6690b863c5fa24 | Recag/Rp_CommonC_649_2 | [
"region:us"
] | 2024-02-15T13:18:50+00:00 | {} | 2024-02-15T13:29:01+00:00 |
|
98b3c3a4520cda946cdb9108b352416f67a183f4 | Recag/Rp_CommonC_650_1 | [
"region:us"
] | 2024-02-15T13:19:05+00:00 | {} | 2024-02-15T13:32:30+00:00 |
|
3682166e946975bec0c659afced6db375786cd12 | Recag/Rp_CommonC_650_2 | [
"region:us"
] | 2024-02-15T13:19:13+00:00 | {} | 2024-02-15T13:30:20+00:00 |
|
30c61e9ecf855123c19d6661f35a85fc27e62775 | Recag/Rp_CommonC_651_1 | [
"region:us"
] | 2024-02-15T13:19:31+00:00 | {} | 2024-02-15T13:32:46+00:00 |
|
9cd7efb8f3aa2218cc9f0e5708897ff93aa42ecd | Recag/Rp_CommonC_651_2 | [
"region:us"
] | 2024-02-15T13:19:41+00:00 | {} | 2024-02-15T13:32:06+00:00 |
|
1bab63119dc083f3791f3205368586c346190d61 | rezaalifilmm/TEST | [
"region:us"
] | 2024-02-15T13:39:48+00:00 | {} | 2024-02-15T13:39:48+00:00 |
|
f70b429e78acfb63da8e7830583e61c67e152190 | Recag/Rp_CommonC_652_1 | [
"region:us"
] | 2024-02-15T13:40:41+00:00 | {} | 2024-02-15T13:52:04+00:00 |
|
b984a17b5ed88e83379e36419e59acb0244094ea | Recag/Rp_CommonC_652_2 | [
"region:us"
] | 2024-02-15T13:40:47+00:00 | {} | 2024-02-15T13:50:24+00:00 |
|
c99fb3f46be9e48e07220df84084c60c5117bb8f | Recag/Rp_CommonC_653_1 | [
"region:us"
] | 2024-02-15T13:41:00+00:00 | {} | 2024-02-15T13:52:50+00:00 |
|
ca44b6e0779d31d110e68e3312b636c1fa1c988d | Recag/Rp_CommonC_653_2 | [
"region:us"
] | 2024-02-15T13:41:06+00:00 | {} | 2024-02-15T13:52:21+00:00 |
|
afcf8ef00427e554c268914f3b6192301fb6d67b | Recag/Rp_CommonC_654_1 | [
"region:us"
] | 2024-02-15T13:41:21+00:00 | {} | 2024-02-15T13:52:57+00:00 |
|
60c31abc353ea3234a2c2b00e21a4861d1edb345 | Recag/Rp_CommonC_654_2 | [
"region:us"
] | 2024-02-15T13:41:26+00:00 | {} | 2024-02-15T13:52:22+00:00 |
|
e8cb02a0c1f1dbda2dea816c396acb8b9da9c42d | Recag/Rp_CommonC_736_1 | [
"region:us"
] | 2024-02-15T13:49:53+00:00 | {} | 2024-02-15T13:52:50+00:00 |
|
aa7d685688653806dc58eae260c5f8220e870640 | Recag/Rp_CommonC_736_2 | [
"region:us"
] | 2024-02-15T13:50:02+00:00 | {} | 2024-02-15T13:57:22+00:00 |
|
6f07b44bfc41de18ac855c1c7d3ea96d677e781c | Recag/Rp_CommonC_736_3 | [
"region:us"
] | 2024-02-15T13:50:49+00:00 | {} | 2024-02-15T13:57:35+00:00 |
|
d0d5930d76c2c938f890acc163d9ea4b7bc7a259 | Recag/Rp_CommonC_737_1 | [
"region:us"
] | 2024-02-15T13:54:39+00:00 | {} | 2024-02-16T12:49:35+00:00 |
|
5fd36a774653bca7ceba86bb6546fb961e0a33bb | SaramNick/ruwhisper_test | [
"region:us"
] | 2024-02-15T13:54:39+00:00 | {} | 2024-02-15T15:39:40+00:00 |
|
5699d075423f04a9f38b38daac18469707c6871d | Recag/Rp_CommonC_737_3 | [
"region:us"
] | 2024-02-15T13:55:31+00:00 | {} | 2024-02-16T12:52:39+00:00 |
|
c1bc0206ca1660676144f3fd94b5fc0980d466a0 | moaminsharifi/Churn_Modelling | [
"region:us"
] | 2024-02-15T13:56:29+00:00 | {} | 2024-02-15T13:57:04+00:00 |
|
0de27a30052a7466d4dd823ac4bdafce0e3d361c | Recag/Rp_CommonC_655_1 | [
"region:us"
] | 2024-02-15T13:58:45+00:00 | {} | 2024-02-15T14:03:39+00:00 |
|
57d402f3b210e83461f6d2a4b5823d242d9a7958 | Recag/Rp_CommonC_655_2 | [
"region:us"
] | 2024-02-15T13:58:58+00:00 | {} | 2024-02-15T14:02:33+00:00 |
|
3d14f087ad2a3124a15886a2e63114db784ccfb1 | Recag/Rp_CommonC_657_1 | [
"region:us"
] | 2024-02-15T14:00:10+00:00 | {} | 2024-02-15T14:04:28+00:00 |
|
1be8fa6fe2650198dc47c37379985feaf24323c9 | Recag/Rp_CommonC_657_2 | [
"region:us"
] | 2024-02-15T14:00:20+00:00 | {} | 2024-02-15T14:03:56+00:00 |
|
8c8cb2a16e6374087aab2383a78721d42fe9c583 | Damodaran/demoTestSetDonut | [
"region:us"
] | 2024-02-15T14:23:11+00:00 | {} | 2024-02-15T14:23:11+00:00 |
|
d742c6dc08a9531c161bffb15f4425d4a054ddc0 | huggingface/figma-Playground-Inference-for-PRO-s-Website | [
"region:us"
] | 2024-02-15T14:40:52+00:00 | {} | 2024-02-15T14:40:52+00:00 |
|
74d48f36edafd0ca1dd60062c99386d3877bfb5f | Orenbac/amz-press-release_summarized | [
"region:us"
] | 2024-02-15T14:59:38+00:00 | {} | 2024-02-17T14:06:31+00:00 |
|
adb49ea9bc6a37cda5be4b18f28b2ee8582045c8 | AlisaMenekse/ErrorCategoriesBCP_10k_rows | [
"region:us"
] | 2024-02-15T15:11:18+00:00 | {} | 2024-02-15T15:13:06+00:00 |
|
e6f6cc9f294d34072a1f674fe81c4afb16d5f192 | enzostvs/figma-plugin-export-frame-to-url | [
"region:us"
] | 2024-02-15T15:26:49+00:00 | {} | 2024-02-15T16:52:02+00:00 |
|
eb5a7ceb7b4a6a47d57e013a808e7bbf55c56168 | Drewskidang/mix_genral | [
"region:us"
] | 2024-02-15T15:32:06+00:00 | {} | 2024-02-16T14:51:20+00:00 |
|
da9c5a516eb71c8da438a53421797a09b95af31a | Drewskidang/ragcomparison | [
"region:us"
] | 2024-02-15T15:46:26+00:00 | {} | 2024-02-15T15:46:38+00:00 |
|
728d5e4963056605837900741d2aed6755060fd1 | Avinier/docker-llm-conversations-v2 | [
"region:us"
] | 2024-02-15T16:03:36+00:00 | {} | 2024-02-15T16:04:13+00:00 |
|
737e6dda9b5f5f056ad1340d4e33483cb2aaa9b2 | mecxlan/brain_images | [
"region:us"
] | 2024-02-15T16:48:30+00:00 | {} | 2024-02-15T16:49:22+00:00 |
|
9e7b96501ec9087337b6e9fa9cd935eb2dfc8283 | Instincts003/p2t-dataset | [
"region:us"
] | 2024-02-15T17:00:55+00:00 | {} | 2024-02-15T17:19:22+00:00 |
|
17c8e6800e238d4e671cff3c7a6cf5c018eda051 | oliverbob/rev1 | [
"region:us"
] | 2024-02-15T17:25:26+00:00 | {} | 2024-02-16T10:11:58+00:00 |
|
c2d8b504a85e32db96bf1c893e4bef6c540020b6 | Bazou/BoobAI | [
"region:us"
] | 2024-02-15T17:50:39+00:00 | {} | 2024-02-15T17:51:06+00:00 |
|
e2f7874bf152a4932ce74a0e124391134bf7b520 | AsemBadr/Al-Rahman | [
"region:us"
] | 2024-02-15T18:03:34+00:00 | {} | 2024-02-15T21:06:22+00:00 |
|
af85f3cec7a812a827474f27a2fa6dfa9665841d | toninhodjj/niki | [
"region:us"
] | 2024-02-15T18:03:52+00:00 | {} | 2024-02-15T18:08:28+00:00 |
|
f85503d964c24a6141b4681735961f52f2858b2e | rodrigotborges/superaudio | [
"region:us"
] | 2024-02-15T18:11:52+00:00 | {} | 2024-02-15T18:15:11+00:00 |
|
e6961f00e5bbefbd4433026a2f17cc7cd4922135 | crncskn/try123 | [
"region:us"
] | 2024-02-15T18:17:46+00:00 | {} | 2024-02-15T18:20:45+00:00 |
|
08326f3ca8807568aa57f76f655fd3ac6f100a75 | rodrigotborges/superaudio2 | [
"region:us"
] | 2024-02-15T18:36:22+00:00 | {} | 2024-02-15T18:37:20+00:00 |
|
91f1104f569a4c06786acb1bae46000e75e13305 | sasha/co2_models | [
"region:us"
] | 2024-02-15T18:48:08+00:00 | {} | 2024-02-16T22:43:01+00:00 |
|
4b15137b7509b9a35dac808b11cc9ae8ce477ad1 | siranli/state-extract | [
"region:us"
] | 2024-02-15T18:53:58+00:00 | {} | 2024-02-15T18:54:33+00:00 |
|
31447d72d83e39a1f0fd3ac41c07da7ba1c9344e | grahvi4545/myVectorStore | [
"license:apache-2.0",
"region:us"
] | 2024-02-15T19:46:18+00:00 | {"license": "apache-2.0"} | 2024-02-15T19:46:18+00:00 |
|
f4e9c7d0423e55e23454f4fd2054efb0e5f0a3c9 | argilla/OpenHermes-2.5-dpo-ckpt2-auto | [
"region:us"
] | 2024-02-15T19:49:04+00:00 | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "weight", "dtype": "float64"}]}, {"name": "input", "dtype": "string"}, {"name": "generation_model", "sequence": "string"}, {"name": "generation_prompt", "sequence": "string"}, {"name": "raw_generation_responses", "sequence": "string"}, {"name": "generations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1181907830, "num_examples": 207569}], "download_size": 576131972, "dataset_size": 1181907830}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T11:20:48+00:00 |
|
09dfd53b92a4990d45272db95f58ccf6b7f38c5a | davidyoungoc/mini-platypus | [
"region:us"
] | 2024-02-15T19:50:59+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4186564, "num_examples": 1000}], "download_size": 2245921, "dataset_size": 4186564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T19:52:33+00:00 |
|
365f1eec822457d5c032b7308bd47b6bce4313c7 | Batraquio1234/Batraquio | [
"region:us"
] | 2024-02-15T19:52:54+00:00 | {} | 2024-02-15T20:03:15+00:00 |
|
4def4661f8501e1da652cd9b5e185dbabb93d7fb | https://huggingface.co/datasets/mhenrichsen/context-aware-splits-english | PocketDoc/text-splitter-alpaca | [
"task_categories:text-generation",
"language:en",
"region:us"
] | 2024-02-15T19:59:32+00:00 | {"language": ["en"], "task_categories": ["text-generation"]} | 2024-02-16T22:27:17+00:00 |
f789247e23bc4bc0fbd364185891bde193655194 | alisson40889/domiro | [
"license:openrail",
"region:us"
] | 2024-02-15T20:05:13+00:00 | {"license": "openrail"} | 2024-02-15T20:06:27+00:00 |
|
4f3df1e4e44270f748866fc8fd7f5a6c60949008 | loubnabnl/math_gradeschool | [
"region:us"
] | 2024-02-15T20:18:16+00:00 | {"dataset_info": {"features": [{"name": "completion", "dtype": "string"}, {"name": "prompt_grade_school", "dtype": "string"}, {"name": "token_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19283248, "num_examples": 5000}], "download_size": 9681102, "dataset_size": 19283248}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T20:18:17+00:00 |
|
3be6278fce61be65736c0adfe3d61ff990fb89e5 | loubnabnl/math_college | [
"region:us"
] | 2024-02-15T20:18:45+00:00 | {"dataset_info": {"features": [{"name": "prompt_college", "dtype": "string"}, {"name": "token_length", "dtype": "int64"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25108775, "num_examples": 5000}], "download_size": 12716387, "dataset_size": 25108775}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T20:18:46+00:00 |
|
7877c7345408921a8984b64b773d11608cc2974d | Kiurachi/lggj | [
"license:openrail",
"region:us"
] | 2024-02-15T20:19:43+00:00 | {"license": "openrail"} | 2024-02-15T20:19:44+00:00 |
|
98aeabdbed04b8e48e1a43763805d5f561c8740d | marcones/elementar1 | [
"license:openrail",
"region:us"
] | 2024-02-15T20:20:59+00:00 | {"license": "openrail"} | 2024-02-15T20:21:38+00:00 |
|
1d6ee463376553b7781730c8a95dfad5769e66ec | VatsaDev/oh2.5-text | [
"license:mit",
"region:us"
] | 2024-02-15T20:24:06+00:00 | {"license": "mit"} | 2024-02-16T16:32:49+00:00 |
|
01261b44a551a41a937f733d650a9c42594fe3fb | macadeliccc/distilabel-neurology-instructions | [
"region:us"
] | 2024-02-15T20:24:57+00:00 | {"dataset_info": {"features": [{"name": "instructions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 372401, "num_examples": 4000}], "download_size": 96796, "dataset_size": 372401}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T20:24:59+00:00 |
|
33a1e962efc1668084958d77d16354acef1d7746 |
# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-34B-ties
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [louisbrulenaudet/Pearl-34B-ties](https://huggingface.co/louisbrulenaudet/Pearl-34B-ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_louisbrulenaudet__Pearl-34B-ties",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T20:29:21.982361](https://huggingface.co/datasets/open-llm-leaderboard/details_louisbrulenaudet__Pearl-34B-ties/blob/main/results_2024-02-15T20-29-21.982361.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7624896367346236,
"acc_stderr": 0.02823253317418589,
"acc_norm": 0.7667330036075873,
"acc_norm_stderr": 0.028764116967369732,
"mc1": 0.5336597307221542,
"mc1_stderr": 0.017463793867168106,
"mc2": 0.7032022498819784,
"mc2_stderr": 0.014189265275795037
},
"harness|arc:challenge|25": {
"acc": 0.6791808873720137,
"acc_stderr": 0.01364094309194653,
"acc_norm": 0.7098976109215017,
"acc_norm_stderr": 0.013261573677520767
},
"harness|hellaswag|10": {
"acc": 0.6525592511451902,
"acc_stderr": 0.004751840646730855,
"acc_norm": 0.8483369846644094,
"acc_norm_stderr": 0.0035796087435066093
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7481481481481481,
"acc_stderr": 0.03749850709174021,
"acc_norm": 0.7481481481481481,
"acc_norm_stderr": 0.03749850709174021
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8,
"acc_stderr": 0.024618298195866518,
"acc_norm": 0.8,
"acc_norm_stderr": 0.024618298195866518
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.8958333333333334,
"acc_norm_stderr": 0.025545239210256917
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.52,
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
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},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.8,
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},
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8361344537815126,
"acc_stderr": 0.024044054940440488,
"acc_norm": 0.8361344537815126,
"acc_norm_stderr": 0.024044054940440488
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.5231788079470199,
"acc_norm_stderr": 0.04078093859163085
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm_stderr": 0.011558198113769584
},
"harness|hendrycksTest-high_school_statistics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
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"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.018869514646658928
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9071729957805907,
"acc_stderr": 0.01888975055095671,
"acc_norm": 0.9071729957805907,
"acc_norm_stderr": 0.01888975055095671
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
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"acc_norm_stderr": 0.03176683948640406
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9444444444444444,
"acc_stderr": 0.015006312806446912,
"acc_norm": 0.9444444444444444,
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.8055865921787709,
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},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8398692810457516,
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"acc_norm": 0.8398692810457516,
"acc_norm_stderr": 0.020998740930362303
},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.8703703703703703,
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8489795918367347,
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"acc_norm": 0.8489795918367347,
"acc_norm_stderr": 0.022923004094736854
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8955223880597015,
"acc_stderr": 0.021628920516700643,
"acc_norm": 0.8955223880597015,
"acc_norm_stderr": 0.021628920516700643
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
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"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
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"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598053
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8830409356725146,
"acc_stderr": 0.024648068961366152,
"acc_norm": 0.8830409356725146,
"acc_norm_stderr": 0.024648068961366152
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5336597307221542,
"mc1_stderr": 0.017463793867168106,
"mc2": 0.7032022498819784,
"mc2_stderr": 0.014189265275795037
},
"harness|winogrande|5": {
"acc": 0.8263614838200474,
"acc_stderr": 0.010646116480330996
},
"harness|gsm8k|5": {
"acc": 0.6747536012130402,
"acc_stderr": 0.012903904752543913
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_louisbrulenaudet__Pearl-34B-ties | [
"region:us"
] | 2024-02-15T20:31:45+00:00 | {"pretty_name": "Evaluation run of louisbrulenaudet/Pearl-34B-ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [louisbrulenaudet/Pearl-34B-ties](https://huggingface.co/louisbrulenaudet/Pearl-34B-ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_louisbrulenaudet__Pearl-34B-ties\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T20:29:21.982361](https://huggingface.co/datasets/open-llm-leaderboard/details_louisbrulenaudet__Pearl-34B-ties/blob/main/results_2024-02-15T20-29-21.982361.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7624896367346236,\n \"acc_stderr\": 0.02823253317418589,\n \"acc_norm\": 0.7667330036075873,\n \"acc_norm_stderr\": 0.028764116967369732,\n \"mc1\": 0.5336597307221542,\n \"mc1_stderr\": 0.017463793867168106,\n \"mc2\": 0.7032022498819784,\n \"mc2_stderr\": 0.014189265275795037\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6791808873720137,\n \"acc_stderr\": 0.01364094309194653,\n \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520767\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6525592511451902,\n \"acc_stderr\": 0.004751840646730855,\n \"acc_norm\": 0.8483369846644094,\n \"acc_norm_stderr\": 0.0035796087435066093\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7481481481481481,\n \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.7481481481481481,\n \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.024618298195866518,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866518\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424056,\n \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424056\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7379310344827587,\n \"acc_stderr\": 0.036646663372252565,\n \"acc_norm\": 0.7379310344827587,\n \"acc_norm_stderr\": 0.036646663372252565\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7248677248677249,\n \"acc_stderr\": 0.023000086859068642,\n \"acc_norm\": 0.7248677248677249,\n \"acc_norm_stderr\": 0.023000086859068642\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6403940886699507,\n \"acc_stderr\": 0.03376458246509567,\n \"acc_norm\": 0.6403940886699507,\n \"acc_norm_stderr\": 0.03376458246509567\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9292929292929293,\n \"acc_stderr\": 0.018263105420199488,\n \"acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.018263105420199488\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.011464523356953162,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.011464523356953162\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.01967163241310029,\n \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.01967163241310029\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.45555555555555555,\n \"acc_stderr\": 0.03036486250482443,\n \"acc_norm\": 0.45555555555555555,\n \"acc_norm_stderr\": 0.03036486250482443\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8361344537815126,\n \"acc_stderr\": 0.024044054940440488,\n \"acc_norm\": 0.8361344537815126,\n \"acc_norm_stderr\": 0.024044054940440488\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5231788079470199,\n \"acc_stderr\": 0.04078093859163085,\n \"acc_norm\": 0.5231788079470199,\n \"acc_norm_stderr\": 0.04078093859163085\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769584,\n \"acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769584\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9071729957805907,\n \"acc_stderr\": 0.01888975055095671,\n \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.01888975055095671\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342323,\n \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342323\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8842975206611571,\n \"acc_stderr\": 0.02919980245562281,\n \"acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.02919980245562281\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.02632138319878367,\n \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.02632138319878367\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.03176683948640406,\n \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.03176683948640406\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n \"acc_stderr\": 0.015006312806446912,\n \"acc_norm\": 0.9444444444444444,\n \"acc_norm_stderr\": 0.015006312806446912\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.909323116219668,\n \"acc_stderr\": 0.010268429662528548,\n \"acc_norm\": 0.909323116219668,\n \"acc_norm_stderr\": 0.010268429662528548\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.8055865921787709,\n \"acc_stderr\": 0.013235808096742286,\n \"acc_norm\": 0.8055865921787709,\n \"acc_norm_stderr\": 0.013235808096742286\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.797427652733119,\n \"acc_stderr\": 0.02282731749105969,\n \"acc_norm\": 0.797427652733119,\n \"acc_norm_stderr\": 0.02282731749105969\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062075,\n \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062075\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6276595744680851,\n \"acc_stderr\": 0.02883892147125145,\n \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.02883892147125145\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5808344198174706,\n \"acc_stderr\": 0.012602244505788228,\n \"acc_norm\": 0.5808344198174706,\n \"acc_norm_stderr\": 0.012602244505788228\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.023157468308559342,\n \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.023157468308559342\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.815359477124183,\n \"acc_stderr\": 0.01569702924075778,\n \"acc_norm\": 0.815359477124183,\n \"acc_norm_stderr\": 0.01569702924075778\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736854,\n \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736854\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5336597307221542,\n \"mc1_stderr\": 0.017463793867168106,\n \"mc2\": 0.7032022498819784,\n \"mc2_stderr\": 0.014189265275795037\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8263614838200474,\n \"acc_stderr\": 0.010646116480330996\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6747536012130402,\n \"acc_stderr\": 0.012903904752543913\n }\n}\n```", "repo_url": "https://huggingface.co/louisbrulenaudet/Pearl-34B-ties", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|arc:challenge|25_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|gsm8k|5_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|hellaswag|10_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T20-29-21.982361.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T20-29-21.982361.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", 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["**/details_harness|truthfulqa:mc|0_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["**/details_harness|winogrande|5_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-15T20-29-21.982361.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_15T20_29_21.982361", "path": ["results_2024-02-15T20-29-21.982361.parquet"]}, {"split": "latest", "path": ["results_2024-02-15T20-29-21.982361.parquet"]}]}]} | 2024-02-15T20:32:19+00:00 |
b019803e0a99af90e6f01dd6d8a4603b018c8541 | mikeg2/vozclaude | [
"license:openrail",
"region:us"
] | 2024-02-15T20:33:39+00:00 | {"license": "openrail"} | 2024-02-15T20:34:04+00:00 |
|
aff8f19ac53f559b73b17b508cbccf0ce7dcca05 | sxandie/arti_kushwaha_cat | [
"region:us"
] | 2024-02-15T20:35:18+00:00 | {} | 2024-02-15T20:45:11+00:00 |
|
f2bdf6ac20a5f53a508bf14aa45662d10a74c65d | rookshanks/small-the_pile | [
"region:us"
] | 2024-02-15T20:36:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "perplexity_score", "dtype": "float64"}, {"name": "pile_set_name", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 484845334.4, "num_examples": 80000}, {"name": "validation", "num_bytes": 60605666.8, "num_examples": 10000}, {"name": "test", "num_bytes": 60605666.8, "num_examples": 10000}], "download_size": 329390472, "dataset_size": 606056667.9999999}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-15T20:36:47+00:00 |
|
5fb3b6308132804ce31daa3cc5629e43837c40a7 |
# Dataset Card for Evaluation run of BarraHome/Wistral-7B-Instruct-v0.4
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BarraHome/Wistral-7B-Instruct-v0.4](https://huggingface.co/BarraHome/Wistral-7B-Instruct-v0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T20:35:44.878136](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.4/blob/main/results_2024-02-15T20-35-44.878136.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6032184784518743,
"acc_stderr": 0.03333730204729809,
"acc_norm": 0.607891645213564,
"acc_norm_stderr": 0.03401402537730786,
"mc1": 0.5226438188494492,
"mc1_stderr": 0.01748554225848964,
"mc2": 0.6766513448639357,
"mc2_stderr": 0.015264009667659464
},
"harness|arc:challenge|25": {
"acc": 0.575938566552901,
"acc_stderr": 0.014441889627464392,
"acc_norm": 0.6220136518771331,
"acc_norm_stderr": 0.0141696645203031
},
"harness|hellaswag|10": {
"acc": 0.6612228639713205,
"acc_stderr": 0.004723266971563391,
"acc_norm": 0.8481378211511651,
"acc_norm_stderr": 0.0035815378475817935
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6597222222222222,
"acc_stderr": 0.039621355734862175,
"acc_norm": 0.6597222222222222,
"acc_norm_stderr": 0.039621355734862175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5838150289017341,
"acc_stderr": 0.03758517775404947,
"acc_norm": 0.5838150289017341,
"acc_norm_stderr": 0.03758517775404947
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5148936170212766,
"acc_stderr": 0.03267151848924777,
"acc_norm": 0.5148936170212766,
"acc_norm_stderr": 0.03267151848924777
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.43859649122807015,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.025010749116137602,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.025010749116137602
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.4 | [
"region:us"
] | 2024-02-15T20:38:05+00:00 | {"pretty_name": "Evaluation run of BarraHome/Wistral-7B-Instruct-v0.4", "dataset_summary": "Dataset automatically created during the evaluation run of model [BarraHome/Wistral-7B-Instruct-v0.4](https://huggingface.co/BarraHome/Wistral-7B-Instruct-v0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T20:35:44.878136](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.4/blob/main/results_2024-02-15T20-35-44.878136.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6032184784518743,\n \"acc_stderr\": 0.03333730204729809,\n \"acc_norm\": 0.607891645213564,\n \"acc_norm_stderr\": 0.03401402537730786,\n \"mc1\": 0.5226438188494492,\n \"mc1_stderr\": 0.01748554225848964,\n \"mc2\": 0.6766513448639357,\n \"mc2_stderr\": 0.015264009667659464\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.575938566552901,\n \"acc_stderr\": 0.014441889627464392,\n \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.0141696645203031\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6612228639713205,\n \"acc_stderr\": 0.004723266971563391,\n \"acc_norm\": 0.8481378211511651,\n \"acc_norm_stderr\": 0.0035815378475817935\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137602,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137602\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6774193548387096,\n \"acc_stderr\": 0.026593084516572277,\n \"acc_norm\": 0.6774193548387096,\n \"acc_norm_stderr\": 0.026593084516572277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["**/details_harness|winogrande|5_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-15T20-35-44.878136.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_15T20_35_44.878136", "path": ["results_2024-02-15T20-35-44.878136.parquet"]}, {"split": "latest", "path": ["results_2024-02-15T20-35-44.878136.parquet"]}]}]} | 2024-02-15T20:38:32+00:00 |
0cebb47837c041c119e279e1ced85778416ef515 | thecaipirinhachannel/serran | [
"region:us"
] | 2024-02-15T20:38:28+00:00 | {} | 2024-02-15T20:39:18+00:00 |
|
3111b14a735e48aaa9d5f39aaa33856b8aaba4d2 | RadAlienware/test1ultrachat | [
"license:mit",
"region:us"
] | 2024-02-15T20:46:09+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "Content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5722, "num_examples": 1}, {"name": "test", "num_bytes": 5324, "num_examples": 1}], "download_size": 5524, "dataset_size": 11046}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-15T21:00:00+00:00 |
|
2f65c90484ced4b4482a0403eaed26a18fb5765c | YingJie0202/test | [
"region:us"
] | 2024-02-15T20:52:56+00:00 | {} | 2024-02-15T21:20:00+00:00 |
|
7bd1bd18f2807a42d04caddaf7f90ef309f25983 | xPXXX/test_ragas | [
"license:mit",
"region:us"
] | 2024-02-15T21:07:10+00:00 | {"license": "mit"} | 2024-02-16T00:51:44+00:00 |
|
418dfbba1351ac18742a1bc8f7428d5fbc0150c8 |
Dataset for human eval infill for java, based on https://arxiv.org/pdf/2207.14255
| njkumarr/humanevalinfilljava | [
"language:en",
"arxiv:2207.14255",
"region:us"
] | 2024-02-15T21:25:22+00:00 | {"language": ["en"], "pretty_name": "HumanEval-Infilling Java"} | 2024-02-16T07:43:34+00:00 |
8ff91a7ff0389de4e31636e8305fddc3e3a2df35 | unigram/fol-04 | [
"region:us"
] | 2024-02-15T21:29:12+00:00 | {"dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "premise_tptp", "dtype": "string"}, {"name": "hypothesis_tptp", "dtype": "string"}, {"name": "deberta_pred", "dtype": "string"}, {"name": "deberta_pred_r1", "dtype": "string"}, {"name": "deberta_pred_r2", "dtype": "string"}, {"name": "deberta_pred_r3", "dtype": "string"}, {"name": "deberta_pred_r4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2197971, "num_examples": 1989}, {"name": "validation", "num_bytes": 387374, "num_examples": 375}, {"name": "test", "num_bytes": 370138, "num_examples": 339}], "download_size": 953363, "dataset_size": 2955483}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-15T21:29:15+00:00 |
|
b12fc65cb8d2fd75335ea8ce2fa64a4be9f8fa7c |
## Dataset Description
- **Repository:** [https://github.com/nlp-uoregon/CulturaX](https://github.com/nlp-uoregon/CulturaX)
- **Papers:** [CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://arxiv.org/abs/2309.09400)
## Dataset Summary
We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. We employ MinHash at document level to achieve fuzzy deduplication for the datasets in different languages. Our data cleaning framework includes diverse criteria and threshold selections, guided by extensive data samples, ensuring comprehensive noise filtering in various aspects. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs.
Our dataset combines the most recent iteration of mC4 (version 3.1.0) [1] with all accessible OSCAR corpora up to the present year, including 20.19, 21.09, 22.01, and 23.01 [2]. After deep cleaning and deduplication, CulturaX involves 16TB data in the parquet format (expanding to 27TB when unpacked). More than a half of our dataset is dedicated to non-English languages to significantly boost the data size and enhance the feasibility of training models in multilingual scenarios.
To obtain perplexity scores for data cleaning, we train a SentencePiece tokenizer and 5-gram Kneser-Ney language models as provided in the KenLM library [3] using the 20230501 dumps of Wikipedia. Our KenLM models are also released in HuggingFace: https://huggingface.co/uonlp/kenlm.
Details for the dataset can be found in our technical paper: [https://arxiv.org/abs/2309.09400](https://arxiv.org/abs/2309.09400)
You can download the dataset using Hugging Face datasets:
*You may need to follow these instructions to setup authentication before downloading the dataset: [https://huggingface.co/docs/huggingface_hub/quick-start#login](https://huggingface.co/docs/huggingface_hub/quick-start#login)*
```python
from datasets import load_dataset
ds = load_dataset("uonlp/CulturaX",
"en",
use_auth_token=True)
```
### Languages
The supported languages and statistics for our dataset can be found below:
*(Note that the language code `als` and `eml` refer to `gsw` and `x-eml` in the OSCAR-2301 dataset.)*
| | Code | Language | # Documents | # Tokens | # Tokens (%) |
|----:|:-------|:-------------------------|:----------------|:--------------------|:------|
| 0 | en | English | 3,241,065,682 | 2,846,970,578,793 | 45.13 |
| 1 | ru | Russian | 799,310,908 | 737,201,800,363 | 11.69 |
| 2 | es | Spanish | 450,937,645 | 373,845,662,394 | 5.93 |
| 3 | de | German | 420,017,484 | 357,030,348,021 | 5.66 |
| 4 | fr | French | 363,754,348 | 319,332,674,695 | 5.06 |
| 5 | zh | Chinese | 218,624,604 | 227,055,380,882 | 3.60 |
| 6 | it | Italian | 211,309,922 | 165,446,410,843 | 2.62 |
| 7 | pt | Portuguese | 190,289,658 | 136,941,763,923 | 2.17 |
| 8 | pl | Polish | 142,167,217 | 117,269,087,143 | 1.86 |
| 9 | ja | Japanese | 111,188,475 | 107,873,841,351 | 1.71 |
| 10 | nl | Dutch | 117,392,666 | 80,032,209,900 | 1.27 |
| 11 | ar | Arabic | 74,027,952 | 69,354,335,076 | 1.10 |
| 12 | tr | Turkish | 94,207,460 | 64,292,787,164 | 1.02 |
| 13 | cs | Czech | 65,350,564 | 56,910,486,745 | 0.90 |
| 14 | vi | Vietnamese | 57,606,341 | 55,380,123,774 | 0.88 |
| 15 | fa | Persian | 59,531,144 | 45,947,657,495 | 0.73 |
| 16 | hu | Hungarian | 44,132,152 | 43,417,981,714 | 0.69 |
| 17 | el | Greek | 51,430,226 | 43,147,590,757 | 0.68 |
| 18 | ro | Romanian | 40,325,424 | 39,647,954,768 | 0.63 |
| 19 | sv | Swedish | 49,709,189 | 38,486,181,494 | 0.61 |
| 20 | uk | Ukrainian | 44,740,545 | 38,226,128,686 | 0.61 |
| 21 | fi | Finnish | 30,467,667 | 28,925,009,180 | 0.46 |
| 22 | ko | Korean | 20,557,310 | 24,765,448,392 | 0.39 |
| 23 | da | Danish | 25,429,808 | 22,921,651,314 | 0.36 |
| 24 | bg | Bulgarian | 24,131,819 | 22,917,954,776 | 0.36 |
| 25 | no | Norwegian | 18,907,310 | 18,426,628,868 | 0.29 |
| 26 | hi | Hindi | 19,665,355 | 16,791,362,871 | 0.27 |
| 27 | sk | Slovak | 18,582,517 | 16,442,669,076 | 0.26 |
| 28 | th | Thai | 20,960,550 | 15,717,374,014 | 0.25 |
| 29 | lt | Lithuanian | 13,339,785 | 14,247,110,836 | 0.23 |
| 30 | ca | Catalan | 15,531,777 | 12,530,288,006 | 0.20 |
| 31 | id | Indonesian | 23,251,368 | 12,062,966,061 | 0.19 |
| 32 | bn | Bangla | 12,436,596 | 9,572,929,804 | 0.15 |
| 33 | et | Estonian | 8,004,753 | 8,805,656,165 | 0.14 |
| 34 | sl | Slovenian | 7,335,378 | 8,007,587,522 | 0.13 |
| 35 | lv | Latvian | 7,136,587 | 7,845,180,319 | 0.12 |
| 36 | he | Hebrew | 4,653,979 | 4,937,152,096 | 0.08 |
| 37 | sr | Serbian | 4,053,166 | 4,619,482,725 | 0.07 |
| 38 | ta | Tamil | 4,728,460 | 4,378,078,610 | 0.07 |
| 39 | sq | Albanian | 5,205,579 | 3,648,893,215 | 0.06 |
| 40 | az | Azerbaijani | 5,084,505 | 3,513,351,967 | 0.06 |
| 41 | kk | Kazakh | 2,733,982 | 2,802,485,195 | 0.04 |
| 42 | ur | Urdu | 2,757,279 | 2,703,052,627 | 0.04 |
| 43 | ka | Georgian | 3,120,321 | 2,617,625,564 | 0.04 |
| 44 | hy | Armenian | 2,964,488 | 2,395,179,284 | 0.04 |
| 45 | is | Icelandic | 2,373,560 | 2,350,592,857 | 0.04 |
| 46 | ml | Malayalam | 2,693,052 | 2,100,556,809 | 0.03 |
| 47 | ne | Nepali | 3,124,040 | 2,061,601,961 | 0.03 |
| 48 | mk | Macedonian | 2,762,807 | 2,003,302,006 | 0.03 |
| 49 | mr | Marathi | 2,266,588 | 1,955,227,796 | 0.03 |
| 50 | mn | Mongolian | 1,928,828 | 1,850,667,656 | 0.03 |
| 51 | be | Belarusian | 1,643,486 | 1,791,473,041 | 0.03 |
| 52 | te | Telugu | 1,822,865 | 1,566,972,146 | 0.02 |
| 53 | gl | Galician | 1,785,963 | 1,382,539,693 | 0.02 |
| 54 | eu | Basque | 1,598,822 | 1,262,066,759 | 0.02 |
| 55 | kn | Kannada | 1,352,142 | 1,242,285,201 | 0.02 |
| 56 | gu | Gujarati | 1,162,878 | 1,131,730,537 | 0.02 |
| 57 | af | Afrikaans | 826,519 | 1,119,009,767 | 0.02 |
| 58 | my | Burmese | 865,575 | 882,606,546 | 0.01 |
| 59 | si | Sinhala | 753,655 | 880,289,097 | 0.01 |
| 60 | eo | Esperanto | 460,088 | 803,948,528 | 0.01 |
| 61 | km | Khmer | 1,013,181 | 746,664,132 | 0.01 |
| 62 | pa | Punjabi | 646,987 | 727,546,145 | 0.01 |
| 63 | cy | Welsh | 549,955 | 576,743,162 | 0.01 |
| 64 | ky | Kyrgyz | 570,922 | 501,442,620 | 0.01 |
| 65 | ga | Irish | 304,251 | 376,947,935 | 0.01 |
| 66 | ps | Pashto | 376,914 | 363,007,770 | 0.01 |
| 67 | am | Amharic | 243,349 | 358,206,762 | 0.01 |
| 68 | ku | Kurdish | 295,314 | 302,990,910 | 0.00 |
| 69 | tl | Filipino | 348,453 | 242,086,456 | 0.00 |
| 70 | yi | Yiddish | 141,156 | 217,584,643 | 0.00 |
| 71 | lo | Lao | 217,842 | 168,256,876 | 0.00 |
| 72 | fy | Western Frisian | 223,268 | 167,193,111 | 0.00 |
| 73 | sd | Sindhi | 109,162 | 147,487,058 | 0.00 |
| 74 | mg | Malagasy | 115,910 | 142,685,412 | 0.00 |
| 75 | or | Odia | 153,461 | 100,323,213 | 0.00 |
| 76 | as | Assamese | 52,627 | 83,787,896 | 0.00 |
| 77 | ug | Uyghur | 47,035 | 77,677,306 | 0.00 |
| 78 | uz | Uzbek | 87,219 | 75,250,787 | 0.00 |
| 79 | la | Latin | 48,968 | 44,176,580 | 0.00 |
| 80 | hr | Croatian | 460,690 | 40,796,811 | 0.00 |
| 81 | sw | Swahili | 66,506 | 30,708,309 | 0.00 |
| 82 | ms | Malay | 238,151 | 19,375,976 | 0.00 |
| 83 | br | Breton | 43,765 | 13,987,037 | 0.00 |
| 84 | sa | Sanskrit | 16,290 | 13,561,367 | 0.00 |
| 85 | gd | Scottish Gaelic | 8,408 | 4,796,485 | 0.00 |
| 86 | su | Sundanese | 1,554 | 1,308,460 | 0.00 |
| 87 | jv | Javanese | 2,058 | 625,429 | 0.00 |
| 88 | tg | Tajik | 483,835 | - | - |
| 89 | ceb | Cebuano | 263,890 | - | - |
| 90 | tt | Tatar | 218,102 | - | - |
| 91 | ckb | Central Kurdish | 172,035 | - | - |
| 92 | lb | Luxembourgish | 165,891 | - | - |
| 93 | mt | Maltese | 151,320 | - | - |
| 94 | nn | Norwegian Nynorsk | 126,083 | - | - |
| 95 | qu | Quechua | 1,202 | 72,101 | 0.00 |
| 96 | ba | Bashkir | 71,957 | - | - |
| 97 | arz | Egyptian Arabic | 71,625 | - | - |
| 98 | dv | Divehi | 66,702 | - | - |
| 99 | bo | Tibetan | 54,185 | - | - |
| 100 | sh | Serbian (Latin) | 45,619 | - | - |
| 101 | yo | Yoruba | 192 | 42,943 | 0.00 |
| 102 | bs | Bosnian | 1,237 | 39,768 | 0.00 |
| 103 | azb | South Azerbaijani | 29,833 | - | - |
| 104 | ht | Haitian Creole | 12 | 26,183 | 0.00 |
| 105 | war | Waray | 23,687 | - | - |
| 106 | cv | Chuvash | 22,570 | - | - |
| 107 | sah | Sakha | 22,141 | - | - |
| 108 | li | Limburgish | 206 | 18,532 | 0.00 |
| 109 | ce | Chechen | 17,322 | - | - |
| 110 | pnb | Western Panjabi | 15,625 | - | - |
| 111 | nds | Low German | 15,139 | - | - |
| 112 | tk | Turkmen | 14,393 | - | - |
| 113 | gn | Guarani | 103 | 12,708 | 0.00 |
| 114 | oc | Occitan | 10,556 | - | - |
| 115 | xmf | Mingrelian | 9,706 | - | - |
| 116 | ast | Asturian | 9,002 | - | - |
| 117 | os | Ossetic | 8,596 | - | - |
| 118 | mhr | Eastern Mari | 7,883 | - | - |
| 119 | pms | Piedmontese | 7,566 | - | - |
| 120 | als[*] | Swiss German | 6,936 | - | - |
| 121 | vo | Volapük | 6,621 | - | - |
| 122 | so | Somali | 39 | 6,053 | 0.00 |
| 123 | bpy | Bishnupriya | 5,087 | - | - |
| 124 | new | Newari | 4,344 | - | - |
| 125 | hsb | Upper Sorbian | 4,244 | - | - |
| 126 | lmo | Lombard | 3,530 | - | - |
| 127 | an | Aragonese | 2,746 | - | - |
| 128 | ilo | Iloko | 2,328 | - | - |
| 129 | mzn | Mazanderani | 1,914 | - | - |
| 130 | lez | Lezghian | 1,806 | - | - |
| 131 | rm | Romansh | 30 | 1,769 | 0.00 |
| 132 | krc | Karachay-Balkar | 1,745 | - | - |
| 133 | min | Minangkabau | 1,429 | - | - |
| 134 | kv | Komi | 1,396 | - | - |
| 135 | wa | Walloon | 1,383 | - | - |
| 136 | jbo | Lojban | 1,349 | - | - |
| 137 | io | Ido | 1,144 | - | - |
| 138 | mrj | Western Mari | 1,056 | - | - |
| 139 | gom | Goan Konkani | 721 | - | - |
| 140 | ia | Interlingua | 613 | - | - |
| 141 | av | Avaric | 438 | - | - |
| 142 | bh | Bihari languages | 265 | - | - |
| 143 | wuu | Wu Chinese | 222 | - | - |
| 144 | nah | Nahuatl languages | 131 | - | - |
| 145 | vec | Venetian | 113 | - | - |
| 146 | bxr | Russia Buriat | 100 | - | - |
| 147 | kw | Cornish | 94 | - | - |
| 148 | mai | Maithili | 93 | - | - |
| 149 | eml[*] | Emiliano-Romagnol | 91 | - | - |
| 150 | dsb | Lower Sorbian | 59 | - | - |
| 151 | xal | Kalmyk | 51 | - | - |
| 152 | lrc | Northern Luri | 43 | - | - |
| 153 | nap | Neapolitan | 31 | - | - |
| 154 | tyv | Tuvinian | 23 | - | - |
| 155 | scn | Sicilian | 21 | - | - |
| 156 | frr | Northern Frisian | 11 | - | - |
| 157 | mwl | Mirandese | 9 | - | - |
| 158 | myv | Erzya | 4 | - | - |
| 159 | ie | Interlingue | 4 | - | - |
| 160 | pam | Pampanga | 4 | - | - |
| 161 | bar | Bavarian | 3 | - | - |
| 162 | yue | Yue Chinese | 3 | - | - |
| 163 | cbk | Chavacano | 2 | - | - |
| 164 | bcl | Central Bikol | 1 | - | - |
| 165 | vls | West Flemish | 1 | - | - |
| 166 | rue | Rusyn | 1 | - | - |
### Dataset Structure
```json
{
"text": ...,
"timestamp": ...,
"url": ...,
"source": "mc4" | "OSCAR-xxxx",
}
```
## Considerations for Using the Data
As CulturaX is the cleaned version of the mC4 and OSCAR datasets, which were both extracted from CommonCrawl, personal and sensitive information might still contain personal and sensitive information.
This must be considered prior to using this dataset for any purpose, such as training deep learning models, etc.
## License Information
The licence terms for CulturaX strictly follows those of `mC4` and `OSCAR`. Please refer to both below licenses when using this dataset.
- [mC4 license](https://huggingface.co/datasets/allenai/c4#license)
- [OSCAR license](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information)
## Citation
To cite CulturaX, please use:
```
@misc{nguyen2023culturax,
title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages},
author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen},
year={2023},
eprint={2309.09400},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Reference
[1] Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual
pre-trained text-to-text transformer. In NAACL 2021. https://huggingface.co/datasets/mc4
[2] Pedro Javier Ortiz Suárez, Benoît Sagot, and Laurent Romary. 2019. Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. In Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-
7) 2019. https://oscar-project.org/
[3] KenLM: Faster and smaller language model queries. In Proceedings of the Sixth
Workshop on Statistical Machine Translation, 2011.
| Madjakul/l-halversting | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
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"multilinguality:multilingual",
"source_datasets:original",
"language:af",
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"language:am",
"language:an",
"language:ar",
"language:arz",
"language:as",
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"language:en",
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"language:qu",
"language:rm",
"language:ro",
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"language:rue",
"language:sa",
"language:sah",
"language:scn",
"language:sd",
"language:sh",
"language:si",
"language:sk",
"language:sl",
"language:so",
"language:sq",
"language:sr",
"language:su",
"language:sv",
"language:sw",
"language:ta",
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"language:tg",
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"language:tk",
"language:tl",
"language:tr",
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"language:wa",
"language:war",
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"language:xal",
"language:xmf",
"language:yi",
"language:yo",
"language:yue",
"language:zh",
"arxiv:2309.09400",
"region:us"
] | 2024-02-15T21:36:53+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "als", "am", "an", "ar", "arz", "as", "ast", "av", "az", "azb", "ba", "bar", "bcl", "be", "bg", "bh", "bn", "bo", "bpy", "br", "bs", "bxr", "ca", "cbk", "ce", "ceb", "ckb", "cs", "cv", "cy", "da", "de", "dsb", "dv", "el", "eml", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "frr", "fy", "ga", "gd", "gl", "gn", "gom", "gu", "he", "hi", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ie", "ilo", "io", "is", "it", "ja", "jbo", "jv", "ka", "kk", "km", "kn", "ko", "krc", "ku", "kv", "kw", "ky", "la", "lb", "lez", "li", "lmo", "lo", "lrc", "lt", "lv", "mai", "mg", "mhr", "min", "mk", "ml", "mn", "mr", "mrj", "ms", "mt", "mwl", "my", "myv", "mzn", "nah", "nap", "nds", "ne", "new", "nl", "nn", "no", "oc", "or", "os", "pa", "pam", "pl", "pms", "pnb", "ps", "pt", "qu", "rm", "ro", "ru", "rue", "sa", "sah", "scn", "sd", "sh", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "tg", "th", "tk", "tl", "tr", "tt", "tyv", "ug", "uk", "ur", "uz", "vec", "vi", "vls", "vo", "wa", "war", "wuu", "xal", "xmf", "yi", "yo", "yue", "zh"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "LHALversting", "configs": [{"config_name": "de", "data_files": "de/*.tar.gz"}, {"config_name": "en", "data_files": "en/*.tar.gz"}, {"config_name": "fr", "data_files": "fr/*.tar.gz"}], "extra_gated_prompt": "By completing the form below, you acknowledge that the provided data is offered as is. Although we anticipate no problems, you accept full responsibility for any repercussions resulting from the use of this data. Furthermore, you agree that the data must not be utilized for malicious or harmful purposes towards humanity.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text", "Usecase": "text", "I have explicitly check with my jurisdiction and I confirm that downloading CulturaX is legal in the country/region where I am located right now, and for the use case that I have described above": "checkbox", "You agree to not attempt to determine the identity of individuals in this dataset": "checkbox"}} | 2024-02-16T19:53:17+00:00 |
88349b95507d871e30c9a5d2b862c34682315395 | trevorweston/monet | [
"region:us"
] | 2024-02-15T21:42:02+00:00 | {} | 2024-02-15T21:44:53+00:00 |
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