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"region:us"
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"region:us"
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"region:us"
] | 2024-01-13T01:15:04+00:00 | {} | 2024-01-13T01:57:41+00:00 |
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"region:us"
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"region:us"
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98f77c38f0a5655dc28b2f31614a2a7ef8d8328f | zhaospei/refine_scg | [
"region:us"
] | 2024-01-13T01:47:29+00:00 | {} | 2024-01-13T01:59:55+00:00 |
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"region:us"
] | 2024-01-13T01:49:53+00:00 | {} | 2024-01-13T01:51:28+00:00 |
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"region:us"
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"region:us"
] | 2024-01-13T02:12:03+00:00 | {} | 2024-01-13T07:41:42+00:00 |
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52d82165949ded43a48f47fb00ba34d398581364 | zhaospei/sample_30k | [
"region:us"
] | 2024-01-13T02:24:41+00:00 | {} | 2024-01-13T02:25:18+00:00 |
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"region:us"
] | 2024-01-13T02:37:57+00:00 | {} | 2024-01-14T14:23:22+00:00 |
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"region:us"
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"region:us"
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"region:us"
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"region:us"
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"region:us"
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"region:us"
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"region:us"
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"region:us"
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4bf3d1587f294a93cb1f4d943235d6aaaa7305f0 | Chinchis/Herramientas_reescalado | [
"region:us"
] | 2024-01-13T07:46:14+00:00 | {} | 2024-01-13T07:47:18+00:00 |
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845f6f604ac832e18d4f4582372d315b39e51a63 | radestijn/AutoOrca | [
"region:us"
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a37ab5782f48d165a293f8b3463ed85d2e585fe6 | jiiishnu-17/url.dataset | [
"region:us"
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2a9950740d1f5c0efa66c4249444e3f663a85903 | AlejandroLanaspa/Jewelry | [
"region:us"
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9743e663b4e2148439086ae99b7e5755ca2b04a9 | xiaoguang123/Pictures | [
"region:us"
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20d9d9de54faae2bb3c4a5e1c1b037009007c35b | chriscelaya/mini-platypus | [
"region:us"
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7c8d592436914b0606c49227a9f11d1be39d6fc7 | MattyMroz/SoloLeveling | [
"region:us"
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3bda59e21da3b16f19632de5214a7e03a2ee367f | cutterd/gelgen_tar_0 | [
"region:us"
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"region:us"
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"region:us"
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83a2cdf17c12369cff402703aa4bc089bc6f7b5f | cutterd/gelgen_tar_3 | [
"region:us"
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3500fca51def95f5bf4e785642405fb621f68268 | cutterd/gelgen_tar_35 | [
"region:us"
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|
8a8e22fd3762b82d0591964002971cae88566d44 | cutterd/gelgen_tar_34 | [
"region:us"
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e49769cc4784913ef1820d0fb4efde61c6a98b9e | cutterd/gelgen_tar_33 | [
"region:us"
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73cb70b339c7a322befa2aec36e957292f9da481 | cutterd/gelgen_tar_32 | [
"region:us"
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5e34965dd20f876efe266283e4b2e9d53db157fc | cutterd/gelgen_tar_26 | [
"region:us"
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|
ac0eef0e54c22fe920c12a61c3c7c3cb438c2f12 | cutterd/gelgen_tar_27 | [
"region:us"
] | 2024-01-13T11:23:07+00:00 | {} | 2024-01-13T12:39:05+00:00 |
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deac3a195af2e41879f8a30a5e719c354acbacd0 | cutterd/gelgen_tar_24 | [
"region:us"
] | 2024-01-13T11:23:07+00:00 | {} | 2024-01-13T12:40:21+00:00 |
|
76ba093dc3f8b390cafb4956e50a0d95eb8b9982 | cutterd/gelgen_tar_25 | [
"region:us"
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9721bc135dc06adba40095e958555b6fda02f74f | cutterd/gelgen_tar_28 | [
"region:us"
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527ae2b57cac30f7962df9bf497b99053c1a6f02 | cutterd/gelgen_tar_29 | [
"region:us"
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|
d6640e9f44b65419decebc1a7e5afbbc9287eee2 | cutterd/gelgen_tar_30 | [
"region:us"
] | 2024-01-13T11:23:20+00:00 | {} | 2024-01-13T12:37:33+00:00 |
|
0ae45e3d573ec812e9484709dca40347c3ed8915 | cutterd/gelgen_tar_31 | [
"region:us"
] | 2024-01-13T11:23:20+00:00 | {} | 2024-01-13T12:39:11+00:00 |
|
e718ac01407bed4892ce12c674df63b9afa76707 | cutterd/gelgen_tar_38 | [
"region:us"
] | 2024-01-13T11:23:21+00:00 | {} | 2024-01-13T12:42:23+00:00 |
|
d5391901a138349d59cc949acd4dccd8edd490cd | cutterd/gelgen_tar_37 | [
"region:us"
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|
8c40f8fcde02df962bdc894b90ec46d7bff6703d | cutterd/gelgen_tar_39 | [
"region:us"
] | 2024-01-13T11:23:21+00:00 | {} | 2024-01-13T12:43:06+00:00 |
|
c4fa37f0ff36d16fd8dcb96a5df2b69e78726a3d | cutterd/gelgen_tar_36 | [
"region:us"
] | 2024-01-13T11:23:22+00:00 | {} | 2024-01-13T12:41:24+00:00 |
|
ce575a37cd89ec1a049eb51f6d4027fa93ff861c | OthnnyEL/fgan-annotate-dataset | [
"region:us"
] | 2024-01-13T12:12:14+00:00 | {} | 2024-01-13T12:12:14+00:00 |
|
68481d611e17759532f811a7a899ef111789fcb8 | montebello-642/civillian-complaints | [
"region:us"
] | 2024-01-13T12:48:02+00:00 | {} | 2024-01-13T12:49:37+00:00 |
|
3b6227fe3c7ac7fdb008b9126700d612446367f5 | cutterd/gelgen_tar_45 | [
"region:us"
] | 2024-01-13T13:05:38+00:00 | {} | 2024-01-13T14:00:41+00:00 |
|
635ee7ecaa6b83a7a8b06ee7bc54ed30c931cc2e | cutterd/gelgen_tar_46 | [
"region:us"
] | 2024-01-13T13:05:38+00:00 | {} | 2024-01-13T13:59:01+00:00 |
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2960b4ab43b42b1e52dc4e5da38095bbee62d42e | cutterd/gelgen_tar_47 | [
"region:us"
] | 2024-01-13T13:05:38+00:00 | {} | 2024-01-13T13:59:58+00:00 |
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e8d70531b676ada7f3f3ed30bfb65a390dd3705b | cutterd/gelgen_tar_44 | [
"region:us"
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|
dc3530424c79872393356e6d0eac8b3ae299025d | cutterd/gelgen_tar_50 | [
"region:us"
] | 2024-01-13T13:05:49+00:00 | {} | 2024-01-13T13:59:12+00:00 |
|
d14742097a5e7818e662494ba2041d9eebee226e | cutterd/gelgen_tar_48 | [
"region:us"
] | 2024-01-13T13:05:49+00:00 | {} | 2024-01-13T13:57:25+00:00 |
|
46648e3a291d8072688c732649b7be5108296a75 | cutterd/gelgen_tar_51 | [
"region:us"
] | 2024-01-13T13:05:49+00:00 | {} | 2024-01-13T14:00:31+00:00 |
|
818b6eab5a2cfc9be23a9ad85f9572eddc891f4b | cutterd/gelgen_tar_49 | [
"region:us"
] | 2024-01-13T13:05:49+00:00 | {} | 2024-01-13T14:00:33+00:00 |
|
f1da79bbc42579738fa30c1c313f32f69d0e2b10 | cutterd/gelgen_tar_53 | [
"region:us"
] | 2024-01-13T13:06:00+00:00 | {} | 2024-01-13T14:12:02+00:00 |
|
b239f868549afb5faf2488c847cc80d21f7c9906 | cutterd/gelgen_tar_55 | [
"region:us"
] | 2024-01-13T13:06:00+00:00 | {} | 2024-01-13T14:13:29+00:00 |
|
41d24169fb57a7c404fe5aff3cfb0e4ec40955f5 | cutterd/gelgen_tar_52 | [
"region:us"
] | 2024-01-13T13:06:00+00:00 | {} | 2024-01-13T14:13:32+00:00 |
|
9b6ef1fd10be973a0dd7b6bbe6e9f08fb8160a47 | cutterd/gelgen_tar_57 | [
"region:us"
] | 2024-01-13T13:06:44+00:00 | {} | 2024-01-13T14:22:11+00:00 |
|
c251f032f511c513f483d82d95bdb52fab0d7a6a | cutterd/gelgen_tar_56 | [
"region:us"
] | 2024-01-13T13:06:44+00:00 | {} | 2024-01-13T14:20:35+00:00 |
|
6b04f1479252b874badede764d5ddf57b44b6f84 | cutterd/gelgen_tar_58 | [
"region:us"
] | 2024-01-13T13:06:44+00:00 | {} | 2024-01-13T14:21:44+00:00 |
|
e2e4abf6ed28ec1213af023c3e06cbbc1c0f3f4f | cutterd/gelgen_tar_59 | [
"region:us"
] | 2024-01-13T13:06:44+00:00 | {} | 2024-01-13T14:23:30+00:00 |
|
6a90d76e9deb18657d8cdbb6e754528c43c3d4b9 | cutterd/gelgen_tar_42 | [
"region:us"
] | 2024-01-13T13:07:24+00:00 | {} | 2024-01-13T14:21:53+00:00 |
|
907804a9be164637ecff658f8df1abb349a51a25 | cutterd/gelgen_tar_40 | [
"region:us"
] | 2024-01-13T13:07:24+00:00 | {} | 2024-01-13T14:21:17+00:00 |
|
ab98c3c30c9dd98ecefba1ada30f1113eeadb673 | cutterd/gelgen_tar_41 | [
"region:us"
] | 2024-01-13T13:07:24+00:00 | {} | 2024-01-13T14:20:24+00:00 |
|
61e984cb16bbdf3342ad84b49cb7354487ae9ae3 | cutterd/gelgen_tar_43 | [
"region:us"
] | 2024-01-13T13:07:24+00:00 | {} | 2024-01-13T14:21:00+00:00 |
|
3b41e89bae6ca8b1a4c6d10b1d5eb66e54da938a | cutterd/gelgen_tar_62 | [
"region:us"
] | 2024-01-13T13:21:12+00:00 | {} | 2024-01-13T14:16:16+00:00 |
|
b984a8d2ac4423ba546cfbe161fb3aec850f239b | cutterd/gelgen_tar_60 | [
"region:us"
] | 2024-01-13T13:21:12+00:00 | {} | 2024-01-13T14:13:29+00:00 |
|
81668c5ad65f9a784ddc9ffb9a40966bad3c90a9 | cutterd/gelgen_tar_61 | [
"region:us"
] | 2024-01-13T13:21:12+00:00 | {} | 2024-01-13T14:17:25+00:00 |
|
3f270401e700a1d10b375e98ca35a281f92c9cd8 | cutterd/gelgen_tar_63 | [
"region:us"
] | 2024-01-13T13:21:12+00:00 | {} | 2024-01-13T14:15:53+00:00 |
|
497f165e995f6f9b6ca58fa9352f84d8c088bade | namankhator/web_questions_custom | [
"region:us"
] | 2024-01-13T13:33:32+00:00 | {} | 2024-01-13T16:52:01+00:00 |
|
8b6c8f4d95fb0b14d36b511672f9f39ffbd2784c | v4lkyr13/kevin | [
"region:us"
] | 2024-01-13T13:36:32+00:00 | {} | 2024-01-13T13:36:33+00:00 |
|
6598e47e85546253ebf132918c988d841c5c4f51 | ryo2/roly-poly-dataset | [
"region:us"
] | 2024-01-13T13:43:30+00:00 | {} | 2024-01-13T14:04:10+00:00 |
|
dda75a6cecd48f6d65be7d59a409df5232ad913f | dashtail/vozdodashtest | [
"region:us"
] | 2024-01-13T14:01:39+00:00 | {} | 2024-01-13T14:36:22+00:00 |
|
8b3a90b2bb78f5b0cc5959288589c1e631b2927c | pbk0/scappy | [
"region:us"
] | 2024-01-13T14:24:49+00:00 | {} | 2024-01-13T14:24:49+00:00 |
|
abfea3af41b221167305acaa37d04533473ccaab | Subuday/lj_speech | [
"region:us"
] | 2024-01-13T14:42:00+00:00 | {} | 2024-01-13T14:51:04+00:00 |
|
4cb59fdf19285d26317ff43eac0b09b618629b6d | TheOriginalMarcelo/dataset_nlp_enem_2k23 | [
"region:us"
] | 2024-01-13T14:43:28+00:00 | {} | 2024-01-13T14:47:03+00:00 |
|
d58e234ddcc1ffc9381068a94b47d88589830cf7 | Janez/mini-platypus-two | [
"region:us"
] | 2024-01-13T15:07:55+00:00 | {} | 2024-01-13T15:07:55+00:00 |
|
a960ce3b8d30a459436d62a3842f933b7c5a695a | sunshine-lwt/Osprey-ValData | [
"region:us"
] | 2024-01-13T15:09:43+00:00 | {} | 2024-01-13T15:29:21+00:00 |
|
24d01c36a763e781ee5102aa54a2ca0e57df2c49 | Janez/mini-platypus-p1 | [
"region:us"
] | 2024-01-13T15:10:25+00:00 | {} | 2024-01-13T15:10:26+00:00 |
|
fbcf9e66cdbdbce83b5a223b34b03c10dfa8522b | ahmettasdemir/mini-platypus | [
"region:us"
] | 2024-01-13T15:31:23+00:00 | {} | 2024-01-13T15:31:26+00:00 |
|
fe96bcc6d46050212d79c9e971dcd79d6aaf8752 | joshmittal/mini-platypus | [
"region:us"
] | 2024-01-13T15:37:44+00:00 | {} | 2024-01-13T15:37:44+00:00 |
|
5ceca22880cbdc1b3ec3972b17a6777535c3e15e | firqaaa/T2Rerank-Bahasa | [
"region:us"
] | 2024-01-13T15:55:17+00:00 | {} | 2024-01-13T15:55:17+00:00 |
|
33d5c482d838aea4a816f83963b3ef76b0cb3040 | damerajee/en-kannada-v3 | [
"region:us"
] | 2024-01-13T16:27:23+00:00 | {} | 2024-01-13T16:27:23+00:00 |
|
b0992df96c871f002405549adeb26b0f66f34490 | shokhjakhon/open_llama-2-9k | [
"region:us"
] | 2024-01-13T16:49:26+00:00 | {} | 2024-01-13T16:49:26+00:00 |
|
cc61a2e51f8beeca6ad5e9bb086802df67c08d66 | Yankz/mini-platypus | [
"region:us"
] | 2024-01-13T16:56:33+00:00 | {} | 2024-01-13T16:56:33+00:00 |
|
2c84d52ca4bc18f76d13c124e177a633ef78cf04 | sizhkhy/kolektor_sdd2 | [
"region:us"
] | 2024-01-13T17:16:41+00:00 | {"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 610807578.625, "num_examples": 2331}, {"name": "valid", "num_bytes": 263360243.5, "num_examples": 1004}], "download_size": 874061649, "dataset_size": 874167822.125}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}]} | 2024-01-13T18:22:27+00:00 |
|
bbe613e1fa533647369080785fa2069cfac9e766 |
# Dataset Card for Evaluation run of alnrg2arg/test_wanda_240109
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109) 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 2 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_alnrg2arg__test_wanda_240109",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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": 0.23401038489636644,
"acc_stderr": 0.029968361313724278,
"acc_norm": 0.23351347966222002,
"acc_norm_stderr": 0.0307471687800331,
"mc1": 1.0,
"mc1_stderr": 0.0,
"mc2": NaN,
"mc2_stderr": NaN
},
"harness|arc:challenge|25": {
"acc": 0.22525597269624573,
"acc_stderr": 0.012207839995407305,
"acc_norm": 0.2295221843003413,
"acc_norm_stderr": 0.012288926760890797
},
"harness|hellaswag|10": {
"acc": 0.25542720573590916,
"acc_stderr": 0.004352098082984431,
"acc_norm": 0.2526389165504879,
"acc_norm_stderr": 0.004336375492801798
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.03715062154998904,
"acc_norm": 0.29605263157894735,
"acc_norm_stderr": 0.03715062154998904
},
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"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768077
},
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"acc_stderr": 0.02528839450289137,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.02528839450289137
},
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"acc": 0.2569444444444444,
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},
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},
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},
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"harness|hendrycksTest-college_medicine|5": {
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"harness|truthfulqa:mc|0": {
"mc1": 1.0,
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"mc2": NaN,
"mc2_stderr": NaN
},
"harness|winogrande|5": {
"acc": 0.4988161010260458,
"acc_stderr": 0.014052446290529019
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## 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_alnrg2arg__test_wanda_240109 | [
"region:us"
] | 2024-01-13T17:17:03+00:00 | {"pretty_name": "Evaluation run of alnrg2arg/test_wanda_240109", "dataset_summary": "Dataset automatically created during the evaluation run of model [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109) 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 2 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_alnrg2arg__test_wanda_240109\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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\": 0.23401038489636644,\n \"acc_stderr\": 0.029968361313724278,\n \"acc_norm\": 0.23351347966222002,\n \"acc_norm_stderr\": 0.0307471687800331,\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.22525597269624573,\n \"acc_stderr\": 0.012207839995407305,\n \"acc_norm\": 0.2295221843003413,\n \"acc_norm_stderr\": 0.012288926760890797\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25542720573590916,\n \"acc_stderr\": 0.004352098082984431,\n \"acc_norm\": 0.2526389165504879,\n \"acc_norm_stderr\": 0.004336375492801798\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.29605263157894735,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.29605263157894735,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2698412698412698,\n \"acc_stderr\": 0.03970158273235173,\n \"acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.03970158273235173\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.15763546798029557,\n \"acc_stderr\": 0.025639014131172404,\n \"acc_norm\": 0.15763546798029557,\n \"acc_norm_stderr\": 0.025639014131172404\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.037752516806863715,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.037752516806863715\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176892,\n \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176892\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467765,\n \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467765\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.22349936143039592,\n \"acc_stderr\": 0.014897235229450707,\n \"acc_norm\": 0.22349936143039592,\n \"acc_norm_stderr\": 0.014897235229450707\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.26878612716763006,\n \"acc_stderr\": 0.023868003262500114,\n \"acc_norm\": 0.26878612716763006,\n \"acc_norm_stderr\": 0.023868003262500114\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2508038585209003,\n \"acc_stderr\": 0.024619771956697165,\n \"acc_norm\": 0.2508038585209003,\n \"acc_norm_stderr\": 0.024619771956697165\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4988161010260458,\n \"acc_stderr\": 0.014052446290529019\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/alnrg2arg/test_wanda_240109", "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_01_13T17_14_43.764095", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-14-43.764095.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-14-43.764095.parquet", 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["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["**/details_harness|winogrande|5_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["**/details_harness|winogrande|5_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T17-19-19.094893.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T17_14_43.764095", "path": ["results_2024-01-13T17-14-43.764095.parquet"]}, {"split": "2024_01_13T17_19_19.094893", "path": ["results_2024-01-13T17-19-19.094893.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T17-19-19.094893.parquet"]}]}]} | 2024-01-13T17:21:58+00:00 |
2a5109f47f3ee42f69c51145df5d823de592857a |
# Dataset of trieste/トリエステ/的里雅斯特 (Azur Lane)
This is the dataset of trieste/トリエステ/的里雅斯特 (Azur Lane), containing 92 images and their tags.
The core tags of this character are `breasts, hair_over_one_eye, large_breasts, long_hair, green_eyes, pink_hair, ponytail, hair_ornament, mole, hairclip, bangs, mole_under_eye, earrings, purple_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 92 | 151.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trieste_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 92 | 71.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trieste_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 222 | 157.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trieste_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 92 | 126.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trieste_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 222 | 248.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trieste_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/trieste_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | onsen, water, 1girl, cleavage, collarbone, looking_at_viewer, solo, blush, wet, nude_cover, sitting, steam, naked_towel, outdoors, night, partially_submerged, sky |
| 1 | 34 |  |  |  |  |  | 1girl, military_uniform, sideboob, solo, black_skirt, miniskirt, jewelry, aiguillette, epaulettes, pencil_skirt, looking_at_viewer, black_jacket, white_gloves, black_pantyhose, mole_on_body, cape, black_coat, simple_background, standing, cowboy_shot, hair_between_eyes, white_background, revealing_clothes, sidelocks, armpits, holding, clipboard |
| 2 | 26 |  |  |  |  |  | white_shirt, 1girl, looking_at_viewer, solo, short_sleeves, pleated_skirt, red_neckerchief, official_alternate_costume, miniskirt, crop_top, serafuku, black_pantyhose, blue_sailor_collar, blue_skirt, blush, navel, ribbon, midriff, see-through_silhouette, black_skirt, standing, wristwatch |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | onsen | water | 1girl | cleavage | collarbone | looking_at_viewer | solo | blush | wet | nude_cover | sitting | steam | naked_towel | outdoors | night | partially_submerged | sky | military_uniform | sideboob | black_skirt | miniskirt | jewelry | aiguillette | epaulettes | pencil_skirt | black_jacket | white_gloves | black_pantyhose | mole_on_body | cape | black_coat | simple_background | standing | cowboy_shot | hair_between_eyes | white_background | revealing_clothes | sidelocks | armpits | holding | clipboard | white_shirt | short_sleeves | pleated_skirt | red_neckerchief | official_alternate_costume | crop_top | serafuku | blue_sailor_collar | blue_skirt | navel | ribbon | midriff | see-through_silhouette | wristwatch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------|:-----------|:-------------|:--------------------|:-------|:--------|:------|:-------------|:----------|:--------|:--------------|:-----------|:--------|:----------------------|:------|:-------------------|:-----------|:--------------|:------------|:----------|:--------------|:-------------|:---------------|:---------------|:---------------|:------------------|:---------------|:-------|:-------------|:--------------------|:-----------|:--------------|:--------------------|:-------------------|:--------------------|:------------|:----------|:----------|:------------|:--------------|:----------------|:----------------|:------------------|:-----------------------------|:-----------|:-----------|:---------------------|:-------------|:--------|:---------|:----------|:-------------------------|:-------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 34 |  |  |  |  |  | | | X | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 26 |  |  |  |  |  | | | X | | | X | X | X | | | | | | | | | | | | X | X | | | | | | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/trieste_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T17:17:32+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T17:42:06+00:00 |
ea7e009ae71a99466156e7413336be1d0516857c |
# Dataset of guam/グアム/关岛 (Azur Lane)
This is the dataset of guam/グアム/关岛 (Azur Lane), containing 54 images and their tags.
The core tags of this character are `blonde_hair, breasts, long_hair, large_breasts, bangs, blue_eyes, very_long_hair, twintails, symbol-shaped_pupils, animal_ears, hat, purple_eyes, rabbit_ears, hair_ornament, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 54 | 116.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guam_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 54 | 52.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guam_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 147 | 120.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guam_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 54 | 96.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guam_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 147 | 194.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guam_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/guam_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, cleavage, fake_animal_ears, looking_at_viewer, open_mouth, playboy_bunny, solo, white_gloves, facial_tattoo, fang, simple_background, thighs, top_hat, two_side_up, white_background, :d, blue_leotard, thighhighs, collarbone, detached_collar, heart-shaped_pupils, holding, huge_breasts, ribbon |
| 1 | 5 |  |  |  |  |  | 1girl, black_gloves, covered_navel, fingerless_gloves, smile, solo, long_sleeves, looking_at_viewer, open_mouth, black_thighhighs, blush, hairclip, highleg_leotard, leotard_under_clothes, one_eye_closed, sidelocks, thighs, \m/, cowboy_shot, skindentation, standing, star_(symbol) |
| 2 | 5 |  |  |  |  |  | 1girl, black_gloves, blush, looking_at_viewer, smile, solo, :q, fingerless_gloves, +_+, hairclip, headgear, heart, simple_background, star_(symbol), upper_body, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | cleavage | fake_animal_ears | looking_at_viewer | open_mouth | playboy_bunny | solo | white_gloves | facial_tattoo | fang | simple_background | thighs | top_hat | two_side_up | white_background | :d | blue_leotard | thighhighs | collarbone | detached_collar | heart-shaped_pupils | holding | huge_breasts | ribbon | black_gloves | covered_navel | fingerless_gloves | smile | long_sleeves | black_thighhighs | hairclip | highleg_leotard | leotard_under_clothes | one_eye_closed | sidelocks | \m/ | cowboy_shot | skindentation | standing | star_(symbol) | :q | +_+ | headgear | heart | upper_body |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:-----------|:-------------------|:--------------------|:-------------|:----------------|:-------|:---------------|:----------------|:-------|:--------------------|:---------|:----------|:--------------|:-------------------|:-----|:---------------|:-------------|:-------------|:------------------|:----------------------|:----------|:---------------|:---------|:---------------|:----------------|:--------------------|:--------|:---------------|:-------------------|:-----------|:------------------|:------------------------|:-----------------|:------------|:------|:--------------|:----------------|:-----------|:----------------|:-----|:------|:-----------|:--------|:-------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | | | X | X | | X | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | |
| 2 | 5 |  |  |  |  |  | X | | X | | | X | | | X | | | | X | | | | X | | | | | | | | | | X | | X | X | | | X | | | | | | | | | X | X | X | X | X | X |
| CyberHarem/guam_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T17:17:41+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T17:32:41+00:00 |
f7b0623743defc554be1d26536694ed8ed6a6fda | jailson23232/renanplay | [
"license:openrail",
"region:us"
] | 2024-01-13T17:18:45+00:00 | {"license": "openrail"} | 2024-01-13T17:20:23+00:00 |
|
4333d8fda7ce5c54cccfbbba597353a66b646554 |
# Dataset Card for Evaluation run of flemmingmiguel/NeuDist-Ro-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B) 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_flemmingmiguel__NeuDist-Ro-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T17:17:37.802131](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__NeuDist-Ro-7B/blob/main/results_2024-01-13T17-17-37.802131.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": {
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"acc_stderr": 0.03204722059494361,
"acc_norm": 0.6561722533035016,
"acc_norm_stderr": 0.032711908051799604,
"mc1": 0.49571603427172584,
"mc1_stderr": 0.01750285857737128,
"mc2": 0.6493023269912708,
"mc2_stderr": 0.015276465453752726
},
"harness|arc:challenge|25": {
"acc": 0.6877133105802048,
"acc_stderr": 0.013542598541688065,
"acc_norm": 0.712457337883959,
"acc_norm_stderr": 0.013226719056266127
},
"harness|hellaswag|10": {
"acc": 0.6977693686516631,
"acc_stderr": 0.004582861219020889,
"acc_norm": 0.8748257319259112,
"acc_norm_stderr": 0.0033024011069263197
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
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"acc_norm": 0.34,
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm_stderr": 0.04094376269996792
},
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},
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},
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"acc_norm_stderr": 0.027724236492700914
},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.709628506444276,
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}
}
```
## 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_flemmingmiguel__NeuDist-Ro-7B | [
"region:us"
] | 2024-01-13T17:19:56+00:00 | {"pretty_name": "Evaluation run of flemmingmiguel/NeuDist-Ro-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B) 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_flemmingmiguel__NeuDist-Ro-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:17:37.802131](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__NeuDist-Ro-7B/blob/main/results_2024-01-13T17-17-37.802131.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\": 0.6564397980772864,\n \"acc_stderr\": 0.03204722059494361,\n \"acc_norm\": 0.6561722533035016,\n \"acc_norm_stderr\": 0.032711908051799604,\n \"mc1\": 0.49571603427172584,\n \"mc1_stderr\": 0.01750285857737128,\n \"mc2\": 0.6493023269912708,\n \"mc2_stderr\": 0.015276465453752726\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266127\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6977693686516631,\n \"acc_stderr\": 0.004582861219020889,\n \"acc_norm\": 0.8748257319259112,\n \"acc_norm_stderr\": 0.0033024011069263197\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700476,\n \"acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700476\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752599,\n \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752599\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992002,\n \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992002\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42681564245810055,\n \"acc_stderr\": 0.01654240195463191,\n \"acc_norm\": 0.42681564245810055,\n \"acc_norm_stderr\": 0.01654240195463191\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.023788583551658533,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.023788583551658533\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.02982074719142244,\n \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.02982074719142244\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898445,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898445\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.49571603427172584,\n \"mc1_stderr\": 0.01750285857737128,\n \"mc2\": 0.6493023269912708,\n \"mc2_stderr\": 0.015276465453752726\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047987\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.709628506444276,\n \"acc_stderr\": 0.01250359248181895\n }\n}\n```", "repo_url": "https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B", "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_01_13T17_17_37.802131", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-17-37.802131.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-17-37.802131.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["**/details_harness|winogrande|5_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T17-17-37.802131.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T17_17_37.802131", "path": ["results_2024-01-13T17-17-37.802131.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T17-17-37.802131.parquet"]}]}]} | 2024-01-13T17:20:18+00:00 |
8d002b6c3f58f5fb73c0e6dde1c8f9242dfb590f | version-control/arrayblow-1.0-train | [
"region:us"
] | 2024-01-13T17:20:32+00:00 | {"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "hexsha", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "api_extract", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7306756, "num_examples": 437}], "download_size": 2407574, "dataset_size": 7306756}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-13T17:20:37+00:00 |
|
b8dec85c46dee5f9217240e5eb51a2797abdd39b | Cshavi/embeddings | [
"region:us"
] | 2024-01-13T17:21:04+00:00 | {} | 2024-01-13T17:21:39+00:00 |
|
f26e6a73877bb3292ba339d7ef3ee169e0eab72e |
# Dataset Card for Evaluation run of kodonho/SolarM-SakuraSolar-SLERP
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kodonho/SolarM-SakuraSolar-SLERP](https://huggingface.co/kodonho/SolarM-SakuraSolar-SLERP) 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_kodonho__SolarM-SakuraSolar-SLERP",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T17:26:45.129484](https://huggingface.co/datasets/open-llm-leaderboard/details_kodonho__SolarM-SakuraSolar-SLERP/blob/main/results_2024-01-13T17-26-45.129484.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": 0.6661841092869378,
"acc_stderr": 0.031618934361982654,
"acc_norm": 0.6670035390095326,
"acc_norm_stderr": 0.03226162867427471,
"mc1": 0.5667074663402693,
"mc1_stderr": 0.01734702445010748,
"mc2": 0.7209867000245426,
"mc2_stderr": 0.014980299607085815
},
"harness|arc:challenge|25": {
"acc": 0.6877133105802048,
"acc_stderr": 0.013542598541688067,
"acc_norm": 0.71160409556314,
"acc_norm_stderr": 0.013238394422428173
},
"harness|hellaswag|10": {
"acc": 0.714797849034057,
"acc_stderr": 0.0045058790846068415,
"acc_norm": 0.8846843258315077,
"acc_norm_stderr": 0.003187497509087418
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.756578947368421,
"acc_stderr": 0.034923496688842384,
"acc_norm": 0.756578947368421,
"acc_norm_stderr": 0.034923496688842384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956913,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956913
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082636,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6212765957446809,
"acc_stderr": 0.03170995606040655,
"acc_norm": 0.6212765957446809,
"acc_norm_stderr": 0.03170995606040655
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6275862068965518,
"acc_stderr": 0.04028731532947558,
"acc_norm": 0.6275862068965518,
"acc_norm_stderr": 0.04028731532947558
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4973544973544973,
"acc_stderr": 0.02575094967813039,
"acc_norm": 0.4973544973544973,
"acc_norm_stderr": 0.02575094967813039
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8129032258064516,
"acc_stderr": 0.022185710092252252,
"acc_norm": 0.8129032258064516,
"acc_norm_stderr": 0.022185710092252252
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.806060606060606,
"acc_stderr": 0.03087414513656209,
"acc_norm": 0.806060606060606,
"acc_norm_stderr": 0.03087414513656209
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
"acc_stderr": 0.024063156416822516,
"acc_norm": 0.8686868686868687,
"acc_norm_stderr": 0.024063156416822516
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.021995311963644244,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.021995311963644244
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.36666666666666664,
"acc_stderr": 0.029381620726465073,
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.6467020470053071,
"acc_stderr": 0.013166337192115683
}
}
```
## Dataset Details
### Dataset Description
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## Dataset Structure
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#### Data Collection and Processing
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[More Information Needed] | open-llm-leaderboard/details_kodonho__SolarM-SakuraSolar-SLERP | [
"region:us"
] | 2024-01-13T17:29:01+00:00 | {"pretty_name": "Evaluation run of kodonho/SolarM-SakuraSolar-SLERP", "dataset_summary": "Dataset automatically created during the evaluation run of model [kodonho/SolarM-SakuraSolar-SLERP](https://huggingface.co/kodonho/SolarM-SakuraSolar-SLERP) 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_kodonho__SolarM-SakuraSolar-SLERP\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:26:45.129484](https://huggingface.co/datasets/open-llm-leaderboard/details_kodonho__SolarM-SakuraSolar-SLERP/blob/main/results_2024-01-13T17-26-45.129484.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\": 0.6661841092869378,\n \"acc_stderr\": 0.031618934361982654,\n \"acc_norm\": 0.6670035390095326,\n \"acc_norm_stderr\": 0.03226162867427471,\n \"mc1\": 0.5667074663402693,\n \"mc1_stderr\": 0.01734702445010748,\n \"mc2\": 0.7209867000245426,\n \"mc2_stderr\": 0.014980299607085815\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688067,\n \"acc_norm\": 0.71160409556314,\n \"acc_norm_stderr\": 0.013238394422428173\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.714797849034057,\n \"acc_stderr\": 0.0045058790846068415,\n \"acc_norm\": 0.8846843258315077,\n \"acc_norm_stderr\": 0.003187497509087418\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6212765957446809,\n \"acc_stderr\": 0.03170995606040655,\n \"acc_norm\": 0.6212765957446809,\n \"acc_norm_stderr\": 0.03170995606040655\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4973544973544973,\n \"acc_stderr\": 0.02575094967813039,\n \"acc_norm\": 0.4973544973544973,\n \"acc_norm_stderr\": 0.02575094967813039\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8129032258064516,\n \"acc_stderr\": 0.022185710092252252,\n \"acc_norm\": 0.8129032258064516,\n \"acc_norm_stderr\": 0.022185710092252252\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644244,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644244\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465073,\n \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465073\n },\n 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["**/details_harness|winogrande|5_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T17-26-45.129484.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T17_26_45.129484", "path": ["results_2024-01-13T17-26-45.129484.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T17-26-45.129484.parquet"]}]}]} | 2024-01-13T17:29:22+00:00 |
7d0c0d94e45731858af0830be29ef6fd91826abd |
# Dataset Card for Evaluation run of kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP](https://huggingface.co/kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP) 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_kodonho__Solar-OrcaDPO-Solar-Instruct-SLERP",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T17:32:35.779900](https://huggingface.co/datasets/open-llm-leaderboard/details_kodonho__Solar-OrcaDPO-Solar-Instruct-SLERP/blob/main/results_2024-01-13T17-32-35.779900.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": 0.6659359197324706,
"acc_stderr": 0.03167249441105516,
"acc_norm": 0.6667077779566729,
"acc_norm_stderr": 0.032318448519432046,
"mc1": 0.5679314565483476,
"mc1_stderr": 0.017341202394988327,
"mc2": 0.7195437123974021,
"mc2_stderr": 0.01500878766115849
},
"harness|arc:challenge|25": {
"acc": 0.6808873720136519,
"acc_stderr": 0.013621696119173306,
"acc_norm": 0.7098976109215017,
"acc_norm_stderr": 0.013261573677520766
},
"harness|hellaswag|10": {
"acc": 0.7105158334993029,
"acc_stderr": 0.004525960965551707,
"acc_norm": 0.882194781915953,
"acc_norm_stderr": 0.003217184906847944
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082636,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6340425531914894,
"acc_stderr": 0.031489558297455304,
"acc_norm": 0.6340425531914894,
"acc_norm_stderr": 0.031489558297455304
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6137931034482759,
"acc_stderr": 0.04057324734419036,
"acc_norm": 0.6137931034482759,
"acc_norm_stderr": 0.04057324734419036
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.47354497354497355,
"acc_stderr": 0.025715239811346758,
"acc_norm": 0.47354497354497355,
"acc_norm_stderr": 0.025715239811346758
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
"acc_stderr": 0.022331707611823078,
"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823078
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.806060606060606,
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"acc_norm": 0.806060606060606,
"acc_norm_stderr": 0.03087414513656209
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8737373737373737,
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
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"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.029185714949857406,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.029185714949857406
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7226890756302521,
"acc_stderr": 0.02907937453948001,
"acc_norm": 0.7226890756302521,
"acc_norm_stderr": 0.02907937453948001
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242741,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242741
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5601851851851852,
"acc_stderr": 0.0338517797604481,
"acc_norm": 0.5601851851851852,
"acc_norm_stderr": 0.0338517797604481
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8480392156862745,
"acc_stderr": 0.025195658428931796,
"acc_norm": 0.8480392156862745,
"acc_norm_stderr": 0.025195658428931796
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8649789029535865,
"acc_stderr": 0.022245776632003694,
"acc_norm": 0.8649789029535865,
"acc_norm_stderr": 0.022245776632003694
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
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"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.03114679648297246
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
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"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
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"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7423312883435583,
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"acc_norm": 0.7423312883435583,
"acc_norm_stderr": 0.03436150827846917
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.03675668832233188,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.0230866350868414,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.0230866350868414
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8084291187739464,
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.39664804469273746,
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"acc_norm": 0.39664804469273746,
"acc_norm_stderr": 0.016361354769822475
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7581699346405228,
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"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7266881028938906,
"acc_stderr": 0.025311765975426122,
"acc_norm": 0.7266881028938906,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7808641975308642,
"acc_norm_stderr": 0.023016705640262192
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5141843971631206,
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},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.49608865710560623,
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},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7426470588235294,
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
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"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.02812342933514278
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.835820895522388,
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
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"acc_norm": 0.9,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
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"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598052
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7894736842105263,
"acc_stderr": 0.031267817146631786,
"acc_norm": 0.7894736842105263,
"acc_norm_stderr": 0.031267817146631786
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5679314565483476,
"mc1_stderr": 0.017341202394988327,
"mc2": 0.7195437123974021,
"mc2_stderr": 0.01500878766115849
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.010450899545370634
},
"harness|gsm8k|5": {
"acc": 0.6527672479150872,
"acc_stderr": 0.013113898382146877
}
}
```
## 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. -->
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[More Information Needed] | open-llm-leaderboard/details_kodonho__Solar-OrcaDPO-Solar-Instruct-SLERP | [
"region:us"
] | 2024-01-13T17:34:57+00:00 | {"pretty_name": "Evaluation run of kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP", "dataset_summary": "Dataset automatically created during the evaluation run of model [kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP](https://huggingface.co/kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP) 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_kodonho__Solar-OrcaDPO-Solar-Instruct-SLERP\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:32:35.779900](https://huggingface.co/datasets/open-llm-leaderboard/details_kodonho__Solar-OrcaDPO-Solar-Instruct-SLERP/blob/main/results_2024-01-13T17-32-35.779900.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\": 0.6659359197324706,\n \"acc_stderr\": 0.03167249441105516,\n \"acc_norm\": 0.6667077779566729,\n \"acc_norm_stderr\": 0.032318448519432046,\n \"mc1\": 0.5679314565483476,\n \"mc1_stderr\": 0.017341202394988327,\n \"mc2\": 0.7195437123974021,\n \"mc2_stderr\": 0.01500878766115849\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.013621696119173306,\n \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520766\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7105158334993029,\n \"acc_stderr\": 0.004525960965551707,\n \"acc_norm\": 0.882194781915953,\n \"acc_norm_stderr\": 0.003217184906847944\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6340425531914894,\n \"acc_stderr\": 0.031489558297455304,\n \"acc_norm\": 0.6340425531914894,\n \"acc_norm_stderr\": 0.031489558297455304\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.47354497354497355,\n \"acc_stderr\": 0.025715239811346758,\n \"acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.025715239811346758\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8737373737373737,\n \"acc_stderr\": 0.02366435940288023,\n \"acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.02366435940288023\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857406,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857406\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7226890756302521,\n \"acc_stderr\": 0.02907937453948001,\n \"acc_norm\": 0.7226890756302521,\n \"acc_norm_stderr\": 0.02907937453948001\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\": 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931796,\n \"acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931796\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7630057803468208,\n \"acc_stderr\": 0.02289408248992599,\n \"acc_norm\": 0.7630057803468208,\n \"acc_norm_stderr\": 0.02289408248992599\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39664804469273746,\n \"acc_stderr\": 0.016361354769822475,\n \"acc_norm\": 0.39664804469273746,\n \"acc_norm_stderr\": 0.016361354769822475\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262192,\n \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262192\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5141843971631206,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.5141843971631206,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.49608865710560623,\n \"acc_stderr\": 0.012769845366441194,\n \"acc_norm\": 0.49608865710560623,\n \"acc_norm_stderr\": 0.012769845366441194\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.0265565194700415,\n \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.0265565194700415\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.01866335967146366,\n \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.01866335967146366\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454125\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\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.031267817146631786,\n \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.031267817146631786\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5679314565483476,\n \"mc1_stderr\": 0.017341202394988327,\n \"mc2\": 0.7195437123974021,\n \"mc2_stderr\": 0.01500878766115849\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370634\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6527672479150872,\n \"acc_stderr\": 0.013113898382146877\n }\n}\n```", "repo_url": "https://huggingface.co/kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP", "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_01_13T17_32_35.779900", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-32-35.779900.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-32-35.779900.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T17_32_35.779900", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-32-35.779900.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-32-35.779900.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T17_32_35.779900", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-32-35.779900.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-32-35.779900.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T17_32_35.779900", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-32-35.779900.parquet", 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b2a292050a3edab5957ecf96ac6a4abd44617711 |
# Dataset: PhilEO Downstream Tasks
A novel 400GB Sentinel-2 dataset of the PhilEO Bench containing labels for the three downstream tasks of building density estimation, road segmentation, and land cover classification.
## Dataset Details
### Dataset Description
The PhilEO dataset is a 400GB global dataset of Sentinel-2 images and has labels for roads, buildings, and land cover, where these are the three downstream tasks. The data is sampled from geographically diverse regions around the globe including: Denmark, East Africa, Egypt, Guinea, Europe, Ghana, Israel, Japan, Nigeria, North America, Senegal, South America, Tanzania, and Uganda. Each region has up to 200 tiles of varying sizes. Some locations have been revisited up to 3 times.
The data contain 11 bands at 10m resolution in the following order: 0-SCL, 1-B02, 2-B03, 3-B04, 4-B08, 5-B05, 6-B06, 7-B07, 8-B8A, 9-B11, and 10-B12 where SCL is the Scene Classification Layer.
- **Curated by:** ESA Phi-lab
- **License:** MIT
## Uses
The dataset can be used to evaluate any EO Foundation Model.
### Dataset Sources
The basic links for the dataset:
- **Repository:** http://huggingface.co/datasets/ESA-philab/PhilEO-downstream
- **Paper:** http://arxiv.org/pdf/2401.04464.pdf
- **Project Website:** http://phileo-bench.github.io
- **Code GitHub:** http://github.com/ESA-PhiLab/PhilEO-Bench
- **Dataset also in:** http://www.eotdl.com/datasets/PhilEO-downstream
- **arXiv:** http://arxiv.org/abs/2401.04464
## Citation
Casper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, and Bertrand Le Saux, “PhilEO Bench: Evaluating Geo-Spatial Foundation Models,” arXiv:2401.04464, 2024.
| ESA-philab/PhilEO-downstream | [
"license:mit",
"arxiv:2401.04464",
"region:us"
] | 2024-01-13T17:39:24+00:00 | {"license": "mit"} | 2024-02-03T13:41:09+00:00 |
9f5072bb6b336b441251ce471b4b5fac3f4e2768 | zukoochan/tripleSNXT | [
"region:us"
] | 2024-01-13T17:41:37+00:00 | {} | 2024-01-13T17:47:48+00:00 |
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