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6aea94a16c95bbc72683433e43cec7cf8fd62bc5 | trevorweston/arii | [
"region:us"
] | 2024-02-15T22:01:52+00:00 | {} | 2024-02-15T22:05:04+00:00 |
|
5c9a1fc2c132bb638807c63e46f9fba76730c000 | # Args
```python
{'base_model': 'mistralai/Mistral-7B-v0.1',
'check_length_correctness': True,
'debug': False,
'hf_entity': 'vwxyzjn',
'params': TaskQueryHParams(length=3000,
format_str='SUBREDDIT: r/{subreddit}\n'
'\n'
'TITLE: {title}\n'
'\n'
'POST: {post}\n'
'\n'
'TL;DR:',
truncate_field='post',
truncate_text='\n',
padding='pad_token',
pad_token=[32000],
pad_side='left',
max_query_length=3000,
max_sft_query_response_length=4000,
max_sft_response_length=1500,
max_rm_query_response_length=4500,
max_rm_response_length=1500),
'push_to_hub': True}
```
| vwxyzjn/ultrachat_200k_filtered_1708034814 | [
"region:us"
] | 2024-02-15T22:09:51+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query_token", "sequence": "int64"}, {"name": "query_reference_response", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query_reference_response_token", "sequence": "int64"}, {"name": "query_reference_response_token_len", "dtype": "int64"}, {"name": "query_token_len", "dtype": "int64"}, {"name": "reference_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "reference_response_token", "sequence": "int64"}, {"name": "reference_response_token_len", "dtype": "int64"}], "splits": [{"name": "test_sft", "num_bytes": 1982888370.9168758, "num_examples": 22991}, {"name": "train_sft", "num_bytes": 17846869528.524822, "num_examples": 206698}], "download_size": 3301659997, "dataset_size": 19829757899.441696}} | 2024-02-15T22:12:40+00:00 |
d92a2db76b7636e59af247ff534c22b4a360a6e0 | marcones/marcones | [
"license:openrail",
"region:us"
] | 2024-02-15T22:10:20+00:00 | {"license": "openrail"} | 2024-02-15T22:10:42+00:00 |
|
2ca50a5c84be3c0aedd53b1c56d1ce5fbe46d88b | yleo/aqua-binarized | [
"region:us"
] | 2024-02-15T22:16:14+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2345096, "num_examples": 1000}], "download_size": 1405535, "dataset_size": 2345096}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T22:49:40+00:00 |
|
58cd51c9c8434992e10c6126e70d769824f18d70 | ekolasky/MainResultIdForMistral7B | [
"region:us"
] | 2024-02-15T22:20:03+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "outputs", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 906830, "num_examples": 333}], "download_size": 504756, "dataset_size": 906830}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T22:21:23+00:00 |
|
02478d2b9ea312804fd60a4088ef80a67e0c7283 |
# Dataset consisting of anonymous polish stories
## Warning: Stories were not curated by me, some may use strong language or use sexual references
This dataset consists of all (28k) stories dumped from [anonimowe wyzwania](https://anonimowe.pl/) in January. Stories are submitted by anonymous users. I have included a community rating, which you can use for filtering.
Stories are very diverse — some are sad, some funny. The huge amount might sound fake, but the vast majority is made by young people. This may help your model to not sound strict, corporate, boring, or academic.
Default sorting is based on community rating.
More information about requirements for stories: [link](https://anonimowe.pl/faq)
## Where to find me
- [Github](https://github.com/JonaszPotoniec)
- [Linkedin](https://www.linkedin.com/in/jonasz-potoniec/)
- [E-mail](mailto:[email protected])
- [Telegram](https://t.me/JonaszPotoniec) | JonaszPotoniec/anonimowe-polish-stories | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:pl",
"license:mit",
"not-for-all-audiences",
"region:us"
] | 2024-02-15T22:20:13+00:00 | {"language": ["pl"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Anonimowe wyzwania", "dataset_info": {"features": [{"name": "points", "dtype": "int64"}, {"name": "story", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33017836, "num_examples": 27798}], "download_size": 22463377, "dataset_size": 33017836}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["not-for-all-audiences"]} | 2024-02-15T22:39:55+00:00 |
224c2e806d73c98a15c688dac6ad0efb3714ca68 | marcones/vozmarcones | [
"license:openrail",
"region:us"
] | 2024-02-15T22:22:44+00:00 | {"license": "openrail"} | 2024-02-15T22:22:59+00:00 |
|
3bbd3fa7009ed6534ede5bbb220b8e10b6025985 | # Args
```python
{'base_model': 'mistralai/Mistral-7B-v0.1',
'check_length_correctness': True,
'debug': False,
'hf_entity': 'vwxyzjn',
'params': TaskQueryHParams(length=3000,
format_str='SUBREDDIT: r/{subreddit}\n'
'\n'
'TITLE: {title}\n'
'\n'
'POST: {post}\n'
'\n'
'TL;DR:',
truncate_field='post',
truncate_text='\n',
padding='pad_token',
pad_token=[32000],
pad_side='left',
max_query_length=3000,
max_sft_query_response_length=4000,
max_sft_response_length=1500,
max_rm_query_response_length=4500,
max_rm_response_length=1500),
'push_to_hub': True}
```
| vwxyzjn/ultrachat_200k_filtered_1708035667 | [
"region:us"
] | 2024-02-15T22:23:56+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query_token", "sequence": "int64"}, {"name": "query_reference_response", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query_reference_response_token", "sequence": "int64"}, {"name": "query_reference_response_token_len", "dtype": "int64"}, {"name": "query_token_len", "dtype": "int64"}, {"name": "reference_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "reference_response_token", "sequence": "int64"}, {"name": "reference_response_token_len", "dtype": "int64"}], "splits": [{"name": "test_sft", "num_bytes": 1982888370.9168758, "num_examples": 22991}, {"name": "train_sft", "num_bytes": 17846869528.524822, "num_examples": 206698}], "download_size": 3301659997, "dataset_size": 19829757899.441696}} | 2024-02-15T22:29:05+00:00 |
4ed6db01a0dd01e3f93cf00fea9d74c7d63953d5 | MatsuoDochiai/IAE | [
"license:openrail",
"region:us"
] | 2024-02-15T22:24:55+00:00 | {"license": "openrail"} | 2024-02-15T22:25:14+00:00 |
|
24e68a15ed3e18781397e6ea448fa00a65812ac3 | Generated by ChatGPT | CreitinGameplays/you-are-elisa-chan | [
"region:us"
] | 2024-02-15T22:26:23+00:00 | {} | 2024-02-15T22:27:31+00:00 |
63c71da57f5891125e2c5fd36ff1351747675891 | # Dataset Card for "ultrafeedback_binarized_1708035667"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vwxyzjn/ultrafeedback_binarized_1708035667 | [
"region:us"
] | 2024-02-15T22:28:31+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "score_chosen", "dtype": "float64"}, {"name": "score_rejected", "dtype": "float64"}, {"name": "query", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "query_token", "sequence": "int64"}, {"name": "query_token_len", "dtype": "int64"}, {"name": "query_chosen_token", "sequence": "int64"}, {"name": "query_chosen_token_len", "dtype": "int64"}, {"name": "chosen_token", "sequence": "int64"}, {"name": "chosen_token_len", "dtype": "int64"}, {"name": "query_rejected_token", "sequence": "int64"}, {"name": "query_rejected_token_len", "dtype": "int64"}, {"name": "rejected_token", "sequence": "int64"}, {"name": "rejected_token_len", "dtype": "int64"}], "splits": [{"name": "test_prefs", "num_bytes": 235051943.0, "num_examples": 2000}, {"name": "train_prefs", "num_bytes": 7188255622.3255415, "num_examples": 61112}], "download_size": 477048940, "dataset_size": 7423307565.3255415}} | 2024-02-15T22:29:04+00:00 |
509daa48db465032eeda6721595abb09a638ee2f |
# Dataset Card for Evaluation run of BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit](https://huggingface.co/BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BarraHome__PequeLLaMa-1B-Instruct-v0.1-16bit",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T22:30:35.769938](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__PequeLLaMa-1B-Instruct-v0.1-16bit/blob/main/results_2024-02-15T22-30-35.769938.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": 0.24925356596098744,
"acc_stderr": 0.03049039803240805,
"acc_norm": 0.250967942400928,
"acc_norm_stderr": 0.0312990053733219,
"mc1": 0.22031823745410037,
"mc1_stderr": 0.014509045171487295,
"mc2": 0.41096447978752615,
"mc2_stderr": 0.014916925934314724
},
"harness|arc:challenge|25": {
"acc": 0.24658703071672355,
"acc_stderr": 0.01259572626879012,
"acc_norm": 0.27986348122866894,
"acc_norm_stderr": 0.013119040897725922
},
"harness|hellaswag|10": {
"acc": 0.333698466440948,
"acc_stderr": 0.0047056977452221435,
"acc_norm": 0.4302927703644692,
"acc_norm_stderr": 0.004941051795214789
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816508,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816508
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2,
"acc_stderr": 0.03455473702325437,
"acc_norm": 0.2,
"acc_norm_stderr": 0.03455473702325437
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123384,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21132075471698114,
"acc_stderr": 0.025125766484827845,
"acc_norm": 0.21132075471698114,
"acc_norm_stderr": 0.025125766484827845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
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"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
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"acc_norm": 0.2,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.35,
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"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
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"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
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"acc_norm_stderr": 0.030952890217749884
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
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"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2425531914893617,
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"acc_norm": 0.2425531914893617,
"acc_norm_stderr": 0.028020226271200217
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159394
},
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},
"harness|hendrycksTest-elementary_mathematics|5": {
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},
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"acc_norm_stderr": 0.03893259610604673
},
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"acc": 0.18,
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},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.16129032258064516,
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"acc_norm_stderr": 0.020923327006423305
},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
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},
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},
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},
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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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},
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},
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},
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},
"harness|hendrycksTest-logical_fallacies|5": {
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},
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},
"harness|hendrycksTest-management|5": {
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},
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},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2515964240102171,
"acc_stderr": 0.015517322365529614,
"acc_norm": 0.2515964240102171,
"acc_norm_stderr": 0.015517322365529614
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2514450867052023,
"acc_stderr": 0.02335736578587404,
"acc_norm": 0.2514450867052023,
"acc_norm_stderr": 0.02335736578587404
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23575418994413408,
"acc_stderr": 0.014196375686290804,
"acc_norm": 0.23575418994413408,
"acc_norm_stderr": 0.014196375686290804
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.023929155517351284,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.023929155517351284
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.21221864951768488,
"acc_stderr": 0.02322275679743511,
"acc_norm": 0.21221864951768488,
"acc_norm_stderr": 0.02322275679743511
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.023468429832451163,
"acc_norm": 0.23148148148148148,
"acc_norm_stderr": 0.023468429832451163
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.22695035460992907,
"acc_stderr": 0.024987106365642973,
"acc_norm": 0.22695035460992907,
"acc_norm_stderr": 0.024987106365642973
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"harness|hendrycksTest-professional_law|5": {
"acc": 0.23533246414602346,
"acc_stderr": 0.010834432543912226,
"acc_norm": 0.23533246414602346,
"acc_norm_stderr": 0.010834432543912226
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.3382352941176471,
"acc_stderr": 0.028739328513983572,
"acc_norm": 0.3382352941176471,
"acc_norm_stderr": 0.028739328513983572
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.23636363636363636,
"acc_stderr": 0.04069306319721376,
"acc_norm": 0.23636363636363636,
"acc_norm_stderr": 0.04069306319721376
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.20816326530612245,
"acc_stderr": 0.025991117672813292,
"acc_norm": 0.20816326530612245,
"acc_norm_stderr": 0.025991117672813292
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.2537313432835821,
"acc_stderr": 0.030769444967296014,
"acc_norm": 0.2537313432835821,
"acc_norm_stderr": 0.030769444967296014
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-virology|5": {
"acc": 0.25903614457831325,
"acc_stderr": 0.03410646614071857,
"acc_norm": 0.25903614457831325,
"acc_norm_stderr": 0.03410646614071857
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3216374269005848,
"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.22031823745410037,
"mc1_stderr": 0.014509045171487295,
"mc2": 0.41096447978752615,
"mc2_stderr": 0.014916925934314724
},
"harness|winogrande|5": {
"acc": 0.5272296764009471,
"acc_stderr": 0.014031631629827701
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_BarraHome__PequeLLaMa-1B-Instruct-v0.1-16bit | [
"region:us"
] | 2024-02-15T22:32:54+00:00 | {"pretty_name": "Evaluation run of BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit", "dataset_summary": "Dataset automatically created during the evaluation run of model [BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit](https://huggingface.co/BarraHome/PequeLLaMa-1B-Instruct-v0.1-16bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BarraHome__PequeLLaMa-1B-Instruct-v0.1-16bit\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T22:30:35.769938](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__PequeLLaMa-1B-Instruct-v0.1-16bit/blob/main/results_2024-02-15T22-30-35.769938.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\": 0.24925356596098744,\n \"acc_stderr\": 0.03049039803240805,\n \"acc_norm\": 0.250967942400928,\n \"acc_norm_stderr\": 0.0312990053733219,\n \"mc1\": 0.22031823745410037,\n \"mc1_stderr\": 0.014509045171487295,\n \"mc2\": 0.41096447978752615,\n \"mc2_stderr\": 0.014916925934314724\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.24658703071672355,\n \"acc_stderr\": 0.01259572626879012,\n \"acc_norm\": 0.27986348122866894,\n \"acc_norm_stderr\": 0.013119040897725922\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.333698466440948,\n \"acc_stderr\": 0.0047056977452221435,\n \"acc_norm\": 0.4302927703644692,\n \"acc_norm_stderr\": 0.004941051795214789\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.03455473702325437,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.03455473702325437\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123384,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123384\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827845,\n \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827845\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\": 0.04020151261036846,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749884,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749884\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\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2425531914893617,\n \"acc_stderr\": 0.028020226271200217,\n \"acc_norm\": 0.2425531914893617,\n \"acc_norm_stderr\": 0.028020226271200217\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.1724137931034483,\n \"acc_stderr\": 0.03147830790259575,\n \"acc_norm\": 0.1724137931034483,\n \"acc_norm_stderr\": 0.03147830790259575\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.02113285918275444,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.02113285918275444\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n \"acc_stderr\": 0.03893259610604673,\n \"acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.03893259610604673\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\": 0.16129032258064516,\n \"acc_stderr\": 0.020923327006423305,\n \"acc_norm\": 0.16129032258064516,\n \"acc_norm_stderr\": 0.020923327006423305\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.1477832512315271,\n \"acc_stderr\": 0.02496962133352127,\n \"acc_norm\": 0.1477832512315271,\n \"acc_norm_stderr\": 0.02496962133352127\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\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\": 0.24352331606217617,\n \"acc_stderr\": 0.03097543638684542,\n \"acc_norm\": 0.24352331606217617,\n \"acc_norm_stderr\": 0.03097543638684542\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.021840866990423088,\n \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.021840866990423088\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945277,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945277\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.18907563025210083,\n \"acc_stderr\": 0.025435119438105353,\n \"acc_norm\": 0.18907563025210083,\n \"acc_norm_stderr\": 0.025435119438105353\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2185430463576159,\n \"acc_stderr\": 0.033742355504256936,\n \"acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.033742355504256936\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.20550458715596331,\n \"acc_stderr\": 0.017324352325016005,\n \"acc_norm\": 0.20550458715596331,\n \"acc_norm_stderr\": 0.017324352325016005\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.2696078431372549,\n \"acc_stderr\": 0.03114557065948678,\n \"acc_norm\": 0.2696078431372549,\n \"acc_norm_stderr\": 0.03114557065948678\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.28270042194092826,\n \"acc_stderr\": 0.029312814153955927,\n \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.029312814153955927\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.29596412556053814,\n \"acc_stderr\": 0.030636591348699796,\n \"acc_norm\": 0.29596412556053814,\n \"acc_norm_stderr\": 0.030636591348699796\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.38016528925619836,\n \"acc_stderr\": 0.04431324501968432,\n \"acc_norm\": 0.38016528925619836,\n \"acc_norm_stderr\": 0.04431324501968432\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.03559039531617342,\n \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.03559039531617342\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n \"acc_stderr\": 0.04246624336697624,\n \"acc_norm\": 0.2767857142857143,\n \"acc_norm_stderr\": 0.04246624336697624\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.1650485436893204,\n \"acc_stderr\": 0.036756688322331886,\n \"acc_norm\": 0.1650485436893204,\n \"acc_norm_stderr\": 0.036756688322331886\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2606837606837607,\n \"acc_stderr\": 0.028760348956523414,\n \"acc_norm\": 0.2606837606837607,\n \"acc_norm_stderr\": 0.028760348956523414\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2515964240102171,\n \"acc_stderr\": 0.015517322365529614,\n \"acc_norm\": 0.2515964240102171,\n \"acc_norm_stderr\": 0.015517322365529614\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2514450867052023,\n \"acc_stderr\": 0.02335736578587404,\n \"acc_norm\": 0.2514450867052023,\n \"acc_norm_stderr\": 0.02335736578587404\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23575418994413408,\n \"acc_stderr\": 0.014196375686290804,\n \"acc_norm\": 0.23575418994413408,\n \"acc_norm_stderr\": 0.014196375686290804\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\": 0.21221864951768488,\n \"acc_stderr\": 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b3ee1a654517bd19bfc0b5bb4628619b4317dc98 | mryannugent/synthetic-responses | [
"region:us"
] | 2024-02-15T22:36:45+00:00 | {} | 2024-02-15T23:32:20+00:00 |
|
6a511f446d966a6003871f0781f56b511303dd16 |
# Dataset Card for Evaluation run of sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted](https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted) 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_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T22:34:32.769102](https://huggingface.co/datasets/open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted/blob/main/results_2024-02-15T22-34-32.769102.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": 0.6363878047023346,
"acc_stderr": 0.032296390142505176,
"acc_norm": 0.6396525594831768,
"acc_norm_stderr": 0.03293649951708016,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5164963215954579,
"mc2_stderr": 0.015210020803636122
},
"harness|arc:challenge|25": {
"acc": 0.60580204778157,
"acc_stderr": 0.014280522667467325,
"acc_norm": 0.64419795221843,
"acc_norm_stderr": 0.01399057113791876
},
"harness|hellaswag|10": {
"acc": 0.6483768173670583,
"acc_stderr": 0.004765012078929387,
"acc_norm": 0.8394742083250348,
"acc_norm_stderr": 0.003663427536178161
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.0286372356398009,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.0286372356398009
},
"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": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.03703851193099521,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.03703851193099521
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"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": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"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": 0.5241379310344828,
"acc_stderr": 0.0416180850350153,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.0416180850350153
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.02548718714785938,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.02548718714785938
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4523809523809524,
"acc_stderr": 0.044518079590553275,
"acc_norm": 0.4523809523809524,
"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7935483870967742,
"acc_stderr": 0.02302589961718871,
"acc_norm": 0.7935483870967742,
"acc_norm_stderr": 0.02302589961718871
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.031584153240477114,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.031584153240477114
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.028606204289229865,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.028606204289229865
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.022935144053919443,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.022935144053919443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6102564102564103,
"acc_stderr": 0.024726967886647074,
"acc_norm": 0.6102564102564103,
"acc_norm_stderr": 0.024726967886647074
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.0279404571362284,
"acc_norm": 0.3,
"acc_norm_stderr": 0.0279404571362284
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8348623853211009,
"acc_stderr": 0.01591955782997604,
"acc_norm": 0.8348623853211009,
"acc_norm_stderr": 0.01591955782997604
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.02812597226565437,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.02812597226565437
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023349,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023349
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477518,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477518
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.035477710041594654,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.035477710041594654
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.04742762361243011,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.04742762361243011
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597528,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597528
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8250319284802043,
"acc_stderr": 0.013586619219903338,
"acc_norm": 0.8250319284802043,
"acc_norm_stderr": 0.013586619219903338
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7167630057803468,
"acc_stderr": 0.02425790170532338,
"acc_norm": 0.7167630057803468,
"acc_norm_stderr": 0.02425790170532338
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.30837988826815643,
"acc_stderr": 0.01544571691099888,
"acc_norm": 0.30837988826815643,
"acc_norm_stderr": 0.01544571691099888
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6816720257234726,
"acc_stderr": 0.026457225067811025,
"acc_norm": 0.6816720257234726,
"acc_norm_stderr": 0.026457225067811025
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7530864197530864,
"acc_stderr": 0.02399350170904211,
"acc_norm": 0.7530864197530864,
"acc_norm_stderr": 0.02399350170904211
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5035460992907801,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.5035460992907801,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46870925684485004,
"acc_stderr": 0.012745204626083131,
"acc_norm": 0.46870925684485004,
"acc_norm_stderr": 0.012745204626083131
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6727941176470589,
"acc_stderr": 0.02850145286039655,
"acc_norm": 0.6727941176470589,
"acc_norm_stderr": 0.02850145286039655
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.01897542792050721,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.01897542792050721
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8159203980099502,
"acc_stderr": 0.027403859410786845,
"acc_norm": 0.8159203980099502,
"acc_norm_stderr": 0.027403859410786845
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5164963215954579,
"mc2_stderr": 0.015210020803636122
},
"harness|winogrande|5": {
"acc": 0.7774269928966061,
"acc_stderr": 0.011690933809712667
},
"harness|gsm8k|5": {
"acc": 0.5322213798332069,
"acc_stderr": 0.013743857303073797
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Who are the annotators?
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#### 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. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted | [
"region:us"
] | 2024-02-15T22:36:52+00:00 | {"pretty_name": "Evaluation run of sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted", "dataset_summary": "Dataset automatically created during the evaluation run of model [sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted](https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted) 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_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T22:34:32.769102](https://huggingface.co/datasets/open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted/blob/main/results_2024-02-15T22-34-32.769102.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\": 0.6363878047023346,\n \"acc_stderr\": 0.032296390142505176,\n \"acc_norm\": 0.6396525594831768,\n \"acc_norm_stderr\": 0.03293649951708016,\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5164963215954579,\n \"mc2_stderr\": 0.015210020803636122\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.60580204778157,\n \"acc_stderr\": 0.014280522667467325,\n \"acc_norm\": 0.64419795221843,\n \"acc_norm_stderr\": 0.01399057113791876\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6483768173670583,\n \"acc_stderr\": 0.004765012078929387,\n \"acc_norm\": 0.8394742083250348,\n \"acc_norm_stderr\": 0.003663427536178161\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\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\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\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.0286372356398009,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.0286372356398009\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\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\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\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\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\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\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\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n \"acc_stderr\": 0.02302589961718871,\n \"acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.02302589961718871\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.031584153240477114,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.031584153240477114\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.0279404571362284,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.0279404571362284\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8348623853211009,\n \"acc_stderr\": 0.01591955782997604,\n \"acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.01591955782997604\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565437,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565437\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477518,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477518\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.04742762361243011,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.04742762361243011\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\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597528\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n \"acc_stderr\": 0.013586619219903338,\n \"acc_norm\": 0.8250319284802043,\n \"acc_norm_stderr\": 0.013586619219903338\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.30837988826815643,\n \"acc_stderr\": 0.01544571691099888,\n \"acc_norm\": 0.30837988826815643,\n \"acc_norm_stderr\": 0.01544571691099888\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46870925684485004,\n \"acc_stderr\": 0.012745204626083131,\n \"acc_norm\": 0.46870925684485004,\n \"acc_norm_stderr\": 0.012745204626083131\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.02850145286039655,\n \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.02850145286039655\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.01897542792050721,\n \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.01897542792050721\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\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\": 0.8159203980099502,\n \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5164963215954579,\n \"mc2_stderr\": 0.015210020803636122\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7774269928966061,\n \"acc_stderr\": 0.011690933809712667\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5322213798332069,\n \"acc_stderr\": 0.013743857303073797\n }\n}\n```", "repo_url": "https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|arc:challenge|25_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|gsm8k|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hellaswag|10_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T22-34-32.769102.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T22-34-32.769102.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-15T22-34-32.769102.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_15T22_34_32.769102", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-15T22-34-32.769102.parquet"]}, {"split": "latest", "path": 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58c6886f62bf6f4139598db460db5bb4693048ff |
# Dataset Card for Evaluation run of sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2](https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2) 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_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T22:39:42.033476](https://huggingface.co/datasets/open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2/blob/main/results_2024-02-15T22-39-42.033476.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": 0.6371686329291266,
"acc_stderr": 0.03229837388268866,
"acc_norm": 0.6400332159863307,
"acc_norm_stderr": 0.03293928846555229,
"mc1": 0.35862913096695226,
"mc1_stderr": 0.016789289499502022,
"mc2": 0.5239619444151867,
"mc2_stderr": 0.015249337191329784
},
"harness|arc:challenge|25": {
"acc": 0.6126279863481229,
"acc_stderr": 0.014235872487909869,
"acc_norm": 0.6493174061433447,
"acc_norm_stderr": 0.013944635930726099
},
"harness|hellaswag|10": {
"acc": 0.6542521410077674,
"acc_stderr": 0.0047463946133845325,
"acc_norm": 0.8454491137223661,
"acc_norm_stderr": 0.0036073726062950894
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.028637235639800893,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.028637235639800893
},
"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": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6242774566473989,
"acc_stderr": 0.036928207672648664,
"acc_norm": 0.6242774566473989,
"acc_norm_stderr": 0.036928207672648664
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.548936170212766,
"acc_stderr": 0.032529096196131965,
"acc_norm": 0.548936170212766,
"acc_norm_stderr": 0.032529096196131965
},
"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": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.02546714904546955,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.02546714904546955
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.023287665127268552,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268552
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5221674876847291,
"acc_stderr": 0.03514528562175007,
"acc_norm": 0.5221674876847291,
"acc_norm_stderr": 0.03514528562175007
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.032250781083062896,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.032250781083062896
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.02833560973246336,
"acc_norm": 0.803030303030303,
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},
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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-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": 0.55420773313116,
"acc_stderr": 0.01369130517450669
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2 | [
"region:us"
] | 2024-02-15T22:41:58+00:00 | {"pretty_name": "Evaluation run of sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2](https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2) 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_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T22:39:42.033476](https://huggingface.co/datasets/open-llm-leaderboard/details_sonthenguyen__OpenHermes-2.5-Mistral-7B-mt-bench-DPO-original-v2/blob/main/results_2024-02-15T22-39-42.033476.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\": 0.6371686329291266,\n \"acc_stderr\": 0.03229837388268866,\n \"acc_norm\": 0.6400332159863307,\n \"acc_norm_stderr\": 0.03293928846555229,\n \"mc1\": 0.35862913096695226,\n \"mc1_stderr\": 0.016789289499502022,\n \"mc2\": 0.5239619444151867,\n \"mc2_stderr\": 0.015249337191329784\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6126279863481229,\n \"acc_stderr\": 0.014235872487909869,\n \"acc_norm\": 0.6493174061433447,\n \"acc_norm_stderr\": 0.013944635930726099\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6542521410077674,\n \"acc_stderr\": 0.0047463946133845325,\n \"acc_norm\": 0.8454491137223661,\n \"acc_norm_stderr\": 0.0036073726062950894\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\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\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\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\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\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268552,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268552\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.032250781083062896,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.032250781083062896\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6153846153846154,\n \"acc_stderr\": 0.024666744915187208,\n \"acc_norm\": 0.6153846153846154,\n \"acc_norm_stderr\": 0.024666744915187208\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.027940457136228405,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.027940457136228405\n },\n 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["**/details_harness|hendrycksTest-public_relations|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["**/details_harness|winogrande|5_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-15T22-39-42.033476.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_15T22_39_42.033476", "path": ["results_2024-02-15T22-39-42.033476.parquet"]}, {"split": "latest", "path": ["results_2024-02-15T22-39-42.033476.parquet"]}]}]} | 2024-02-15T22:42:22+00:00 |
18ca6e2c9c4f066c5e68020611e7e2f018f811ae | iNeil77/SLTrans | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"license:cc-by-nc-sa-4.0",
"code",
"region:us"
] | 2024-02-15T22:48:03+00:00 | {"license": "cc-by-nc-sa-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "tags": ["code"], "extra_gated_prompt": "You agree to not use the model to conduct experiments that cause harm to human subjects or generate malicious code.", "extra_gated_fields": {"Company": "text", "Country": "country", "Specific date": "date_picker", "I want to use this model for": {"type": "select", "options": ["Research", "Education", {"label": "Other", "value": "other"}]}, "I agree to use this model for non-commercial use ONLY": "checkbox"}, "dataset_info": [{"config_name": "C", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 3383884149, "num_examples": 341419}, {"name": "Size_Optimized", "num_bytes": 2528286566, "num_examples": 341785}], "download_size": 1323447636, "dataset_size": 5912170715}, {"config_name": "C++", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 116351369851, "num_examples": 2898509}, {"name": "Size_Optimized", "num_bytes": 92572469724, "num_examples": 2916655}], "download_size": 51690627847, "dataset_size": 208923839575}, {"config_name": "D", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 2320830137, "num_examples": 7000}, {"name": "Size_Optimized", "num_bytes": 3271276765, "num_examples": 11054}], "download_size": 1316382832, "dataset_size": 5592106902}, {"config_name": "Fortran", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 357741835, "num_examples": 6327}, {"name": "Size_Optimized", "num_bytes": 2320830137, "num_examples": 7000}], "download_size": 563853972, "dataset_size": 2678571972}, {"config_name": "Go", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 819560767, "num_examples": 3913}, {"name": "Size_Optimized", "num_bytes": 741733997, "num_examples": 3925}], "download_size": 317182680, "dataset_size": 1561294764}, {"config_name": "Haskell", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 3838556743, "num_examples": 27892}, {"name": "Size_Optimized", "num_bytes": 3667186152, "num_examples": 28203}], "download_size": 1736729352, "dataset_size": 7505742895}, {"config_name": "Nim", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Size_Optimized", "num_bytes": 106424381, "num_examples": 215}], "download_size": 22506456, "dataset_size": 106424381}, {"config_name": "Objective-C", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 1729045, "num_examples": 283}, {"name": "Size_Optimized", "num_bytes": 1433377, "num_examples": 283}], "download_size": 707508, "dataset_size": 3162422}, {"config_name": "Python", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 13118428652, "num_examples": 154507}, {"name": "Size_Optimized", "num_bytes": 13118428652, "num_examples": 154507}], "download_size": 6511950536, "dataset_size": 26236857304}, {"config_name": "Rust", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 5859467468, "num_examples": 38323}, {"name": "Size_Optimized", "num_bytes": 8695405064, "num_examples": 32720}], "download_size": 5326634011, "dataset_size": 14554872532}, {"config_name": "Swift", "features": [{"name": "Source_Code", "dtype": "string"}, {"name": "IR_Original", "dtype": "string"}], "splits": [{"name": "Perf_Optimized", "num_bytes": 260013963, "num_examples": 2003}, {"name": "Size_Optimized", "num_bytes": 266356839, "num_examples": 2015}], "download_size": 144113584, "dataset_size": 526370802}], "configs": [{"config_name": "C", "data_files": [{"split": "Perf_Optimized", "path": "C/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "C/Size_Optimized-*"}]}, {"config_name": "C++", "data_files": [{"split": "Perf_Optimized", "path": "C++/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "C++/Size_Optimized-*"}]}, {"config_name": "D", "data_files": [{"split": "Perf_Optimized", "path": "D/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "D/Size_Optimized-*"}]}, {"config_name": "Fortran", "data_files": [{"split": "Perf_Optimized", "path": "Fortran/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Fortran/Size_Optimized-*"}]}, {"config_name": "Go", "data_files": [{"split": "Perf_Optimized", "path": "Go/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Go/Size_Optimized-*"}]}, {"config_name": "Haskell", "data_files": [{"split": "Perf_Optimized", "path": "Haskell/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Haskell/Size_Optimized-*"}]}, {"config_name": "Nim", "data_files": [{"split": "Size_Optimized", "path": "Nim/Size_Optimized-*"}]}, {"config_name": "Objective-C", "data_files": [{"split": "Perf_Optimized", "path": "Objective-C/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Objective-C/Size_Optimized-*"}]}, {"config_name": "Python", "data_files": [{"split": "Perf_Optimized", "path": "Python/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Python/Size_Optimized-*"}]}, {"config_name": "Rust", "data_files": [{"split": "Perf_Optimized", "path": "Rust/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Rust/Size_Optimized-*"}]}, {"config_name": "Swift", "data_files": [{"split": "Perf_Optimized", "path": "Swift/Perf_Optimized-*"}, {"split": "Size_Optimized", "path": "Swift/Size_Optimized-*"}]}]} | 2024-02-16T01:02:34+00:00 |
|
51831028aba9e7579e7314ddeed814fea75af7b2 | eagle0504/larkin-web-scrape-dataset-qa-formatted | [
"region:us"
] | 2024-02-15T23:02:44+00:00 | {"dataset_info": {"features": [{"name": "questions", "dtype": "string"}, {"name": "answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 115322, "num_examples": 568}], "download_size": 62490, "dataset_size": 115322}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T23:02:45+00:00 |
|
e998248084186177e8803376dbcf3e8967925bc3 |
# Dataset Card for Evaluation run of Kquant03/Buttercup-V2-bf16
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kquant03/Buttercup-V2-bf16](https://huggingface.co/Kquant03/Buttercup-V2-bf16) 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_Kquant03__Buttercup-V2-bf16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-15T23:01:38.445097](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__Buttercup-V2-bf16/blob/main/results_2024-02-15T23-01-38.445097.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": 0.6530538161665846,
"acc_stderr": 0.031991249389942286,
"acc_norm": 0.6524163789680969,
"acc_norm_stderr": 0.03266539743840073,
"mc1": 0.554467564259486,
"mc1_stderr": 0.01739933528014034,
"mc2": 0.6947306262348207,
"mc2_stderr": 0.015031157853542046
},
"harness|arc:challenge|25": {
"acc": 0.7039249146757679,
"acc_stderr": 0.013340916085246258,
"acc_norm": 0.7372013651877133,
"acc_norm_stderr": 0.012862523175351335
},
"harness|hellaswag|10": {
"acc": 0.7112129057956582,
"acc_stderr": 0.004522725412556955,
"acc_norm": 0.885381398127863,
"acc_norm_stderr": 0.003179100565887989
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"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": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"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": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"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": 0.5724137931034483,
"acc_stderr": 0.04122737111370333,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4470899470899471,
"acc_stderr": 0.025606723995777025,
"acc_norm": 0.4470899470899471,
"acc_norm_stderr": 0.025606723995777025
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
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"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.02328766512726854
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
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"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8181818181818182,
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"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.027479603010538797
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
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"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
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"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.029116617606083008,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.029116617606083008
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
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"acc_norm": 0.6890756302521008,
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},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
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"acc_norm": 0.36423841059602646,
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},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8422018348623853,
"acc_stderr": 0.015630022970092448,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.015630022970092448
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
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"acc_norm": 0.5,
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},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
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"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.02595502084162113,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|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": 0.686125852918878,
"acc_stderr": 0.012782681251053198
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### 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. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Kquant03__Buttercup-V2-bf16 | [
"region:us"
] | 2024-02-15T23:03:54+00:00 | {"pretty_name": "Evaluation run of Kquant03/Buttercup-V2-bf16", "dataset_summary": "Dataset automatically created during the evaluation run of model [Kquant03/Buttercup-V2-bf16](https://huggingface.co/Kquant03/Buttercup-V2-bf16) 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_Kquant03__Buttercup-V2-bf16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-15T23:01:38.445097](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__Buttercup-V2-bf16/blob/main/results_2024-02-15T23-01-38.445097.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\": 0.6530538161665846,\n \"acc_stderr\": 0.031991249389942286,\n \"acc_norm\": 0.6524163789680969,\n \"acc_norm_stderr\": 0.03266539743840073,\n \"mc1\": 0.554467564259486,\n \"mc1_stderr\": 0.01739933528014034,\n \"mc2\": 0.6947306262348207,\n \"mc2_stderr\": 0.015031157853542046\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7039249146757679,\n \"acc_stderr\": 0.013340916085246258,\n \"acc_norm\": 0.7372013651877133,\n \"acc_norm_stderr\": 0.012862523175351335\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7112129057956582,\n \"acc_stderr\": 0.004522725412556955,\n \"acc_norm\": 0.885381398127863,\n \"acc_norm_stderr\": 0.003179100565887989\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\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\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\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\": 0.45,\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\": 0.54,\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\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\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\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\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\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4470899470899471,\n \"acc_stderr\": 0.025606723995777025,\n \"acc_norm\": 0.4470899470899471,\n \"acc_norm_stderr\": 0.025606723995777025\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538797,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538797\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\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\": 0.35185185185185186,\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\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297794,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297794\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092448,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092448\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\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\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T23-01-38.445097.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_15T23_01_38.445097", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-15T23-01-38.445097.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_15T23_01_38.445097", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-15T23-01-38.445097.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_15T23_01_38.445097", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-15T23-01-38.445097.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_15T23_01_38.445097", "path": ["**/details_harness|winogrande|5_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-15T23-01-38.445097.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_15T23_01_38.445097", "path": ["results_2024-02-15T23-01-38.445097.parquet"]}, {"split": "latest", "path": ["results_2024-02-15T23-01-38.445097.parquet"]}]}]} | 2024-02-15T23:04:17+00:00 |
4be847cc89900bf9db1eba729a1ef42a1d9cca54 | PAD6/drwithsquadformat | [
"region:us"
] | 2024-02-15T23:12:14+00:00 | {} | 2024-02-15T23:15:49+00:00 |
|
967dd83abf6b67b4c5066e94dddbd7fe5ab0bfb6 | Based on AEZAKMI V3, I removed some general airoboros things that made the model predictable and boring and changed up system prompts for wsb_001 prompts a bit. | adamo1139/AEZAKMI_v3-1 | [
"license:other",
"region:us"
] | 2024-02-15T23:21:26+00:00 | {"license": "other", "license_name": "other", "license_link": "LICENSE"} | 2024-02-15T23:22:52+00:00 |
e683653ca7e2f3fa34251f89bec89df1ec1224c0 | Mayareis/vozz | [
"license:openrail",
"region:us"
] | 2024-02-15T23:26:03+00:00 | {"license": "openrail"} | 2024-02-15T23:28:55+00:00 |
|
1e4dbe6985990654c62d8a29f959b7089989662c | chavinlo/hdvila100test | [
"region:us"
] | 2024-02-15T23:58:40+00:00 | {"dataset_info": {"features": [{"name": "clip_id", "dtype": "string"}, {"name": "video_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "span_start", "dtype": "string"}, {"name": "span_end", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16430, "num_examples": 100}], "download_size": 8118, "dataset_size": 16430}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-15T23:58:59+00:00 |
|
03a170667a7ec80246da008798e2203ab07a0867 | chavinlo/hdvila5ktest | [
"region:us"
] | 2024-02-16T00:00:26+00:00 | {"dataset_info": {"features": [{"name": "clip_id", "dtype": "string"}, {"name": "video_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "span_start", "dtype": "string"}, {"name": "span_end", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 823279, "num_examples": 5000}], "download_size": 200764, "dataset_size": 823279}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T00:00:36+00:00 |
|
7c92851a5d413d723a5611b7c94f6b78ceedd6d3 | # MathPile ArXiv (subset)
## Description
This dataset consists of 343,830 TeX files containing mathematics papers sourced from the arXiv. Training and testing sets are already split
## Source
The data was obtained from the training + validation portion of the arXiv subset of MathPile.
## Format
- LLaMa BOS and EOS tokens (`<s>` and `</s>`) have been added to mark the beginning and end of each sequence.
- The dataset is organized into blocks of 64,000 documents each, stored in JSONL format.
## Usage
- LaTeX stuff idk
## License
The original data is subject to the licensing terms of the arXiv. Users should refer to the arXiv's terms of use for details on permissible usage. | aluncstokes/math_arxiv_temp | [
"region:us"
] | 2024-02-16T00:10:27+00:00 | {} | 2024-02-17T02:59:11+00:00 |
2ae7aaafb4dd8278f21f6481930e66ceecd60197 | furry-br/civilliam | [
"license:openrail",
"region:us"
] | 2024-02-16T00:11:18+00:00 | {"license": "openrail"} | 2024-02-16T00:11:58+00:00 |
|
764f1db623dacf9b4faeadbb043539255c868bb4 | cnmoro/Text_Structuring_SOLAR_10.7B_Distilled_Smaller | [
"region:us"
] | 2024-02-16T00:40:20+00:00 | {} | 2024-02-16T00:40:41+00:00 |
|
62a13de410999c20f60ec2f3be93457dcaac0d62 | KomeijiForce/PropL | [
"region:us"
] | 2024-02-16T00:41:51+00:00 | {} | 2024-02-16T00:47:05+00:00 |
|
07c0f3cc63403b9bf768d236c2f995ff2d67e61a | tyranus/trisvoice | [
"license:openrail",
"region:us"
] | 2024-02-16T00:43:39+00:00 | {"license": "openrail"} | 2024-02-16T01:00:07+00:00 |
|
aa4156d9096f814de79f427873c84750132ffd08 | cogent-demon/l0lpusher69 | [
"region:us"
] | 2024-02-16T00:46:33+00:00 | {} | 2024-02-16T00:48:18+00:00 |
|
e9945407504d876959f0932b3e2387072b6ab099 |
# Dataset Card for Evaluation run of NovoCode/NeuralPaca-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [NovoCode/NeuralPaca-7b](https://huggingface.co/NovoCode/NeuralPaca-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_NovoCode__NeuralPaca-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T00:47:24.688523](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__NeuralPaca-7b/blob/main/results_2024-02-16T00-47-24.688523.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": 0.6323051718212915,
"acc_stderr": 0.032436835484599615,
"acc_norm": 0.6335791604628077,
"acc_norm_stderr": 0.033101979094730234,
"mc1": 0.35006119951040393,
"mc1_stderr": 0.01669794942015103,
"mc2": 0.4831753085957374,
"mc2_stderr": 0.015324947436319568
},
"harness|arc:challenge|25": {
"acc": 0.5921501706484642,
"acc_stderr": 0.014361097288449707,
"acc_norm": 0.6279863481228669,
"acc_norm_stderr": 0.014124597881844461
},
"harness|hellaswag|10": {
"acc": 0.632742481577375,
"acc_stderr": 0.004810723108378215,
"acc_norm": 0.8301135232025493,
"acc_norm_stderr": 0.0037476555337545153
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621503,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621503
},
"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": 0.6644736842105263,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6127167630057804,
"acc_stderr": 0.03714325906302065,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.03714325906302065
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.04858083574266345,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"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": 0.5234042553191489,
"acc_stderr": 0.03265019475033582,
"acc_norm": 0.5234042553191489,
"acc_norm_stderr": 0.03265019475033582
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.04657047260594964,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.04657047260594964
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778405,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778405
},
"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": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7612903225806451,
"acc_stderr": 0.024251071262208837,
"acc_norm": 0.7612903225806451,
"acc_norm_stderr": 0.024251071262208837
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.49261083743842365,
"acc_stderr": 0.035176035403610084,
"acc_norm": 0.49261083743842365,
"acc_norm_stderr": 0.035176035403610084
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267045,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267045
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768787,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768787
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6435897435897436,
"acc_stderr": 0.02428314052946731,
"acc_norm": 0.6435897435897436,
"acc_norm_stderr": 0.02428314052946731
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.02918571494985741,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.02918571494985741
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.03038835355188679,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.03038835355188679
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
"acc_stderr": 0.01577623925616323,
"acc_norm": 0.8385321100917431,
"acc_norm_stderr": 0.01577623925616323
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5046296296296297,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.5046296296296297,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.027599174300640766,
"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.027599174300640766
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
"acc_stderr": 0.027123298205229966,
"acc_norm": 0.7763713080168776,
"acc_norm_stderr": 0.027123298205229966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6995515695067265,
"acc_stderr": 0.030769352008229146,
"acc_norm": 0.6995515695067265,
"acc_norm_stderr": 0.030769352008229146
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7314814814814815,
"acc_stderr": 0.042844679680521934,
"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.042844679680521934
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.03408997886857529,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.03408997886857529
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.03760178006026621,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.03760178006026621
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179326,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179326
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.67,
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"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8173690932311622,
"acc_stderr": 0.013816335389973133,
"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973133
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
"acc_stderr": 0.02418242749657761,
"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.02418242749657761
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.32625698324022345,
"acc_stderr": 0.01568044151888918,
"acc_norm": 0.32625698324022345,
"acc_norm_stderr": 0.01568044151888918
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.026568921015457162,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.026568921015457162
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.025755865922632945,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7283950617283951,
"acc_stderr": 0.02474862449053737,
"acc_norm": 0.7283950617283951,
"acc_norm_stderr": 0.02474862449053737
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4574468085106383,
"acc_stderr": 0.02971928127223685,
"acc_norm": 0.4574468085106383,
"acc_norm_stderr": 0.02971928127223685
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.439374185136897,
"acc_stderr": 0.012676014778580214,
"acc_norm": 0.439374185136897,
"acc_norm_stderr": 0.012676014778580214
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6397058823529411,
"acc_stderr": 0.029163128570670733,
"acc_norm": 0.6397058823529411,
"acc_norm_stderr": 0.029163128570670733
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.01943177567703731,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.01943177567703731
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.044612721759105085,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.044612721759105085
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6816326530612244,
"acc_stderr": 0.029822533793982066,
"acc_norm": 0.6816326530612244,
"acc_norm_stderr": 0.029822533793982066
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8258706467661692,
"acc_stderr": 0.026814951200421603,
"acc_norm": 0.8258706467661692,
"acc_norm_stderr": 0.026814951200421603
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.02753912288906145,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.02753912288906145
},
"harness|truthfulqa:mc|0": {
"mc1": 0.35006119951040393,
"mc1_stderr": 0.01669794942015103,
"mc2": 0.4831753085957374,
"mc2_stderr": 0.015324947436319568
},
"harness|winogrande|5": {
"acc": 0.8168902920284136,
"acc_stderr": 0.01086977863316836
},
"harness|gsm8k|5": {
"acc": 0.6057619408642911,
"acc_stderr": 0.013460852357095666
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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[More Information Needed] | open-llm-leaderboard/details_NovoCode__NeuralPaca-7b | [
"region:us"
] | 2024-02-16T00:49:45+00:00 | {"pretty_name": "Evaluation run of NovoCode/NeuralPaca-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [NovoCode/NeuralPaca-7b](https://huggingface.co/NovoCode/NeuralPaca-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_NovoCode__NeuralPaca-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T00:47:24.688523](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__NeuralPaca-7b/blob/main/results_2024-02-16T00-47-24.688523.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\": 0.6323051718212915,\n \"acc_stderr\": 0.032436835484599615,\n \"acc_norm\": 0.6335791604628077,\n \"acc_norm_stderr\": 0.033101979094730234,\n \"mc1\": 0.35006119951040393,\n \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.4831753085957374,\n \"mc2_stderr\": 0.015324947436319568\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.014361097288449707,\n \"acc_norm\": 0.6279863481228669,\n \"acc_norm_stderr\": 0.014124597881844461\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.632742481577375,\n \"acc_stderr\": 0.004810723108378215,\n \"acc_norm\": 0.8301135232025493,\n \"acc_norm_stderr\": 0.0037476555337545153\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\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\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\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\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\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\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768787,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768787\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.02428314052946731,\n \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.02428314052946731\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8385321100917431,\n \"acc_stderr\": 0.01577623925616323,\n \"acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.01577623925616323\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.030769352008229146,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.030769352008229146\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\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\": 0.7314814814814815,\n \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\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\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.020930193185179326\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n \"acc_stderr\": 0.013816335389973133,\n \"acc_norm\": 0.8173690932311622,\n \"acc_norm_stderr\": 0.013816335389973133\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32625698324022345,\n \"acc_stderr\": 0.01568044151888918,\n \"acc_norm\": 0.32625698324022345,\n \"acc_norm_stderr\": 0.01568044151888918\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.026568921015457162,\n \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.026568921015457162\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\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4574468085106383,\n \"acc_stderr\": 0.02971928127223685,\n \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.02971928127223685\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.439374185136897,\n \"acc_stderr\": 0.012676014778580214,\n \"acc_norm\": 0.439374185136897,\n \"acc_norm_stderr\": 0.012676014778580214\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 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693d353fc9bd9e57ca97a05a8175a7abfdc752d0 |
# Dataset Card for Evaluation run of jeiku/NarrativeNexus_7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jeiku/NarrativeNexus_7B](https://huggingface.co/jeiku/NarrativeNexus_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_jeiku__NarrativeNexus_7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T01:30:29.349287](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__NarrativeNexus_7B/blob/main/results_2024-02-16T01-30-29.349287.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": 0.6331502373053775,
"acc_stderr": 0.032649477056743835,
"acc_norm": 0.6360612367088411,
"acc_norm_stderr": 0.03330403787596569,
"mc1": 0.46878824969400246,
"mc1_stderr": 0.017469364874577537,
"mc2": 0.6394506791157332,
"mc2_stderr": 0.015272071804569947
},
"harness|arc:challenge|25": {
"acc": 0.6279863481228669,
"acc_stderr": 0.01412459788184446,
"acc_norm": 0.6612627986348123,
"acc_norm_stderr": 0.01383056892797433
},
"harness|hellaswag|10": {
"acc": 0.6773551085441147,
"acc_stderr": 0.004665327309399188,
"acc_norm": 0.8573989245170285,
"acc_norm_stderr": 0.003489509493001621
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
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}
```
## Dataset Details
### Dataset Description
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[More Information Needed] | open-llm-leaderboard/details_jeiku__NarrativeNexus_7B | [
"region:us"
] | 2024-02-16T01:32:47+00:00 | {"pretty_name": "Evaluation run of jeiku/NarrativeNexus_7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [jeiku/NarrativeNexus_7B](https://huggingface.co/jeiku/NarrativeNexus_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_jeiku__NarrativeNexus_7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T01:30:29.349287](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__NarrativeNexus_7B/blob/main/results_2024-02-16T01-30-29.349287.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\": 0.6331502373053775,\n \"acc_stderr\": 0.032649477056743835,\n \"acc_norm\": 0.6360612367088411,\n \"acc_norm_stderr\": 0.03330403787596569,\n \"mc1\": 0.46878824969400246,\n \"mc1_stderr\": 0.017469364874577537,\n \"mc2\": 0.6394506791157332,\n \"mc2_stderr\": 0.015272071804569947\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.01412459788184446,\n \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.01383056892797433\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6773551085441147,\n \"acc_stderr\": 0.004665327309399188,\n \"acc_norm\": 0.8573989245170285,\n \"acc_norm_stderr\": 0.003489509493001621\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["**/details_harness|winogrande|5_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T01-30-29.349287.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T01_30_29.349287", "path": ["results_2024-02-16T01-30-29.349287.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T01-30-29.349287.parquet"]}]}]} | 2024-02-16T01:33:12+00:00 |
ecc5ad5859783aac2628568b9a6debbcb43975c6 | Raspberry-ai/ref_image_retrieval_v2_isabel_madewell_veronica | [
"region:us"
] | 2024-02-16T01:40:56+00:00 | {"dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "image_uri", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9532899, "num_examples": 3897}], "download_size": 11042284, "dataset_size": 9532899}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T01:40:58+00:00 |
|
3d10fbafbe7dc4c2ece5ec8b03ab0920704b2dd8 | franlucc/starcoderbase-3b-completions_typeinf_analysis | [
"region:us"
] | 2024-02-16T01:58:26+00:00 | {"dataset_info": {"features": [{"name": "generated", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "hexsha", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "correctness", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 27150344, "num_examples": 5313}], "download_size": 5176569, "dataset_size": 27150344}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T01:58:27+00:00 |
|
ecb19e3f97e5c464bbcf19a78b6af55964afa7b8 | mesolitica/noisy-rephrase | [
"region:us"
] | 2024-02-16T02:00:45+00:00 | {} | 2024-02-16T03:19:23+00:00 |
|
5ccd2361ba28f3f07c37f184d445bb3c7c0e577c | lab42/grasp-conditioned-hindrance-v3.4 | [
"region:us"
] | 2024-02-16T02:13:50+00:00 | {"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 37813735.0, "num_examples": 41}, {"name": "validation", "num_bytes": 17486270.0, "num_examples": 19}], "download_size": 55219472, "dataset_size": 55300005.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2024-02-16T02:14:02+00:00 |
|
88326967444e823e142e4e010139c82b1b76cfc2 | dhilgaertner/cod-profile-info-dataset | [
"size_categories:n<1K",
"language:en",
"region:us"
] | 2024-02-16T02:18:22+00:00 | {"language": ["en"], "size_categories": ["n<1K"]} | 2024-02-16T02:25:53+00:00 |
|
90451914c3fca8621fb8ba377beb88da3e7b4adc | # Dataset Card for "Test_Dataset_1K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ouvic215/Test_Dataset_1K | [
"region:us"
] | 2024-02-16T02:23:19+00:00 | {"dataset_info": {"features": [{"name": "mask_image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 147332332.0, "num_examples": 1588}], "download_size": 146499523, "dataset_size": 147332332.0}} | 2024-02-16T02:23:49+00:00 |
96f536570316ef052012ff5182e10316e3ac591e | yan2069/ProfessorGarlick | [
"license:openrail",
"region:us"
] | 2024-02-16T02:24:00+00:00 | {"license": "openrail"} | 2024-02-16T02:30:18+00:00 |
|
effb9731cf538da7e6398371760a81d6b7fb5ff8 | benayas/banking_augmented_5pct_v0 | [
"region:us"
] | 2024-02-16T02:30:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1040273, "num_examples": 10003}], "download_size": 407790, "dataset_size": 1040273}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T02:30:48+00:00 |
|
fef084ca60b690b044c1654b1089080ebdf71967 | Jacklab/pixiv_v1 | [
"license:gpl-3.0",
"region:us"
] | 2024-02-16T02:42:20+00:00 | {"license": "gpl-3.0"} | 2024-02-16T03:29:45+00:00 |
|
f9578abd8ffa972d85921b1072148184bd98625f | wangxingjun778/test_dogs_and_cats | [
"license:apache-2.0",
"region:us"
] | 2024-02-16T02:42:34+00:00 | {"license": "apache-2.0"} | 2024-02-16T03:17:27+00:00 |
|
7d29f635111f692fc91b40b425059c2ba9c9da41 | manishiitg/berkeley-nest-Nectar | [
"region:us"
] | 2024-02-16T02:44:51+00:00 | {"dataset_info": {"features": [{"name": "org_dataset", "dtype": "string"}, {"name": "uniq_id", "dtype": "string"}, {"name": "en_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "hi_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 788010420, "num_examples": 180461}], "download_size": 303565053, "dataset_size": 788010420}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T04:30:31+00:00 |
|
cc779b07c502b1ab061f5ea2ad562db7234be298 | manishiitg/teknium-GPTeacher-General-Instruct | [
"region:us"
] | 2024-02-16T02:51:10+00:00 | {"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 209818583, "num_examples": 178520}], "download_size": 99430551, "dataset_size": 209818583}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T04:16:54+00:00 |
|
6d48ce683e3a633e78fdda21183064824ac7f3fe | manishiitg/ai2_arc | [
"region:us"
] | 2024-02-16T02:51:24+00:00 | {"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2318104, "num_examples": 4502}], "download_size": 674650, "dataset_size": 2318104}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T04:17:12+00:00 |
|
2619ffe280f4816149a86ff488984e347d1d7e16 | jlbaker361/hacs-segment-pairs | [
"region:us"
] | 2024-02-16T02:53:53+00:00 | {"dataset_info": {"features": [{"name": "src_image", "dtype": "image"}, {"name": "src_pose", "dtype": "image"}, {"name": "target_image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 991404.0, "num_examples": 4}], "download_size": 1000950, "dataset_size": 991404.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T04:25:38+00:00 |
|
b177233b46a174f19003ed1cbe1caf36541e9068 | manishiitg/databricks-databricks-dolly-15k | [
"region:us"
] | 2024-02-16T02:55:11+00:00 | {"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44799212, "num_examples": 29980}], "download_size": 20957197, "dataset_size": 44799212}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T04:16:45+00:00 |
|
90485c7ec0ee3d8278ab1dab86a3fcefc9616e3d |
This is an LLM rated version of **euclaise/reddit-instruct-curated**, which is already a good dataset imo.
Only **post titles** and **comment texts** were rated as post texts can be confusing due to edits and seemingly out of context information.
First, **I filtered examples with <250 comment score**. Of course this is not a very efficient filtering as some pairs might have references to other comments or simply be unhelpful, yet upvoted due to Reddit hivemind.
Next I sent the example pairs with a rating prompt to Senku-Q2-XS and collected the numeric votes **(out of 10)**.
Overall there aren't many low rated examples. Here are three "worst" examples:
There are only 66 examples with <6 rate.
An example of highly upvoted but poorly rated pair:
**Let me know if I fucked up anything, I still have no idea what I am doing honestly.** | Ba2han/Reddit-instruct-curated_rated-1.2k | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | 2024-02-16T03:14:47+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"]} | 2024-02-16T03:50:26+00:00 |
88f10b1844a5d9330998736c30800510cb17a896 | franlucc/starcoderbase-1b-completions-cheeky_typeinf_analysis | [
"region:us"
] | 2024-02-16T03:21:14+00:00 | {"dataset_info": {"features": [{"name": "generated", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "hexsha", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "correctness", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 27377274, "num_examples": 5313}], "download_size": 5382971, "dataset_size": 27377274}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T03:21:15+00:00 |
|
7cd7243e4f7c07090530db29083d02fc34217020 | # Dataset Card for "Test_Dataset_1K-0216"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ouvic215/Test_Dataset_1K-0216 | [
"region:us"
] | 2024-02-16T03:22:22+00:00 | {"dataset_info": {"features": [{"name": "mask_image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 147332332.0, "num_examples": 1588}], "download_size": 146499523, "dataset_size": 147332332.0}} | 2024-02-16T03:22:49+00:00 |
d5ea52d256f7444e4bd6ed5aa0715b2d397789b9 | beta-reduction/webcrawl-202401 | [
"license:cc-by-sa-3.0",
"region:us"
] | 2024-02-16T03:23:07+00:00 | {"license": "cc-by-sa-3.0"} | 2024-02-16T03:52:17+00:00 |
|
5f036a24efb58c8445c2b647dd24ff7757cf6340 | wanz/jyshuju | [
"region:us"
] | 2024-02-16T03:27:19+00:00 | {} | 2024-02-16T07:05:57+00:00 |
|
fcca893e56e3a1f25286b13ab93070a202a37abe | Tarmana/pendahuluan_jurnal_pertanian | [
"task_categories:summarization",
"size_categories:n<1K",
"language:id",
"region:us"
] | 2024-02-16T03:29:07+00:00 | {"language": ["id"], "size_categories": ["n<1K"], "task_categories": ["summarization"]} | 2024-02-16T03:30:21+00:00 |
|
9a72476c0dc833b829d1b99b521ffa99a096b811 |
# Dataset
This repository contains the final dataset created using various resources. The primary datasets used for the construction of this final dataset are:
- [Telugu NLP Dataset from Kaggle](https://www.kaggle.com/datasets/sudalairajkumar/telugu-nlp)
- [Telugu ASR Corpus from HuggingFace](https://huggingface.co/datasets/parambharat/telugu_asr_corpus)
- [Wikipedia Telugu Dataset from Wikimedia on HuggingFace](https://huggingface.co/datasets/wikimedia/wikipedia)
These datasets have been combined to form a comprehensive resource for Telugu Natural Language Processing (NLP) tasks.
| indiehackers/telugu_dataset | [
"region:us"
] | 2024-02-16T03:32:53+00:00 | {"dataset_info": [{"config_name": "telugu_asr", "features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47887486, "num_examples": 209270}], "download_size": 20219871, "dataset_size": 47887486}, {"config_name": "telugu_nlp", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 387671180, "num_examples": 47415}], "download_size": 150012515, "dataset_size": 387671180}, {"config_name": "wikipedia", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 710613522, "num_examples": 87854}], "download_size": 209754217, "dataset_size": 710613522}], "configs": [{"config_name": "telugu_asr", "data_files": [{"split": "train", "path": "telugu_asr/train-*"}]}, {"config_name": "telugu_nlp", "data_files": [{"split": "train", "path": "telugu_nlp/train-*"}]}, {"config_name": "wikipedia", "data_files": [{"split": "train", "path": "wikipedia/train-*"}]}]} | 2024-02-16T03:40:32+00:00 |
9e242c6c2d693b7159fb8db57178c780bc925077 | trappy/katland_cat_XL | [
"region:us"
] | 2024-02-16T03:33:37+00:00 | {} | 2024-02-16T03:34:28+00:00 |
|
0c28e236eb0101cbe1e3da3cbce930b01de669ba | TJohnson250/OGSDI | [
"license:apache-2.0",
"region:us"
] | 2024-02-16T03:34:48+00:00 | {"license": "apache-2.0"} | 2024-02-16T03:41:24+00:00 |
|
b3dd583ce90282a9084fb4e4b3b7ca8978cc9abb |
# Dataset
This repository contains the final dataset created using various resources. The primary datasets used for the construction of this final dataset are:
- [Telugu NLP Dataset from Kaggle](https://www.kaggle.com/datasets/sudalairajkumar/telugu-nlp)
- [Telugu ASR Corpus from HuggingFace](https://huggingface.co/datasets/parambharat/telugu_asr_corpus)
- [Wikipedia Telugu Dataset from Wikimedia on HuggingFace](https://huggingface.co/datasets/wikimedia/wikipedia)
These datasets have been combined to form a comprehensive resource for Telugu Natural Language Processing (NLP) tasks.
| eswardivi/telugu_dataset | [
"region:us"
] | 2024-02-16T03:35:51+00:00 | {"dataset_info": [{"config_name": "telugu_asr", "features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47887486, "num_examples": 209270}], "download_size": 20219871, "dataset_size": 47887486}, {"config_name": "telugu_nlp", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 387671180, "num_examples": 47415}], "download_size": 150012515, "dataset_size": 387671180}, {"config_name": "wikipedia", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 710613522, "num_examples": 87854}], "download_size": 209754217, "dataset_size": 710613522}], "configs": [{"config_name": "telugu_asr", "data_files": [{"split": "train", "path": "telugu_asr/train-*"}]}, {"config_name": "telugu_nlp", "data_files": [{"split": "train", "path": "telugu_nlp/train-*"}]}, {"config_name": "wikipedia", "data_files": [{"split": "train", "path": "wikipedia/train-*"}]}]} | 2024-02-16T03:39:52+00:00 |
659935c6f026666ee8238090a3697c9d39116260 | Sophialucky/WheelScrewDemo | [
"region:us"
] | 2024-02-16T03:46:25+00:00 | {} | 2024-02-17T10:23:37+00:00 |
|
3cc0ea4cbc9f46eb2f18299fcdd05fca063ca2ff | presencesw/hash_fid_vi_sample | [
"region:us"
] | 2024-02-16T03:55:26+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3494014, "num_examples": 1000}, {"name": "validation", "num_bytes": 3177477, "num_examples": 917}, {"name": "test", "num_bytes": 1260384, "num_examples": 362}], "download_size": 3583868, "dataset_size": 7931875}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-17T03:10:26+00:00 |
|
f998e7a82884ba1b24397a871f1a7d2f478459f7 | H34lthy/Isotropy | [
"license:mit",
"region:us"
] | 2024-02-16T03:56:32+00:00 | {"license": "mit"} | 2024-02-16T03:57:57+00:00 |
|
467d4630cecf4064fa289da00cc4e033e4da6ed7 | djvictordance/vocal | [
"license:openrail",
"region:us"
] | 2024-02-16T04:02:44+00:00 | {"license": "openrail"} | 2024-02-16T04:06:05+00:00 |
|
a5a244a457e122977da71affda04262c5d0aed09 | justmalhar/aya_dataset_hindi | [
"region:us"
] | 2024-02-16T04:09:08+00:00 | {"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_code", "dtype": "string"}, {"name": "annotation_type", "dtype": "string"}, {"name": "user_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1453695.8054298195, "num_examples": 1155}, {"name": "test", "num_bytes": 0.0, "num_examples": 0}], "download_size": 599486, "dataset_size": 1453695.8054298195}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-16T04:09:17+00:00 |
|
1053ec6c8bedc4810d2b2242881e8ad6cabe7b2a | benayas/banking_augmented_10pct_v0 | [
"region:us"
] | 2024-02-16T04:12:42+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1018873, "num_examples": 10003}], "download_size": 411598, "dataset_size": 1018873}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T04:12:44+00:00 |
|
5190b4db7ab7037775447965088ae8f29928883a | karthikshf/cvparser | [
"region:us"
] | 2024-02-16T04:22:02+00:00 | {} | 2024-02-16T04:22:49+00:00 |
|
c91543bdc6ca004c4f0bbcb04be03dcda09caafe | djvictordance/vocal2 | [
"license:openrail",
"region:us"
] | 2024-02-16T04:22:04+00:00 | {"license": "openrail"} | 2024-02-16T04:23:51+00:00 |
|
91527921b78194abda879d03e23de72a1aa77233 | ayrus08/medcodgoogle | [
"region:us"
] | 2024-02-16T04:24:17+00:00 | {} | 2024-02-16T04:25:25+00:00 |
|
6f230bf6194f811930b4208f3a8f253125a236e1 | Utsav2001/Context-Orca | [
"region:us"
] | 2024-02-16T04:33:10+00:00 | {} | 2024-02-16T04:38:56+00:00 |
|
46cec9c2b00f0775d81f888c169e9de1ff003dd2 | krishan-CSE/HatEval_Relabled_with_Emotion_Author | [
"license:apache-2.0",
"region:us"
] | 2024-02-16T04:35:31+00:00 | {"license": "apache-2.0"} | 2024-02-16T04:35:47+00:00 |
|
d061ff56c1a3cabdbd47c91630c947bebf1c5d9f | chansung/test-dataset-repo | [
"region:us"
] | 2024-02-16T04:35:56+00:00 | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "assistant", "dtype": "string"}, {"name": "user", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 15024, "num_examples": 12}], "download_size": 11446, "dataset_size": 15024}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T05:11:55+00:00 |
|
d4614d53c5e02a57e720c36b1ed9b306980848b1 | jlbaker361/cyberpunk-lite-500-cropped | [
"region:us"
] | 2024-02-16T04:40:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "frame", "dtype": "int64"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5417030.0, "num_examples": 24}], "download_size": 5421221, "dataset_size": 5417030.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T04:40:07+00:00 |
|
2a1420176a8573433736795f8ea07c7fa19f7003 | karthikshf/lcv | [
"region:us"
] | 2024-02-16T04:40:15+00:00 | {} | 2024-02-16T04:42:02+00:00 |
|
29369aa7ebfa0c6a5184d742039373e92c1d1fac | wisenut-nlp-team/poc_small_negative | [
"region:us"
] | 2024-02-16T04:47:12+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": "string"}, {"name": "answer", "sequence": "string"}, {"name": "original_answer", "sequence": "string"}, {"name": "similar_contexts", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 9186186206.087706, "num_examples": 503927}], "download_size": 3999833332, "dataset_size": 9186186206.087706}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T04:56:02+00:00 |
|
b9aab1ab5834df51364f0b5d112978cfaa479851 | chunping-vi/pair_files | [
"region:us"
] | 2024-02-16T04:48:02+00:00 | {} | 2024-02-16T08:38:44+00:00 |
|
b4e8f468d7764c4f03a4133a4b3c779ed0876ce2 | jlbaker361/cyberpunk-lite-1000 | [
"region:us"
] | 2024-02-16T05:00:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "frame", "dtype": "int64"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2642369.0, "num_examples": 12}], "download_size": 2647158, "dataset_size": 2642369.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T05:00:42+00:00 |
|
c52f633509b7efe3793c39b94f14499e9640f850 | pradeep239/philp_plain_only5Years | [
"license:mit",
"region:us"
] | 2024-02-16T05:03:10+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 377350598.0, "num_examples": 793}, {"name": "validation", "num_bytes": 43159301.0, "num_examples": 94}, {"name": "test", "num_bytes": 22114074.0, "num_examples": 47}], "download_size": 320085727, "dataset_size": 442623973.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-16T06:17:42+00:00 |
|
1464e237ae0454afff2ec5c754067b48eaba8c04 | bala1524/drug-comb-data | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"biology",
"medical",
"region:us"
] | 2024-02-16T05:17:14+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "drug comb", "tags": ["biology", "medical"]} | 2024-02-16T05:20:12+00:00 |
|
cff0a10b314f47a52f9646ce3c38ec3b7d0b4852 |
# Dataset Card for Evaluation run of NLUHOPOE/test-case-0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [NLUHOPOE/test-case-0](https://huggingface.co/NLUHOPOE/test-case-0) 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_NLUHOPOE__test-case-0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T05:25:06.093843](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__test-case-0/blob/main/results_2024-02-16T05-25-06.093843.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": 0.5791278236658676,
"acc_stderr": 0.033494817808173614,
"acc_norm": 0.5837595891503912,
"acc_norm_stderr": 0.03419368461778056,
"mc1": 0.3268053855569155,
"mc1_stderr": 0.01641987473113503,
"mc2": 0.4880155663864428,
"mc2_stderr": 0.015371746911854285
},
"harness|arc:challenge|25": {
"acc": 0.5349829351535836,
"acc_stderr": 0.01457558392201967,
"acc_norm": 0.5750853242320819,
"acc_norm_stderr": 0.014445698968520769
},
"harness|hellaswag|10": {
"acc": 0.5997809201354312,
"acc_stderr": 0.004889413126208774,
"acc_norm": 0.796355307707628,
"acc_norm_stderr": 0.004018847286468061
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4888888888888889,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.4888888888888889,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5855263157894737,
"acc_stderr": 0.04008973785779206,
"acc_norm": 0.5855263157894737,
"acc_norm_stderr": 0.04008973785779206
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6490566037735849,
"acc_stderr": 0.02937364625323469,
"acc_norm": 0.6490566037735849,
"acc_norm_stderr": 0.02937364625323469
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6736111111111112,
"acc_stderr": 0.03921067198982266,
"acc_norm": 0.6736111111111112,
"acc_norm_stderr": 0.03921067198982266
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5086705202312138,
"acc_stderr": 0.03811890988940412,
"acc_norm": 0.5086705202312138,
"acc_norm_stderr": 0.03811890988940412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006718,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006718
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46382978723404256,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.46382978723404256,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.045981880578165414,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.045981880578165414
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3783068783068783,
"acc_stderr": 0.024976954053155254,
"acc_norm": 0.3783068783068783,
"acc_norm_stderr": 0.024976954053155254
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7129032258064516,
"acc_stderr": 0.025736542745594528,
"acc_norm": 0.7129032258064516,
"acc_norm_stderr": 0.025736542745594528
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.43842364532019706,
"acc_stderr": 0.03491207857486518,
"acc_norm": 0.43842364532019706,
"acc_norm_stderr": 0.03491207857486518
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.03192271569548301,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.03192271569548301
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.03173071239071724,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.03173071239071724
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8238341968911918,
"acc_stderr": 0.027493504244548047,
"acc_norm": 0.8238341968911918,
"acc_norm_stderr": 0.027493504244548047
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5923076923076923,
"acc_stderr": 0.02491524398598785,
"acc_norm": 0.5923076923076923,
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},
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"harness|gsm8k|5": {
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"acc_stderr": 0.013079933811800311
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_NLUHOPOE__test-case-0 | [
"region:us"
] | 2024-02-16T05:27:27+00:00 | {"pretty_name": "Evaluation run of NLUHOPOE/test-case-0", "dataset_summary": "Dataset automatically created during the evaluation run of model [NLUHOPOE/test-case-0](https://huggingface.co/NLUHOPOE/test-case-0) 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_NLUHOPOE__test-case-0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T05:25:06.093843](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__test-case-0/blob/main/results_2024-02-16T05-25-06.093843.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\": 0.5791278236658676,\n \"acc_stderr\": 0.033494817808173614,\n \"acc_norm\": 0.5837595891503912,\n \"acc_norm_stderr\": 0.03419368461778056,\n \"mc1\": 0.3268053855569155,\n \"mc1_stderr\": 0.01641987473113503,\n \"mc2\": 0.4880155663864428,\n \"mc2_stderr\": 0.015371746911854285\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5349829351535836,\n \"acc_stderr\": 0.01457558392201967,\n \"acc_norm\": 0.5750853242320819,\n \"acc_norm_stderr\": 0.014445698968520769\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5997809201354312,\n \"acc_stderr\": 0.004889413126208774,\n \"acc_norm\": 0.796355307707628,\n \"acc_norm_stderr\": 0.004018847286468061\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6736111111111112,\n \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5086705202312138,\n \"acc_stderr\": 0.03811890988940412,\n \"acc_norm\": 0.5086705202312138,\n \"acc_norm_stderr\": 0.03811890988940412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006718,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006718\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7129032258064516,\n \"acc_stderr\": 0.025736542745594528,\n \"acc_norm\": 0.7129032258064516,\n \"acc_norm_stderr\": 0.025736542745594528\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486518,\n \"acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486518\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548047,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548047\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5923076923076923,\n \"acc_stderr\": 0.02491524398598785,\n \"acc_norm\": 0.5923076923076923,\n \"acc_norm_stderr\": 0.02491524398598785\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119994,\n \"acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119994\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7596330275229358,\n \"acc_stderr\": 0.01832060732096407,\n \"acc_norm\": 0.7596330275229358,\n \"acc_norm_stderr\": 0.01832060732096407\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437378,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["**/details_harness|winogrande|5_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T05-25-06.093843.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T05_25_06.093843", "path": ["results_2024-02-16T05-25-06.093843.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T05-25-06.093843.parquet"]}]}]} | 2024-02-16T05:27:47+00:00 |
4a2e29cd0c17e294217b49b3abdf9433e6c55473 | Jonakhawbs/linkedin_dataset_hire | [
"region:us"
] | 2024-02-16T05:30:22+00:00 | {} | 2024-02-16T05:33:27+00:00 |
|
2a63d669ac8c2994f6e618528dec466208041853 |
This dataset consists ColBERTv2.0 document vectors for the entire TREC-COVID dataset from BeIR. That 128 dimension per token, with 180 tokens for each of 171332 documents.
The dataset was created using A100-40GB sponsored by Qdrant. The code to create these vectors is here: https://colab.research.google.com/drive/1hEhyleSrBz_mPyQJnRc0MwBenDuX1ahY?usp=sharing
This dataset was created for indexing experiments by Qdrant. | Qdrant/ColBERT-TREC-COVID | [
"task_categories:feature-extraction",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"medical",
"region:us"
] | 2024-02-16T05:35:27+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"], "pretty_name": "ColBERT TREC COVID", "dataset_info": {"features": [{"name": "documents", "sequence": {"sequence": "float16"}}], "splits": [{"name": "train", "num_bytes": 8019022928, "num_examples": 171332}], "download_size": 5775769873, "dataset_size": 8019022928}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["medical"]} | 2024-02-16T06:10:04+00:00 |
106f24d6f230aea99516287a1e136e9e29e4945d | benayas/banking_augmented_20pct_v0 | [
"region:us"
] | 2024-02-16T05:41:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1018827, "num_examples": 10003}], "download_size": 425615, "dataset_size": 1018827}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T05:41:07+00:00 |
|
3835f192b287746f00fc85ffff28b1b097a4cc52 | dooooosooooo/tofu_character_ai | [
"task_categories:question-answering",
"language:ko",
"license:unknown",
"region:us"
] | 2024-02-16T06:10:26+00:00 | {"language": ["ko"], "license": "unknown", "task_categories": ["question-answering"]} | 2024-02-16T06:22:59+00:00 |
|
e66fbe25b1e23cf967f1d2832a4e06b7717c808f | JJini/honsol | [
"region:us"
] | 2024-02-16T06:14:25+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6682255, "num_examples": 12880}], "download_size": 1056734, "dataset_size": 6682255}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T06:14:35+00:00 |
|
9824f1afb87451b3837e585d85eb93896dfe1107 |
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Matichon Maneegard
- **Shared by [optional]:** Matichon Maneegard
- **Language(s) (NLP):** image-to-text
- **License:** apache-2.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
The dataset was entirely synthetic. It does not contain real information or pertain to any specific person.
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
Using for tranning OCR or Multimodal.
## 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. -->
This dataset contains 98 x 6 = 588 samples, and the labels contain 98 samples.
Each sample will have a different scenario to represent.
The 'train.csv' file contains 11 attributes:
```File_Index, first_name_th, first_name_en, last_name_en, birth_date_th, birth_date_en, religion, first_address_th, second_address_th, third_address_th, forth_address_th```
The 'File_Index' corresponds to the number of the image in the scenario with the training data.
It means that '/Scenario_1/file_1.png' has the same attributes as '/Scenario_2/file_1.png'.
### 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.
### Contact
Twitter : (Mati)[https://twitter.com/KMatiDev1]
E-mail : [email protected]
VulturePrime : (VulturePrime)[https://vultureprime.com]
Float16 : (Float16.cloud)[https://float16.cloud]
| matichon/ThaiIDCardSynt | [
"task_categories:image-to-text",
"size_categories:n<1K",
"language:th",
"license:apache-2.0",
"region:us"
] | 2024-02-16T06:18:15+00:00 | {"language": ["th"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["image-to-text"]} | 2024-02-16T07:35:32+00:00 |
d36778cf817b687b55988c45dfc662dd4c8b6c23 | taesiri/simple_fsm_bench_long_text | [
"region:us"
] | 2024-02-16T06:20:49+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "fsm_json", "dtype": "string"}, {"name": "long_string", "dtype": "string"}, {"name": "final_acceptance_status", "dtype": "string"}, {"name": "acceptance_status", "sequence": "string"}, {"name": "char_list", "sequence": "string"}, {"name": "state_list", "sequence": "string"}, {"name": "final_state", "dtype": "string"}, {"name": "difficulty_level", "dtype": "int64"}, {"name": "dot", "dtype": "string"}, {"name": "transition_matrix", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 6435777, "num_examples": 1000}], "download_size": 1029673, "dataset_size": 6435777}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2024-02-16T06:20:50+00:00 |
|
7fa3bcad12f32cbbbb10c60a67ae5aae1376df59 | ammaralam/guanaco-llama2-1k | [
"region:us"
] | 2024-02-16T06:22:34+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966692, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T06:22:36+00:00 |
|
048fc1a5f3f8cf3f8d050f1e727bce1233b32f12 |
# Dataset Card for Evaluation run of Undi95/PsyMedRP-v1-20B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Undi95/PsyMedRP-v1-20B](https://huggingface.co/Undi95/PsyMedRP-v1-20B) 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_Undi95__PsyMedRP-v1-20B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T06:33:57.302712](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__PsyMedRP-v1-20B/blob/main/results_2024-02-16T06-33-57.302712.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": 0.5647260784625223,
"acc_stderr": 0.033553791007284096,
"acc_norm": 0.5721079188379258,
"acc_norm_stderr": 0.03429829853750649,
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142985,
"mc2": 0.5444967551355537,
"mc2_stderr": 0.015846880267326138
},
"harness|arc:challenge|25": {
"acc": 0.5861774744027304,
"acc_stderr": 0.014392730009221009,
"acc_norm": 0.6049488054607508,
"acc_norm_stderr": 0.01428589829293817
},
"harness|hellaswag|10": {
"acc": 0.6552479585739892,
"acc_stderr": 0.004743160034271149,
"acc_norm": 0.8393746265684127,
"acc_norm_stderr": 0.0036643462998943955
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5037037037037037,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.5037037037037037,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5394736842105263,
"acc_stderr": 0.04056242252249034,
"acc_norm": 0.5394736842105263,
"acc_norm_stderr": 0.04056242252249034
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5924528301886792,
"acc_stderr": 0.030242233800854494,
"acc_norm": 0.5924528301886792,
"acc_norm_stderr": 0.030242233800854494
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5972222222222222,
"acc_stderr": 0.04101405519842426,
"acc_norm": 0.5972222222222222,
"acc_norm_stderr": 0.04101405519842426
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5086705202312138,
"acc_stderr": 0.038118909889404105,
"acc_norm": 0.5086705202312138,
"acc_norm_stderr": 0.038118909889404105
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006716,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006716
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.66,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.66,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46808510638297873,
"acc_stderr": 0.03261936918467381,
"acc_norm": 0.46808510638297873,
"acc_norm_stderr": 0.03261936918467381
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.04096985139843671,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.04096985139843671
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.02441923496681907,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.02441923496681907
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.04240799327574924,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.04240799327574924
},
"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": 0.6741935483870968,
"acc_stderr": 0.026662010578567107,
"acc_norm": 0.6741935483870968,
"acc_norm_stderr": 0.026662010578567107
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.458128078817734,
"acc_stderr": 0.03505630140785741,
"acc_norm": 0.458128078817734,
"acc_norm_stderr": 0.03505630140785741
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.03546563019624336,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.03546563019624336
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.031911782267135466,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.031911782267135466
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7979274611398963,
"acc_stderr": 0.028979089794296732,
"acc_norm": 0.7979274611398963,
"acc_norm_stderr": 0.028979089794296732
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5538461538461539,
"acc_stderr": 0.02520357177302833,
"acc_norm": 0.5538461538461539,
"acc_norm_stderr": 0.02520357177302833
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066482,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066482
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6050420168067226,
"acc_stderr": 0.031753678460966266,
"acc_norm": 0.6050420168067226,
"acc_norm_stderr": 0.031753678460966266
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7211009174311926,
"acc_stderr": 0.0192274688764635,
"acc_norm": 0.7211009174311926,
"acc_norm_stderr": 0.0192274688764635
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.03388857118502326,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.03388857118502326
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654362,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654362
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7468354430379747,
"acc_stderr": 0.02830465794303529,
"acc_norm": 0.7468354430379747,
"acc_norm_stderr": 0.02830465794303529
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.648854961832061,
"acc_stderr": 0.04186445163013751,
"acc_norm": 0.648854961832061,
"acc_norm_stderr": 0.04186445163013751
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794089,
"acc_norm": 0.768595041322314,
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},
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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": 0.14859742228961334,
"acc_stderr": 0.009797503180527883
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### 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. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Undi95__PsyMedRP-v1-20B | [
"region:us"
] | 2024-02-16T06:36:16+00:00 | {"pretty_name": "Evaluation run of Undi95/PsyMedRP-v1-20B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/PsyMedRP-v1-20B](https://huggingface.co/Undi95/PsyMedRP-v1-20B) 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_Undi95__PsyMedRP-v1-20B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T06:33:57.302712](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__PsyMedRP-v1-20B/blob/main/results_2024-02-16T06-33-57.302712.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\": 0.5647260784625223,\n \"acc_stderr\": 0.033553791007284096,\n \"acc_norm\": 0.5721079188379258,\n \"acc_norm_stderr\": 0.03429829853750649,\n \"mc1\": 0.379436964504284,\n \"mc1_stderr\": 0.016987039266142985,\n \"mc2\": 0.5444967551355537,\n \"mc2_stderr\": 0.015846880267326138\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5861774744027304,\n \"acc_stderr\": 0.014392730009221009,\n \"acc_norm\": 0.6049488054607508,\n \"acc_norm_stderr\": 0.01428589829293817\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6552479585739892,\n \"acc_stderr\": 0.004743160034271149,\n \"acc_norm\": 0.8393746265684127,\n \"acc_norm_stderr\": 0.0036643462998943955\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5037037037037037,\n \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.5037037037037037,\n \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5394736842105263,\n \"acc_stderr\": 0.04056242252249034,\n \"acc_norm\": 0.5394736842105263,\n \"acc_norm_stderr\": 0.04056242252249034\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5972222222222222,\n \"acc_stderr\": 0.04101405519842426,\n \"acc_norm\": 0.5972222222222222,\n \"acc_norm_stderr\": 0.04101405519842426\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5086705202312138,\n \"acc_stderr\": 0.038118909889404105,\n \"acc_norm\": 0.5086705202312138,\n \"acc_norm_stderr\": 0.038118909889404105\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006716,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006716\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.047609522856952365\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467381,\n \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467381\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3412698412698413,\n \"acc_stderr\": 0.02441923496681907,\n \"acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.02441923496681907\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n \"acc_stderr\": 0.04240799327574924,\n \"acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.04240799327574924\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\": 0.6741935483870968,\n \"acc_stderr\": 0.026662010578567107,\n \"acc_norm\": 0.6741935483870968,\n \"acc_norm_stderr\": 0.026662010578567107\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.031911782267135466,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.031911782267135466\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.028979089794296732,\n \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.028979089794296732\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066482,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066482\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6050420168067226,\n \"acc_stderr\": 0.031753678460966266,\n \"acc_norm\": 0.6050420168067226,\n \"acc_norm_stderr\": 0.031753678460966266\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\": 0.7211009174311926,\n \"acc_stderr\": 0.0192274688764635,\n \"acc_norm\": 0.7211009174311926,\n \"acc_norm_stderr\": 0.0192274688764635\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502326,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502326\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654362,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654362\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7468354430379747,\n \"acc_stderr\": 0.02830465794303529,\n \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.02830465794303529\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794089,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794089\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6932515337423313,\n \"acc_stderr\": 0.03623089915724147,\n \"acc_norm\": 0.6932515337423313,\n \"acc_norm_stderr\": 0.03623089915724147\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6504854368932039,\n \"acc_stderr\": 0.047211885060971716,\n \"acc_norm\": 0.6504854368932039,\n \"acc_norm_stderr\": 0.047211885060971716\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.811965811965812,\n \"acc_stderr\": 0.02559819368665225,\n \"acc_norm\": 0.811965811965812,\n \"acc_norm_stderr\": 0.02559819368665225\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 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64f35b28ffff24cc2eb0773f807368da32873599 | 本数据集共138min，大概包含yousa的50首歌（大部分在2016-2022年），已经过切片处理并筛选，时长在4-15s，共796条wav音频数据。中文占绝大部分，有少量日文及极少英文。
This dataset consists of 138 minutes in total and approximately includes 50 songs by yousa (most of them released between 2016 and 2022). The dataset has been sliced and filtered, with durations ranging from 4 to 15 seconds, resulting in a total of 796 WAV audio files. The majority of the content is in Chinese, with a small amount in Japanese and very little in English. | yousaforever/yousa_data_0 | [
"license:gpl-3.0",
"region:us"
] | 2024-02-16T06:49:58+00:00 | {"license": "gpl-3.0"} | 2024-02-16T06:59:56+00:00 |
cbad2bf81120a84400915e19ea76eb3fb437132a | bertram-gilfoyle/CC-MAIN-2023-40 | [
"region:us"
] | 2024-02-16T06:50:46+00:00 | {} | 2024-02-16T11:30:22+00:00 |
|
de13ee43091b66636c7be0bf0152a158fbe2a486 | marcones/marcoselementar | [
"license:openrail",
"region:us"
] | 2024-02-16T06:58:39+00:00 | {"license": "openrail"} | 2024-02-16T06:58:52+00:00 |
|
5cc645f5f3e9116d75dfcc66d1ff3f4a497df607 | 大约9min的正常说话声音，划分为70个切片，可用于训练tts模型。
A 9-minute normal speaking voice divided into 70 slices for training a TTS model. | yousaforever/yousa_data_1 | [
"license:gpl-3.0",
"region:us"
] | 2024-02-16T07:03:45+00:00 | {"license": "gpl-3.0"} | 2024-02-16T07:10:56+00:00 |
bec85a7b45ef8b3ed51710621404935762eb5ed6 | Ketan3101/bot_train | [
"license:mit",
"region:us"
] | 2024-02-16T07:07:51+00:00 | {"license": "mit"} | 2024-02-16T09:53:44+00:00 |
|
192462fe2072e3a1d2c1e32c02170978328bddf1 |
# Dataset Card for Evaluation run of jisukim8873/falcon-7B-case-6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jisukim8873/falcon-7B-case-6](https://huggingface.co/jisukim8873/falcon-7B-case-6) 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_jisukim8873__falcon-7B-case-6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T07:12:28.485530](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6/blob/main/results_2024-02-16T07-12-28.485530.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": 0.032195034392452436,
"acc_norm": 0.30103224915319854,
"acc_norm_stderr": 0.032944763241990214,
"mc1": 0.25091799265605874,
"mc1_stderr": 0.015176985027707687,
"mc2": 0.364571668218642,
"mc2_stderr": 0.014117416041879967
},
"harness|arc:challenge|25": {
"acc": 0.4274744027303754,
"acc_stderr": 0.014456862944650654,
"acc_norm": 0.46501706484641636,
"acc_norm_stderr": 0.014575583922019665
},
"harness|hellaswag|10": {
"acc": 0.5976897032463653,
"acc_stderr": 0.0048936170149753,
"acc_norm": 0.7849034056960765,
"acc_norm_stderr": 0.004100495978108428
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.36,
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.3018867924528302,
"acc_norm_stderr": 0.028254200344438662
},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.040925639582376536
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.33,
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"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159394
},
"harness|hendrycksTest-electrical_engineering|5": {
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},
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.030557101589417515
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.33,
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},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.02679556084812279
},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm_stderr": 0.026202766534652155
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.24369747899159663,
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"acc_norm": 0.24369747899159663,
"acc_norm_stderr": 0.027886828078380572
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
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"acc_norm": 0.2781456953642384,
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},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.28990825688073396,
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"acc_norm": 0.28990825688073396,
"acc_norm_stderr": 0.019453066609201597
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.19444444444444445,
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"acc_norm": 0.19444444444444445,
"acc_norm_stderr": 0.026991454502036744
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27450980392156865,
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"acc_norm": 0.27450980392156865,
"acc_norm_stderr": 0.03132179803083289
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.31645569620253167,
"acc_stderr": 0.03027497488021897,
"acc_norm": 0.31645569620253167,
"acc_norm_stderr": 0.03027497488021897
},
"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm_stderr": 0.04489931073591312
},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.2857142857142857,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.32038834951456313,
"acc_stderr": 0.04620284082280039,
"acc_norm": 0.32038834951456313,
"acc_norm_stderr": 0.04620284082280039
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.3076923076923077,
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"acc_norm": 0.3076923076923077,
"acc_norm_stderr": 0.03023638994217307
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24692737430167597,
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"acc_norm": 0.24692737430167597,
"acc_norm_stderr": 0.014422292204808835
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.3202614379084967,
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},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.42,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.3253012048192771,
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"acc_norm": 0.3253012048192771,
"acc_norm_stderr": 0.03647168523683227
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3391812865497076,
"acc_stderr": 0.03631053496488905,
"acc_norm": 0.3391812865497076,
"acc_norm_stderr": 0.03631053496488905
},
"harness|truthfulqa:mc|0": {
"mc1": 0.25091799265605874,
"mc1_stderr": 0.015176985027707687,
"mc2": 0.364571668218642,
"mc2_stderr": 0.014117416041879967
},
"harness|winogrande|5": {
"acc": 0.7008681925808997,
"acc_stderr": 0.012868639066091541
},
"harness|gsm8k|5": {
"acc": 0.06141015921152388,
"acc_stderr": 0.006613027536586305
}
}
```
## 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. -->
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#### Personal and Sensitive Information
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[More Information Needed] | open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6 | [
"region:us"
] | 2024-02-16T07:14:11+00:00 | {"pretty_name": "Evaluation run of jisukim8873/falcon-7B-case-6", "dataset_summary": "Dataset automatically created during the evaluation run of model [jisukim8873/falcon-7B-case-6](https://huggingface.co/jisukim8873/falcon-7B-case-6) 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_jisukim8873__falcon-7B-case-6\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T07:12:28.485530](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6/blob/main/results_2024-02-16T07-12-28.485530.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\": 0.2999741752010719,\n \"acc_stderr\": 0.032195034392452436,\n \"acc_norm\": 0.30103224915319854,\n \"acc_norm_stderr\": 0.032944763241990214,\n \"mc1\": 0.25091799265605874,\n \"mc1_stderr\": 0.015176985027707687,\n \"mc2\": 0.364571668218642,\n \"mc2_stderr\": 0.014117416041879967\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4274744027303754,\n \"acc_stderr\": 0.014456862944650654,\n \"acc_norm\": 0.46501706484641636,\n \"acc_norm_stderr\": 0.014575583922019665\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5976897032463653,\n \"acc_stderr\": 0.0048936170149753,\n \"acc_norm\": 0.7849034056960765,\n \"acc_norm_stderr\": 0.004100495978108428\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.3026315789473684,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.3026315789473684,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.3018867924528302,\n \"acc_stderr\": 0.028254200344438662,\n \"acc_norm\": 0.3018867924528302,\n \"acc_norm_stderr\": 0.028254200344438662\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.2658959537572254,\n \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.040925639582376536,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.040925639582376536\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3148936170212766,\n \"acc_stderr\": 0.03036358219723817,\n \"acc_norm\": 0.3148936170212766,\n \"acc_norm_stderr\": 0.03036358219723817\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.037245636197746325,\n \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.037245636197746325\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.02256989707491841,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02256989707491841\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1349206349206349,\n \"acc_stderr\": 0.030557101589417515,\n \"acc_norm\": 0.1349206349206349,\n \"acc_norm_stderr\": 0.030557101589417515\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.33225806451612905,\n \"acc_stderr\": 0.02679556084812279,\n \"acc_norm\": 0.33225806451612905,\n \"acc_norm_stderr\": 0.02679556084812279\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3151515151515151,\n \"acc_stderr\": 0.0362773057502241,\n \"acc_norm\": 0.3151515151515151,\n \"acc_norm_stderr\": 0.0362773057502241\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.30303030303030304,\n \"acc_stderr\": 0.03274287914026869,\n \"acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.03274287914026869\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.25906735751295334,\n \"acc_stderr\": 0.03161877917935411,\n \"acc_norm\": 0.25906735751295334,\n \"acc_norm_stderr\": 0.03161877917935411\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.021840866990423095,\n \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.021840866990423095\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.026202766534652155,\n \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.026202766534652155\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.24369747899159663,\n \"acc_stderr\": 0.027886828078380572,\n \"acc_norm\": 0.24369747899159663,\n \"acc_norm_stderr\": 0.027886828078380572\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.28990825688073396,\n \"acc_stderr\": 0.019453066609201597,\n \"acc_norm\": 0.28990825688073396,\n \"acc_norm_stderr\": 0.019453066609201597\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.19444444444444445,\n \"acc_stderr\": 0.026991454502036744,\n \"acc_norm\": 0.19444444444444445,\n \"acc_norm_stderr\": 0.026991454502036744\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.03132179803083289,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.03132179803083289\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.31645569620253167,\n \"acc_stderr\": 0.03027497488021897,\n \"acc_norm\": 0.31645569620253167,\n \"acc_norm_stderr\": 0.03027497488021897\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.37668161434977576,\n \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.37668161434977576,\n \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.03880848301082396,\n \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.03880848301082396\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.4132231404958678,\n \"acc_stderr\": 0.04495087843548408,\n \"acc_norm\": 0.4132231404958678,\n \"acc_norm_stderr\": 0.04495087843548408\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.035590395316173425,\n \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.035590395316173425\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.042878587513404565,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.042878587513404565\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.32038834951456313,\n \"acc_stderr\": 0.04620284082280039,\n \"acc_norm\": 0.32038834951456313,\n \"acc_norm_stderr\": 0.04620284082280039\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n \"acc_stderr\": 0.03023638994217307,\n \"acc_norm\": 0.3076923076923077,\n \"acc_norm_stderr\": 0.03023638994217307\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3537675606641124,\n \"acc_stderr\": 0.017098184708161903,\n \"acc_norm\": 0.3537675606641124,\n \"acc_norm_stderr\": 0.017098184708161903\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.3236994219653179,\n \"acc_stderr\": 0.025190181327608422,\n \"acc_norm\": 0.3236994219653179,\n \"acc_norm_stderr\": 0.025190181327608422\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.3202614379084967,\n \"acc_stderr\": 0.026716118380156844,\n \"acc_norm\": 0.3202614379084967,\n \"acc_norm_stderr\": 0.026716118380156844\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3183279742765273,\n \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.3183279742765273,\n \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.024922001168886335,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.024922001168886335\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.24113475177304963,\n \"acc_stderr\": 0.02551873104953776,\n \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.02551873104953776\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2627118644067797,\n \"acc_stderr\": 0.01124054551499567,\n \"acc_norm\": 0.2627118644067797,\n \"acc_norm_stderr\": 0.01124054551499567\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.21323529411764705,\n \"acc_stderr\": 0.024880971512294292,\n \"acc_norm\": 0.21323529411764705,\n \"acc_norm_stderr\": 0.024880971512294292\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n \"acc_stderr\": 0.04122066502878284,\n \"acc_norm\": 0.24545454545454545,\n \"acc_norm_stderr\": 0.04122066502878284\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017493,\n \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017493\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3034825870646766,\n \"acc_stderr\": 0.032510068164586174,\n \"acc_norm\": 0.3034825870646766,\n \"acc_norm_stderr\": 0.032510068164586174\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3253012048192771,\n \"acc_stderr\": 0.03647168523683227,\n \"acc_norm\": 0.3253012048192771,\n \"acc_norm_stderr\": 0.03647168523683227\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3391812865497076,\n \"acc_stderr\": 0.03631053496488905,\n \"acc_norm\": 0.3391812865497076,\n \"acc_norm_stderr\": 0.03631053496488905\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25091799265605874,\n \"mc1_stderr\": 0.015176985027707687,\n \"mc2\": 0.364571668218642,\n \"mc2_stderr\": 0.014117416041879967\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7008681925808997,\n \"acc_stderr\": 0.012868639066091541\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06141015921152388,\n \"acc_stderr\": 0.006613027536586305\n }\n}\n```", "repo_url": "https://huggingface.co/jisukim8873/falcon-7B-case-6", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|arc:challenge|25_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|gsm8k|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hellaswag|10_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-16T07-12-28.485530.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-16T07-12-28.485530.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["**/details_harness|winogrande|5_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T07-12-28.485530.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T07_12_28.485530", "path": ["results_2024-02-16T07-12-28.485530.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T07-12-28.485530.parquet"]}]}]} | 2024-02-16T07:14:33+00:00 |
17b17b1a5a333d153426db496a222394f81ff515 | shrms/structured-data | [
"region:us"
] | 2024-02-16T07:16:45+00:00 | {} | 2024-02-16T07:16:45+00:00 |
|
bb6f259fcae3ac2babbd461480306097b56e0fd4 | doanhm/images_input | [
"region:us"
] | 2024-02-16T07:22:50+00:00 | {} | 2024-02-16T07:33:03+00:00 |
|
3e28d7e97cd337e7204aaa32ede07ee661e32daa | KimByeongSu/full | [
"region:us"
] | 2024-02-16T07:23:47+00:00 | {} | 2024-02-16T07:24:04+00:00 |
|
792f28be3d1964ddbf74fc09ada1bce675a765bf | andersonbcdefg/misc_qa_pairs_v2_deduped_cf | [
"region:us"
] | 2024-02-16T07:28:50+00:00 | {"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 745217529.7175856, "num_examples": 1336274}], "download_size": 515527807, "dataset_size": 745217529.7175856}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T07:35:10+00:00 |
|
2a62a9ea17e2bcf749ee557d849856f73fb585ba | ll00292007/my-dataset-lora | [
"region:us"
] | 2024-02-16T07:32:15+00:00 | {} | 2024-02-16T07:32:15+00:00 |
|
4435181c6da6a5236a0ad5a0c9df3f102c797dbe | HEMASENTHIL/NEWDEMO | [
"region:us"
] | 2024-02-16T07:35:46+00:00 | {"dataset_info": {"features": [{"name": "english", "dtype": "string"}, {"name": "tamil", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6350, "num_examples": 9}], "download_size": 9674, "dataset_size": 6350}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T07:38:51+00:00 |
|
28c35a4152ed69188fc9eb344579fc577016c925 |
# Dataset Card for Evaluation run of Kquant03/Buttercup-V2-laser
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kquant03/Buttercup-V2-laser](https://huggingface.co/Kquant03/Buttercup-V2-laser) 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_Kquant03__Buttercup-V2-laser",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T07:34:11.973720](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__Buttercup-V2-laser/blob/main/results_2024-02-16T07-34-11.973720.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": 0.6535761549256881,
"acc_stderr": 0.03205604876868876,
"acc_norm": 0.6528640185317818,
"acc_norm_stderr": 0.032733047429496384,
"mc1": 0.5520195838433293,
"mc1_stderr": 0.017408513063422917,
"mc2": 0.6899750707536572,
"mc2_stderr": 0.01507018824423322
},
"harness|arc:challenge|25": {
"acc": 0.7081911262798635,
"acc_stderr": 0.013284525292403511,
"acc_norm": 0.7312286689419796,
"acc_norm_stderr": 0.012955065963710698
},
"harness|hellaswag|10": {
"acc": 0.7135032861979685,
"acc_stderr": 0.004512002459757956,
"acc_norm": 0.8847839075881299,
"acc_norm_stderr": 0.0031863002304505753
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Kquant03__Buttercup-V2-laser | [
"region:us"
] | 2024-02-16T07:36:29+00:00 | {"pretty_name": "Evaluation run of Kquant03/Buttercup-V2-laser", "dataset_summary": "Dataset automatically created during the evaluation run of model [Kquant03/Buttercup-V2-laser](https://huggingface.co/Kquant03/Buttercup-V2-laser) 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_Kquant03__Buttercup-V2-laser\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T07:34:11.973720](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__Buttercup-V2-laser/blob/main/results_2024-02-16T07-34-11.973720.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\": 0.6535761549256881,\n \"acc_stderr\": 0.03205604876868876,\n \"acc_norm\": 0.6528640185317818,\n \"acc_norm_stderr\": 0.032733047429496384,\n \"mc1\": 0.5520195838433293,\n \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6899750707536572,\n \"mc2_stderr\": 0.01507018824423322\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7081911262798635,\n \"acc_stderr\": 0.013284525292403511,\n \"acc_norm\": 0.7312286689419796,\n \"acc_norm_stderr\": 0.012955065963710698\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7135032861979685,\n \"acc_stderr\": 0.004512002459757956,\n \"acc_norm\": 0.8847839075881299,\n \"acc_norm_stderr\": 0.0031863002304505753\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n 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d51dee2e2b1ec50127a6338e07966b820cf30842 | chansung/janus-test-ds | [
"region:us"
] | 2024-02-16T07:37:31+00:00 | {"dataset_info": {"features": [{"name": "model", "dtype": "string"}, {"name": "conversations", "list": [{"name": "assistant", "dtype": "string"}, {"name": "user", "dtype": "string"}]}, {"name": "type", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "conversation", "list": [{"name": "assistant", "dtype": "string"}, {"name": "user", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 96757.43396226416, "num_examples": 49}], "download_size": 54382, "dataset_size": 96757.43396226416}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T08:02:29+00:00 |
|
2591781a02b2c91ad1bea58d1bbf9bbebaddcdb1 | benayas/banking_augmented_5pct_v1 | [
"region:us"
] | 2024-02-16T07:54:37+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1025004, "num_examples": 10003}], "download_size": 399224, "dataset_size": 1025004}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-16T07:54:40+00:00 |
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