datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
onkarsus13/segment_med_images | ---
license: mit
---
|
davidberenstein1957/text2text_translation | ---
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---
# Dataset Card for "text2text_translation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
desik98/telugu_paraphrase_instruction_tune_iith | ---
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---
# Dataset Card for "telugu_paraphrase_instruction_tune_iith"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zilu-peter-tang/MultiPL-C2C | ---
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---
# Dataset Card for MultiPL-C2C
## Dataset Description
- **Repository:** https://github.com/PootieT/explain-then-translate
- **Paper:** https://openreview.net/forum?id=uyUO80sbm0
- **Point of Contact:** zilutang@bu.edu
## Dataset Summary
MultiPL-C2C is a dataset for evaluating large language models for code
translation between any of the 19 programming languages. It is an extension
from MultiPL-E dataset, which takes the OpenAI "HumanEval" and the MBPP
Python benchmarks and uses little compilers to translate them to other
languages. On top of MultiPL-E, it is easy to add support for new languages
and benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.
## Subsets
To evaluate program translation with MultiPL-C2C, you need mainly three
arguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension
for each language to identify the language, e.g., "py" for Python, "cpp" for
C++, "lua" for Lua, and so on. *EXP* specifies what type of prompting is done for
the translation: `direct` is direct translation. {`exp`, `exp-lbl`, `exp-lbl-d`}
are other explain-then-translate method. See paper for detailed description.
Our paper ["explain-then-translate"](https://openreview.net/forum?id=uyUO80sbm0)
explores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot
translation accuracy. We also provide a few other variations:
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we
used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,
we cached Python-Java intermediate generations (explanations), and reused them
in other Python-to-X directions.
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for
our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all
intermediate generations (explanations) in the prompts. Different from
*SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as `Lua-Python` will have
explanations generated by GPT-3.5
Note that we did not experiment with MBPP even thought it is available with
MultiPL-E. Feel free to PR and we can include them to further improve
code-translation datasets.
## Example
The following script uses the Salesforce/codegen model to translate Python-Lua
and MultiPL-E to produce a script with unit tests for luaunit.
```python
import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM
SRC_LANG = "py"
TGT_LANG = "lua"
EXP = "direct"
MODEL_NAME = "Salesforce/codegen-350M-multi"
TOFILL_TOKEN = "{{{TOFILL}}}"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
problems = datasets.load_dataset("zilu-peter-tang/MultiPL-C2C", f"humaneval-{SRC_LANG}-{TGT_LANG}-{EXP}")
def stop_at_stop_token(decoded_string, problem):
"""
Truncates the output at stop tokens, taking care to skip the prompt
which may have stop tokens.
"""
min_stop_index = len(decoded_string)
for stop_token in problem["stop_tokens"]:
stop_index = decoded_string.find(stop_token, len(problem["translation_prompt"]))
if stop_index != -1 and stop_index < min_stop_index:
min_stop_index = stop_index
return decoded_string[:min_stop_index]
def generate(problem, tokenizer, model):
input_ids = tokenizer(
problem["translation_prompt"],
return_tensors="pt",
).input_ids.cuda()
generated_ids = model.generate(
input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
)
truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
return truncated_string
for problem in problems["test"]:
# generate each intermediate steps (explanation) sequentially, if any
stop = problem["stop_tokens"]
prompt = problem["translation_prompt"]
while TOFILL_TOKEN in problem["translation_prompt"]:
incomplete_prompt = prompt[:prompt.find(TOFILL_TOKEN)]
problem["stop_tokens"] = ["\n#"]
problem["translation_prompt"] = incomplete_prompt
intermediate_completion = generate(problem, tokenizer, model).replace(incomplete_prompt, "")
prompt = prompt.replace(TOFILL_TOKEN, intermediate_completion, 1)
# finally generate translation
problem["translation_prompt"] = prompt
problem["stop_tokens"] = stop
completion = generate(problem, tokenizer, model).replace(prompt, "")
filename = problem["name"] + "-" + SRC_LANG + "-" + TGT_LANG + "." + TGT_LANG
with open(filename, "w") as f:
print(f"Created {filename}")
f.write(problem["prompt"])
f.write(completion)
f.write("\n")
f.write(problem["tests"])
```
Depending on the quality of the model, you may or may not find some extraneous natural langauge
or program commands at the end of the generated functions. To trim them using programmatic rules,
see our [repo](https://github.com/PootieT/explain-then-translate/blob/main/MultiPL-C2C/inference/__main__.py)
for a more detailed inference script that takes care of this in post-processing.
For few shot examples checkout our all other translation prompts used [here](https://github.com/PootieT/explain-then-translate/tree/main/MultiPL-C2C/translation_prompts/translation_prompts_chatgpt_with_explanation) |
open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-7 | ---
pretty_name: Evaluation run of juhwanlee/llmdo-Mistral-7B-case-7
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [juhwanlee/llmdo-Mistral-7B-case-7](https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-7)\
\ 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-7\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-11T02:59:07.184028](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-7/blob/main/results_2024-03-11T02-59-07.184028.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\": 0.6293874204442154,\n\
\ \"acc_stderr\": 0.03245765893138893,\n \"acc_norm\": 0.6345328719122807,\n\
\ \"acc_norm_stderr\": 0.03311526736789786,\n \"mc1\": 0.31456548347613217,\n\
\ \"mc1_stderr\": 0.01625524199317918,\n \"mc2\": 0.4582610592701175,\n\
\ \"mc2_stderr\": 0.014684929022027894\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5878839590443686,\n \"acc_stderr\": 0.014383915302225403,\n\
\ \"acc_norm\": 0.6194539249146758,\n \"acc_norm_stderr\": 0.014188277712349812\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6354311890061741,\n\
\ \"acc_stderr\": 0.004803253812881043,\n \"acc_norm\": 0.8353913563035252,\n\
\ \"acc_norm_stderr\": 0.003700690995600888\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\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\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\": 0.028152837942493857,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\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\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\"\
: {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n\
\ \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n\
\ \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.4019607843137255,\n\
\ \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\
\ 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.03202563076101735\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.04685473041907789,\n\
\ \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\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\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7483870967741936,\n\
\ \"acc_stderr\": 0.024685979286239963,\n \"acc_norm\": 0.7483870967741936,\n\
\ \"acc_norm_stderr\": 0.024685979286239963\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\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\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\": 0.022935144053919436,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919436\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \
\ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.03077805742293167,\n \
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.03077805742293167\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\"\
: 0.8110091743119267,\n \"acc_stderr\": 0.016785481159203627,\n \"\
acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.016785481159203627\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4583333333333333,\n \"acc_stderr\": 0.033981108902946366,\n \"\
acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.033981108902946366\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069432,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069432\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\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\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.023086635086841403\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8186462324393359,\n\
\ \"acc_stderr\": 0.013778693778464076,\n \"acc_norm\": 0.8186462324393359,\n\
\ \"acc_norm_stderr\": 0.013778693778464076\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\": 0.311731843575419,\n\
\ \"acc_stderr\": 0.015491756531894638,\n \"acc_norm\": 0.311731843575419,\n\
\ \"acc_norm_stderr\": 0.015491756531894638\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.02567025924218893,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.02567025924218893\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829714,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829714\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44132985658409385,\n\
\ \"acc_stderr\": 0.012682016335646671,\n \"acc_norm\": 0.44132985658409385,\n\
\ \"acc_norm_stderr\": 0.012682016335646671\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.02928941340940319,\n\
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.02928941340940319\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724556,\n \
\ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724556\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454132,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454132\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31456548347613217,\n\
\ \"mc1_stderr\": 0.01625524199317918,\n \"mc2\": 0.4582610592701175,\n\
\ \"mc2_stderr\": 0.014684929022027894\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7940015785319653,\n \"acc_stderr\": 0.011366474352008828\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.39272175890826383,\n \
\ \"acc_stderr\": 0.013451745349586576\n }\n}\n```"
repo_url: https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-7
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|arc:challenge|25_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|gsm8k|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hellaswag|10_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T02-59-07.184028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T02-59-07.184028.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- '**/details_harness|winogrande|5_2024-03-11T02-59-07.184028.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-11T02-59-07.184028.parquet'
- config_name: results
data_files:
- split: 2024_03_11T02_59_07.184028
path:
- results_2024-03-11T02-59-07.184028.parquet
- split: latest
path:
- results_2024-03-11T02-59-07.184028.parquet
---
# Dataset Card for Evaluation run of juhwanlee/llmdo-Mistral-7B-case-7
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [juhwanlee/llmdo-Mistral-7B-case-7](https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-7) 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_juhwanlee__llmdo-Mistral-7B-case-7",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-11T02:59:07.184028](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-7/blob/main/results_2024-03-11T02-59-07.184028.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": 0.6293874204442154,
"acc_stderr": 0.03245765893138893,
"acc_norm": 0.6345328719122807,
"acc_norm_stderr": 0.03311526736789786,
"mc1": 0.31456548347613217,
"mc1_stderr": 0.01625524199317918,
"mc2": 0.4582610592701175,
"mc2_stderr": 0.014684929022027894
},
"harness|arc:challenge|25": {
"acc": 0.5878839590443686,
"acc_stderr": 0.014383915302225403,
"acc_norm": 0.6194539249146758,
"acc_norm_stderr": 0.014188277712349812
},
"harness|hellaswag|10": {
"acc": 0.6354311890061741,
"acc_stderr": 0.004803253812881043,
"acc_norm": 0.8353913563035252,
"acc_norm_stderr": 0.003700690995600888
},
"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": 0.042320736951515885,
"acc_norm": 0.6,
"acc_norm_stderr": 0.042320736951515885
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.038234289699266046,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.038234289699266046
},
"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": 0.028152837942493857,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493857
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"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": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.036430371689585475,
"acc_norm": 0.6473988439306358,
"acc_norm_stderr": 0.036430371689585475
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6,
"acc_stderr": 0.03202563076101735,
"acc_norm": 0.6,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"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": 0.4074074074074074,
"acc_stderr": 0.02530590624159063,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.02530590624159063
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.043758884927270605,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.043758884927270605
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[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. -->
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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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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Citation [optional]
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pbwinter/masked_chunks_hindi_wiki | ---
dataset_info:
features:
- name: labels
dtype: string
- name: mask_text
dtype: string
splits:
- name: train
num_bytes: 1280772471
num_examples: 1168190
download_size: 498464131
dataset_size: 1280772471
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Thundergb/Musculosobr | ---
license: openrail
---
|
CyberHarem/yagokoro_eirin_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yagokoro_eirin/八意永琳/야고코로에이린 (Touhou)
This is the dataset of yagokoro_eirin/八意永琳/야고코로에이린 (Touhou), containing 500 images and their tags.
The core tags of this character are `long_hair, hat, nurse_cap, braid, grey_hair, breasts, very_long_hair, blue_eyes, single_braid, white_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 580.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yagokoro_eirin_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 362.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yagokoro_eirin_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1058 | 694.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yagokoro_eirin_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 524.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yagokoro_eirin_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1058 | 936.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yagokoro_eirin_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yagokoro_eirin_touhou',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, arrow_(projectile), bow_(weapon), solo, smile |
| 1 | 8 |  |  |  |  |  | 1girl, bow_(weapon), full_moon, solo, arrow_(projectile) |
| 2 | 10 |  |  |  |  |  | 1girl, smile, solo |
| 3 | 14 |  |  |  |  |  | 1girl, bangs, multicolored_dress, puffy_short_sleeves, red_cross, solo, holding_bow_(weapon), looking_at_viewer, blue_dress, constellation_print, closed_mouth, holding_arrow, smile, blue_headwear, trigram, frilled_dress, black_footwear, full_body, standing, two-tone_dress, blush, simple_background, white_background, yellow_eyes |
| 4 | 11 |  |  |  |  |  | 1girl, closed_mouth, looking_at_viewer, simple_background, solo, blue_headwear, multicolored_dress, upper_body, white_background, blue_dress, puffy_short_sleeves, red_cross, smile, parted_bangs, blush, constellation_print, holding |
| 5 | 6 |  |  |  |  |  | 1girl, blush, nipples, nude, solo, purple_eyes, blue_hair, pussy, spread_legs, sweat |
| 6 | 8 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, blush, huge_breasts, nipples, penis, paizuri, nude, sweat, blue_hair, cum_on_breasts, purple_eyes, ejaculation, fellatio, tongue_out |
| 7 | 15 |  |  |  |  |  | 2girls, smile, black_hair, closed_eyes, blush |
| 8 | 5 |  |  |  |  |  | 1girl, blush, nipples, solo, looking_at_viewer, on_back, barefoot, bed_sheet, no_bra, off_shoulder, on_side, pillow, smile, ass, bare_legs, bare_shoulders, black_panties, dakimakura_(medium), hair_bow, navel, on_bed, open_dress, sweat, thighs, torn_clothes, white_panties |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | arrow_(projectile) | bow_(weapon) | solo | smile | full_moon | bangs | multicolored_dress | puffy_short_sleeves | red_cross | holding_bow_(weapon) | looking_at_viewer | blue_dress | constellation_print | closed_mouth | holding_arrow | blue_headwear | trigram | frilled_dress | black_footwear | full_body | standing | two-tone_dress | blush | simple_background | white_background | yellow_eyes | upper_body | parted_bangs | holding | nipples | nude | purple_eyes | blue_hair | pussy | spread_legs | sweat | 1boy | hetero | solo_focus | huge_breasts | penis | paizuri | cum_on_breasts | ejaculation | fellatio | tongue_out | 2girls | black_hair | closed_eyes | on_back | barefoot | bed_sheet | no_bra | off_shoulder | on_side | pillow | ass | bare_legs | bare_shoulders | black_panties | dakimakura_(medium) | hair_bow | navel | on_bed | open_dress | thighs | torn_clothes | white_panties |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:---------------|:-------|:--------|:------------|:--------|:---------------------|:----------------------|:------------|:-----------------------|:--------------------|:-------------|:----------------------|:---------------|:----------------|:----------------|:----------|:----------------|:-----------------|:------------|:-----------|:-----------------|:--------|:--------------------|:-------------------|:--------------|:-------------|:---------------|:----------|:----------|:-------|:--------------|:------------|:--------|:--------------|:--------|:-------|:---------|:-------------|:---------------|:--------|:----------|:-----------------|:--------------|:-----------|:-------------|:---------|:-------------|:--------------|:----------|:-----------|:------------|:---------|:---------------|:----------|:---------|:------|:------------|:-----------------|:----------------|:----------------------|:-----------|:--------|:---------|:-------------|:---------|:---------------|:----------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | | X | X | | | X | X | X | | X | X | X | X | | X | | | | | | | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 15 |  |  |  |  |  | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | X | | | | | | | | | | | | X | | | | | | | X | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
oliverbob/asvchapters | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_sst2_he_inanimate_objects | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 12018
num_examples: 78
- name: test
num_bytes: 28569
num_examples: 183
- name: train
num_bytes: 228658
num_examples: 1665
download_size: 146197
dataset_size: 269245
---
# Dataset Card for "MULTI_VALUE_sst2_he_inanimate_objects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
marup/SabrinaCarpenterRVC200Epochs | ---
license: openrail
---
|
DopeorNope/2000sample_COT | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: rationale
dtype: string
- name: task
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 2298020
num_examples: 2159
download_size: 1099835
dataset_size: 2298020
license: cc-by-nc-sa-4.0
---
# Dataset Card for "2000sample_COT"
# DopeorNope/Eng_Kor_COT_combined
- KOpen-platypus + DopeorNope/2000sample_COT
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
- 고품질 한국어 데이터셋 + COT 방식으로 구성한 영어+ 한국어 dataset구성
---
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atom92/all_medical | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 44683704
num_examples: 52105
download_size: 19461357
dataset_size: 44683704
---
# Dataset Card for "all_medical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nolan1206/Nolan_whisper_educate | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: test
num_bytes: 82981.0
num_examples: 3
- name: train
num_bytes: 82981.0
num_examples: 3
download_size: 169572
dataset_size: 165962.0
---
# Dataset Card for "Nolan_whisper_educate"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ahmedgongi/version_09_04 | ---
license: apache-2.0
---
|
joey234/mmlu-logical_fallacies-rule-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 103626
num_examples: 163
download_size: 46095
dataset_size: 103626
---
# Dataset Card for "mmlu-logical_fallacies-rule-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kaleemWaheed/twitter_dataset_1713181316 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 29630
num_examples: 68
download_size: 15017
dataset_size: 29630
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
PaulLerner/oscar_inclure | ---
license: cc0-1.0
task_categories:
- translation
language:
- fr
pretty_name: Inclure
size_categories:
- 10K<n<100K
---
Original dataset: https://huggingface.co/datasets/oscar-corpus/OSCAR-2201 |
ummagumm-a/samolet_frames | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': 2023-02-14 15-03-46
'3': 2023-04-19 09-56-23
'4': 2023-05-10 14-44-10
'5': 3_Trim
'6': '4'
'7': '5'
'8': '6'
'9': '7'
'10': IMG_9920
'11': IMG_9921
'12': IMG_9922
'13': IMG_9923
'14': IMG_9924
splits:
- name: train
num_bytes: 334183336.777
num_examples: 6541
- name: test
num_bytes: 68858108.0
num_examples: 915
download_size: 456039861
dataset_size: 403041444.777
---
# Dataset Card for "samolet_frames"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chengli-thu/yuebuqun | ---
license: cc-by-4.0
---
支持ChatHaruhi2 的 岳不群 数据,可以使用如下方式调用
```python
from chatharuhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'chengli-thu/yuebuqun', \
llm = 'openai')
response = chatbot.chat(role='令狐冲', text = '师父,我来了')
print(response)
```
上传者: 李鲁鲁
更具体的信息,见 [ChatHaruhi](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
欢迎加入我们的 [众筹角色创建项目](https://github.com/LC1332/Chat-Haruhi-Suzumiya/tree/main/characters/novel_collecting)
### Citation引用
Please cite the repo if you use the data or code in this repo.
```
@misc{li2023chatharuhi,
title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model},
author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
year={2023},
eprint={2308.09597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bias-amplified-splits/anli | ---
license: cc-by-nc-4.0
dataset_info:
- config_name: minority_examples
features:
- name: round
dtype: string
- name: uid
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
splits:
- name: train.biased
num_bytes: 61260115
num_examples: 134068
- name: train.anti_biased
num_bytes: 13246263
num_examples: 28797
- name: validation.biased
num_bytes: 1311433
num_examples: 2317
- name: validation.anti_biased
num_bytes: 500409
num_examples: 883
- name: test.biased
num_bytes: 1284544
num_examples: 2262
- name: test.anti_biased
num_bytes: 539798
num_examples: 938
download_size: 86373189
dataset_size: 78142562
- config_name: partial_input
features:
- name: round
dtype: string
- name: uid
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
splits:
- name: train.biased
num_bytes: 60769911
num_examples: 134068
- name: train.anti_biased
num_bytes: 13736467
num_examples: 28797
- name: validation.biased
num_bytes: 1491254
num_examples: 2634
- name: validation.anti_biased
num_bytes: 320588
num_examples: 566
- name: test.biased
num_bytes: 1501586
num_examples: 2634
- name: test.anti_biased
num_bytes: 322756
num_examples: 566
download_size: 86373189
dataset_size: 78142562
task_categories:
- text-classification
language:
- en
pretty_name: Adversarial NLI
size_categories:
- 100K<n<1M
---
# Dataset Card for Bias-amplified Splits for Adversarial NLI
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Fighting Bias with Bias repo](https://github.com/schwartz-lab-nlp/fight-bias-with-bias)
- **Paper:** [arXiv](https://arxiv.org/abs/2305.18917)
- **Point of Contact:** [Yuval Reif](mailto:yuval.reif@mail.huji.ac.il)
- **Original Dataset's Paper:** [ANLI](https://arxiv.org/abs/1910.14599)
### Dataset Summary
Bias-amplified splits is a novel evaluation framework to assess model robustness, by amplifying dataset biases in the training data and challenging models to generalize beyond them. This framework is defined by a bias-amplified training set and a hard, anti-biased test set, which we automatically extract from existing datasets using model-based methods.
Our experiments show that the identified anti-biased examples are naturally challenging for models, and moreover, models trained on bias-amplified data exhibit dramatic performance drops on anti-biased examples, which are not mitigated by common approaches to improve generalization.
Here we apply our framework to Adversarial Natural Language Inference (ANLI), a large-scale NLI benchmark dataset. The dataset was collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI.
Our evaluation framework can be applied to any existing dataset, even those considered obsolete, to test model robustness. We hope our work will guide the development of robust models that do not rely on superficial biases and correlations.
#### Evaluation Results (DeBERTa-large)
##### For splits based on minority examples:
| Training Data \ Test Data | Original test | Anti-biased test |
|---------------------------|---------------|------------------|
| Original training split | 67.5 | 58.3 |
| Biased training split | 60.6 | 21.4 |
##### For splits based on partial-input model:
| Training Data \ Test Data | Original test | Anti-biased test |
|---------------------------|---------------|------------------|
| Original training split | 67.5 | 50.0 |
| Biased training split | 62.5 | 28.3 |
#### Loading the Data
ANLI contains three rounds of data collection, and each round has train/dev/test splits. We concatenated the splits from all rounds to create one train/dev/test splits.
```
from datasets import load_dataset
# choose which bias detection method to use for the bias-amplified splits: either "minority_examples" or "partial_input"
dataset = load_dataset("bias-amplified-splits/anli", "minority_examples")
# use the biased training split and anti-biased test split
train_dataset = dataset['train.biased']
eval_dataset = dataset['validation.anti_biased']
```
## Dataset Structure
### Data Instances
Data instances are taken directly from ANLI, and re-split into biased and anti-biased subsets. Here is an example of an instance from the dataset:
```
{
"round": "r1",
"idx": "20a331ee-cf54-4e8a-9ff9-6152cd679780",
"premise": "Milton Teagle "Richard" Simmons (born July 12, 1948) is an American fitness guru, actor, and comedian. He promotes weight-loss programs, prominently through his "Sweatin' to the Oldies" line of aerobics videos and is known for his eccentric, flamboyant, and energetic personality.",
"hypothesis": "Milton Teagle "Richard" Simmons created his "Sweatin' to the Oldies" line of aerobics videos without help or input from anyone else.",
"label": 1,
"reason": "The context gives no information as to how the "Sweatin' to the Oldies" videos are produced, Simmons may well produce them alone, or may produce them with a team. The system may have had difficulty with this because it is unlikely that Simmons produced the videos alone."
}
```
### Data Fields
- `round`: which round of data collection the example comes from (one of `r1`, `r2` and `r3`)
- `uid`: unique identifier for the example.
- `premise`: a piece of text
- `hypothesis`: a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- `label`: one of `0`, `1` and `2` (`entailment`, `neutral`, and `contradiction`)
- `reason`: explanation why the label is true (only for some examples).
### Data Splits
Bias-amplified splits require a method to detect *biased* and *anti-biased* examples in datasets. We release bias-amplified splits based created with each of these two methods:
- **Minority examples**: A novel method we introduce that leverages representation learning and clustering for identifying anti-biased *minority examples* (Tu et al., 2020)—examples that defy common statistical patterns found in the rest of the dataset.
- **Partial-input baselines**: A common method for identifying biased examples containing annotation artifacts in a dataset, which examines the performance of models that are restricted to using only part of the input. Such models, if successful, are bound to rely on unintended or spurious patterns in the dataset.
Using each of the two methods, we split each of the original train and test splits into biased and anti-biased subsets. See the [paper](https://arxiv.org/abs/2305.18917) for more details.
#### Minority Examples
| Dataset Split | Number of Instances in Split |
|--------------------------|------------------------------|
| Train - biased | 134068 |
| Train - anti-biased | 28797 |
| Validation - biased | 2317 |
| Validation - anti-biased | 883 |
| Test - biased | 2262 |
| Test - anti-biased | 938 |
#### Partial-input Baselines
| Dataset Split | Number of Instances in Split |
|--------------------------|------------------------------|
| Train - biased | 134068 |
| Train - anti-biased | 28797 |
| Validation - biased | 2634 |
| Validation - anti-biased | 566 |
| Test - biased | 2634 |
| Test - anti-biased | 566 |
## Dataset Creation
### Curation Rationale
NLP models often rely on superficial cues known as *dataset biases* to achieve impressive performance, and can fail on examples where these biases do not hold. To develop more robust, unbiased models, recent work aims to filter bisased examples from training sets. We argue that in order to encourage the development of robust models, we should in fact **amplify** biases in the training sets, while adopting the challenge set approach and making test sets anti-biased. To implement our approach, we introduce a simple framework that can be applied automatically to any existing dataset to use it for testing model robustness.
### Annotations
#### Annotation process
No new annotations are required to create bias-amplified splits. Existing data instances are split into *biased* and *anti-biased* splits based on automatic model-based methods to detect such examples.
## Considerations for Using the Data
### Social Impact of Dataset
Bias-amplified splits were created to promote the development of robust NLP models that do not rely on superficial biases and correlations, and provide more challenging evaluation of existing systems.
### Discussion of Biases
We propose to use bias-amplified splits to complement benchmarks with challenging evaluation settings that test model robustness, in addition to the dataset’s main training and test sets. As such, while existing dataset biases are *amplified* during training with bias-amplified splits, these splits are intended primarily for model evaluation, to expose the bias-exploiting behaviors of models and to identify more robsut models and effective robustness interventions.
## Additional Information
### Dataset Curators
Bias-amplified splits were introduced by Yuval Reif and Roy Schwartz from the [Hebrew University of Jerusalem](https://schwartz-lab-huji.github.io).
ANLI was developed by Adina Williams, Tristan Thrush and Douwe Kiela.
### Citation Information
```
@misc{reif2023fighting,
title = "Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases",
author = "Yuval Reif and Roy Schwartz",
month = may,
year = "2023",
url = "https://arxiv.org/pdf/2305.18917",
}
```
Source dataset:
```
@article{williams-etal-2020-anlizing,
title = "ANLIzing the Adversarial Natural Language Inference Dataset",
author = "Adina Williams and
Tristan Thrush and
Douwe Kiela",
booktitle = "Proceedings of the 5th Annual Meeting of the Society for Computation in Linguistics",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
murukkuu/guanaco-llama2-200 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 338808
num_examples: 200
download_size: 201257
dataset_size: 338808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PavelDanek/autotrain-data-skill2go_summ_mbart | ---
task_categories:
- summarization
---
# AutoTrain Dataset for project: skill2go_summ_mbart
## Dataset Description
This dataset has been automatically processed by AutoTrain for project skill2go_summ_mbart.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_Unnamed: 0": 1258,
"text": "<p>\u0414\u0430\u043d\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0438\u0435\u0439 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u041c\u0413\u0423 \u0418\u0421\u0410\u0410 \u0434\u043b\u044f \u0434\u0438\u0441\u0442\u0430\u043d\u0446\u0438\u043e\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u0412\u0441\u0435 \u043c\u0435\u0442\u043e\u0434\u0438\u043a\u0438, \u043f\u043e\u0434\u0445\u043e\u0434 \u043a \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044e, \u0443\u043f\u0440\u0430\u0436\u043d\u0435\u043d\u0438\u044f, \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b, \u0430\u0443\u0434\u0438\u043e \u0438 \u0432\u0438\u0434\u0435\u043e\u0444\u0430\u0439\u043b\u044b \u043f\u043e\u0434\u0447\u0438\u043d\u0435\u043d\u044b \u043e\u0434\u043d\u043e\u0439 \u0446\u0435\u043b\u0438 - \u0432\u043e\u0441\u043f\u0438\u0442\u0430\u043d\u0438\u044e \u0432\u044b\u0441\u043e\u043a\u043e\u043a\u043b\u0430\u0441\u0441\u043d\u044b\u0445 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u043e\u0432 \u0432 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430. \u041d\u0430 \u043f\u0440\u043e\u0442\u044f\u0436\u0435\u043d\u0438\u0438 \u0431\u043e\u043b\u0435\u0435 \u0447\u0435\u043c 70 \u043b\u0435\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 \u043a\u0438\u0442\u0430\u0438\u0441\u0442\u043e\u0432 \u0432 \u041c\u0413\u0423 \u0418\u0421\u0410\u0410 \u043d\u0435\u043f\u0440\u0435\u0440\u044b\u0432\u043d\u043e \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0441\u0442\u0432\u0443\u0435\u0442\u0441\u044f \u0438 \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u043e \u0434\u0430\u0435\u0442 \u043e\u0442\u043b\u0438\u0447\u043d\u044b\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b. \u0412\u043f\u0435\u0440\u0432\u044b\u0435 \u0434\u0430\u043d\u043d\u0430\u044f \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430, \u0440\u0430\u043d\u0435\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430\u044f \u043b\u0438\u0448\u044c 20-30 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u043c \u0432 \u0433\u043e\u0434\u0443, \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430 \u0434\u043b\u044f \u0448\u0438\u0440\u043e\u043a\u0438\u0445 \u043c\u0430\u0441\u0441, \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0443\u044e\u0449\u0438\u0445\u0441\u044f \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u0438\u043c \u044f\u0437\u044b\u043a\u043e\u043c. </p><p><br></p><p>\u042d\u0442\u043e\u0442 \u043a\u0443\u0440\u0441 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u0435\u0440\u0432\u043e\u0439 \u0441\u0442\u0443\u043f\u0435\u043d\u044c\u044e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 \u043a\u0438\u0442\u0430\u0438\u0441\u0442\u043e\u0432. \u041f\u043e\u0441\u043b\u0435 \u0443\u0441\u043f\u0435\u0448\u043d\u043e\u0433\u043e \u043e\u0441\u0432\u043e\u0435\u043d\u0438\u044f \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u0430 \u0432\u044b \u0431\u0443\u0434\u0435\u0442\u0435 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u044b \u0441 \u043b\u0435\u0433\u043a\u043e\u0441\u0442\u044c\u044e \u0441\u0434\u0430\u0442\u044c \u044d\u043a\u0437\u0430\u043c\u0435\u043d 1HSK. \u0412\u044b \u0441\u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0444\u043e\u043d\u0435\u0442\u0438\u043a\u0443 \u0438 \u0431\u0443\u0434\u0435\u0442\u0435 \u0437\u0432\u0443\u0447\u0430\u0442\u044c \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043a\u0430\u043a \u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044c \u044f\u0437\u044b\u043a\u0430. \u0412\u044b \u043e\u0441\u0432\u043e\u0438\u0442\u0435 \u0431\u0430\u0437\u043e\u0432\u0443\u044e \u0438\u0435\u0440\u043e\u0433\u043b\u0438\u0444\u0438\u043a\u0443, \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 \u0437\u043d\u0430\u043d\u0438\u044f \u043f\u043e \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u0433\u0440\u0430\u043c\u043c\u0430\u0442\u0438\u043a\u0435 \u0438 \u0441 \u043b\u0435\u0433\u043a\u043e\u0441\u0442\u044c\u044e \u043d\u0430\u0447\u043d\u0435\u0442\u0435 \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044f\u043c\u0438. </p><p><br></p><p>\u041d\u0435 \u0442\u0440\u0435\u0431\u0443\u0435\u0442 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0433\u043e \u043e\u043f\u044b\u0442\u0430 \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u044f \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430. \u041f\u043e\u0434\u0445\u043e\u0434\u0438\u0442 \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u0432\u043e\u0437\u0440\u0430\u0441\u0442\u043e\u0432.</p>",
"target": "\u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430 \u0434\u043b\u044f \u0432\u0441\u0435\u0445"
},
{
"feat_Unnamed: 0": 598,
"text": "<p>\u041a\u0443\u0440\u0441 \u043d\u0430\u0446\u0435\u043b\u0435\u043d \u043d\u0430 \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u0435 \u0440\u0435\u0436\u0438\u0441\u0441\u0443\u0440\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u0441 \u043d\u0443\u043b\u044f \u0438 \u0432 \u043f\u043e\u043b\u043d\u043e\u043c \u043e\u0431\u044a\u0435\u043c\u0435 \u0440\u0430\u0441\u043a\u0440\u044b\u0432\u0430\u0435\u0442 \u0432\u0435\u0441\u044c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. \u0414\u043e\u043b\u0433\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u044d\u0442\u043e\u0442 \u043a\u0443\u0440\u0441 \u0431\u044b\u043b \u043e\u0434\u0438\u043d \u0438\u0437 \u0441\u0430\u043c\u044b\u0445 \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u044b\u0445 \u0438 \u0433\u043b\u0443\u0431\u043e\u043a\u0438\u0445 \u0432\u043e \u0432\u0441\u0435\u043c \u0440\u0443\u0441\u0441\u043a\u043e\u044f\u0437\u044b\u0447\u043d\u043e\u043c \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u0435. \u0421\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0430 \u043d\u0435\u043c \u0431\u044b\u043b\u043e 800$. \u0421\u0435\u0439\u0447\u0430\u0441 \u043c\u044b \u0434\u0435\u043b\u0438\u043c\u0441\u044f \u0438\u043c \u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u043e. </p><p><strong>\u041e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u0431\u043b\u043e\u043a\u0438 \u043a\u0443\u0440\u0441\u0430:</strong></p><ol><li><p>\u041e\u0431\u0437\u043e\u0440 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u0430</p></li><li><p>\u041d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0431\u043e\u0442\u044b. \u041e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441\u043e \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c\u044e, \u0410\u0442\u0440\u0438\u0431\u0443\u0442\u0430\u043c\u0438 \u0438 \u0410\u043d\u0438\u043c\u0430\u0446\u0438\u044f</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441\u043e \u0437\u0432\u0443\u043a\u043e\u043c</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0439</p></li><li><p>\u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u044d\u0444\u0444\u0435\u043a\u0442\u043e\u0432 \u0438 \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u043e\u0432. \u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0445\u0440\u043e\u043c\u0430\u043a\u0435\u0435\u043c</p></li><li><p>\u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0438 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0442\u0435\u043a\u0441\u0442\u0430</p></li><li><p>\u0418\u043d\u0434\u0436\u0435\u0441\u0442 \u0438 \u043c\u043d\u043e\u0433\u043e\u043a\u0430\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u043e\u043d\u0442\u0430\u0436</p></li><li><p>\u0426\u0432\u0435\u0442\u043e\u043a\u043e\u0440\u0440\u0435\u043a\u0446\u0438\u044f</p></li><li><p>\u0424\u0438\u043d\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u041a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0438\u044f</p><p><br></p></li></ol><p>Final Cut Pro 7 \u0434\u043e\u043b\u0433\u043e \u0432\u0440\u0435\u043c\u044f \u0431\u044b\u043b \u043b\u0438\u0434\u0435\u0440\u043e\u043c \u0441\u0440\u0435\u0434\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c \u0434\u043b\u044f \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u0432 \u0413\u043e\u043b\u043b\u0438\u0432\u0443\u0434\u0435 \u0438 \u0415\u0432\u0440\u043e\u043f\u0435. \u0421\u0432\u044f\u0437\u0430\u043d\u043e \u044d\u0442\u043e \u0441 \u0435\u0433\u043e \u0448\u0438\u0440\u043e\u043a\u0438\u043c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0435\u043c \u0438 \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u043e\u043c \u0440\u0430\u0431\u043e\u0442\u044b. </p><p>\u041e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u044d\u0442\u043e\u0439 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0431\u044b\u043b\u0430 \u043f\u0440\u0435\u043a\u0440\u0430\u0449\u0435\u043d\u0430 \u0432 2011 \u0433\u043e\u0434\u0443. \u0421\u0442\u0430\u0440\u043e\u0435 \u043f\u043e\u043a\u043e\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u043b \u043d\u043e\u0432\u044b\u0439 Final Cut Pro X. \u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u0434\u0430\u0436\u0435 \u0441\u043f\u0443\u0441\u0442\u044f \u0431\u043e\u043b\u0435\u0435 10 \u043b\u0435\u0442, \u043c\u043d\u043e\u0433\u0438\u0435 \u0440\u0435\u0436\u0438\u0441\u0441\u0435\u0440\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u043d\u0430 \u0442\u0435\u043b\u0435\u043a\u0430\u043d\u0430\u043b\u0430\u0445 \u0438 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u0435\u043d\u0430\u0445 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0430\u044e\u0442 \u0440\u0430\u0431\u043e\u0442\u0443 \u0432 \u0441\u0442\u0430\u0440\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. \u0418\u0437\u0443\u0447\u0438\u0432 \u044d\u0442\u0443 \u0432\u0435\u0440\u0441\u0438\u044e \u0432\u044b \u043f\u043e\u0439\u043c\u0435\u0442\u0435 \u043a\u0430\u043a \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u043c\u043e\u043d\u0442\u0430\u0436 \u0438 \u043d\u0430\u0443\u0447\u0438\u0442\u0435\u0441\u044c \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438 \u0442\u0435\u0445\u043d\u0438\u043a\u0430\u043c\u0438 \u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438 \u043c\u043e\u043d\u0442\u0430\u0436\u0430.</p><p>\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0435 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0434\u0435\u043c\u043e\u043a\u0440\u0430\u0442\u0438\u0447\u043d\u044b. \u0414\u043b\u044f \u0437\u0430\u043f\u0443\u0441\u043a\u0430 Final Cut Pro 7 \u043f\u043e\u0434\u043e\u0439\u0434\u0435\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0441 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u043c Core 2 Duo, 2 \u0413\u0411 \u043e\u043f\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u043e\u0439 \u043f\u0430\u043c\u044f\u0442\u0438, \u0438 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442\u0430 \u0443\u0440\u043e\u0432\u043d\u044f Intel HD Graphics 3000. \u041d\u043e \u0432\u0435\u0440\u0441\u0438\u044f \u041c\u0430\u0441\u041e\u0421 \u0434\u043e\u043b\u0436\u043d\u0430 \u0431\u044b\u0442\u044c \u043d\u0435 \u043d\u043e\u0432\u0435\u0435 10.11 (High Sierra). \u0423\u0447\u0442\u0438\u0442\u0435 \u0442\u0430\u043a\u0436\u0435, \u0447\u0442\u043e \u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0435 \u043d\u0435 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0430 \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u0441\u0442\u0430\u0440\u0430\u044f \u0438 \u043d\u043e\u0432\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. </p><p> </p><p><br></p><p>\u0416\u0435\u043b\u0430\u0435\u043c \u043f\u0440\u0438\u044f\u0442\u043d\u043e\u0433\u043e \u0438 \u043f\u043b\u043e\u0434\u043e\u0442\u0432\u043e\u0440\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f!</p>",
"target": "\u041f\u043e\u043b\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \u043f\u043e \u0440\u0435\u0436\u0438\u0441\u0441\u0443\u0440\u0435 \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u0432 Final Cut Pro 7 \u043e\u0442 Apple Certified Pro"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1830 |
| valid | 458 |
|
katarinagresova/Genomic_Benchmarks_human_enhancers_cohn | ---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 10671616
num_examples: 20843
- name: test
num_bytes: 3557376
num_examples: 6948
download_size: 1662449
dataset_size: 14228992
---
# Dataset Card for "Genomic_Benchmarks_human_enhancers_cohn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CreatlV/CoVa-coco-v2-fold2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: height
dtype: int64
- name: width
dtype: int64
- name: objects
struct:
- name: bbox
sequence:
sequence: float64
- name: categories
sequence: int64
- name: annotations
list:
- name: id
dtype: int64
- name: image_id
dtype: string
- name: category_id
dtype: int64
- name: segmentation
sequence: 'null'
- name: area
dtype: float64
- name: bbox
sequence: float64
- name: iscrowd
dtype: int64
- name: attributes
struct:
- name: occluded
dtype: bool
- name: rotation
dtype: float64
- name: pixel_values
dtype: image
splits:
- name: train
num_bytes: 4632653914.321059
num_examples: 4969
- name: val
num_bytes: 1322017156.4715762
num_examples: 1418
- name: test
num_bytes: 1261416934.2073643
num_examples: 1353
download_size: 7207373107
dataset_size: 7216088004.999999
---
# Dataset Card for "CoVa-coco-v2-fold2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PNLPhub/FarsTail | ---
license: apache-2.0
task_categories:
- text-classification
language:
- fa
size_categories:
- 1K<n<10K
---
## Dataset Description
- **Repository:https://github.com/dml-qom/FarsTail**
- **Paper:https://arxiv.org/abs/2009.08820**
### Dataset Summary
Persian (Farsi) language is a pluricentric language spoken by around 110 million people in countries like Iran, Afghanistan, and Tajikistan. Here, we present the first relatively large-scale Persian dataset for NLI task, called FarsTail. A total of 10,367 samples are generated from a collection of 3,539 multiple-choice questions. The train, validation, and test portions include 7,266, 1,537, and 1,564 instances, respectively
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{amirkhani2020farstail,
title={FarsTail: A Persian Natural Language Inference Dataset},
author={Hossein Amirkhani, Mohammad Azari Jafari, Azadeh Amirak, Zohreh Pourjafari, Soroush Faridan Jahromi, and Zeinab Kouhkan},
journal={arXiv preprint arXiv:2009.08820},
year={2020}
}
``` |
jiuyuan/policy_AI | ---
license: apache-2.0
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 47309
num_examples: 75
- name: test
num_bytes: 7510
num_examples: 19
download_size: 42243
dataset_size: 54819
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
jonathang/dreambooth-hackathon-images-protein3 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 2000745.0
num_examples: 11
download_size: 1946505
dataset_size: 2000745.0
---
# Dataset Card for "dreambooth-hackathon-images-protein3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vwxyzjn/test-dataset-bug3 | ---
dataset_info:
features:
- name: data
sequence: int64
splits:
- name: train
num_bytes: 40
num_examples: 2
- name: remove_CritiqueRequest_10_18_2023_1697667530
num_bytes: 40
num_examples: 2
download_size: 2130
dataset_size: 80
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: remove_CritiqueRequest_10_18_2023_1697667530
path: data/remove_CritiqueRequest_10_18_2023_1697667530-*
---
# Dataset Card for "test-dataset-bug3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lorinma/EvolInstruct_zh_COIG-PC_Deepseek | ---
license: mit
language:
- zh
---
中文世界欠缺类似FLAN和Orca的工作,COIG-PC又称自己包括了很多中文NLP任务,可以和FLAN类比。既然我之前做了一些Evol-Instruction的工作,那么我很好奇结合COIG-PC和EvolInstruction会有什么火花。
因为OpenAI接口太贵,使用了DeepSeek赠送的1000万token,这次生成了5000条,差不多用了600万token。
一共有3个文件:
0227_COIG_seed_tasks_CoarseFilter_1106_adjusted.json 是使用的COIG-pc-Lite,每一个task抽一条出来,并且过滤掉多语言的任务(只保留中英文),形成种子任务1106条.
evolve_chinese.py 基于H2O EvolInstruction的代码。
0227_EvolInstruct-COIG 生成的6680条数据。 |
hojzas/proj8-lab2 | ---
license: apache-2.0
---
|
BeIR/nq-qrels | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
AlekseyKorshuk/evol-codealpaca-dpo | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: chosen_response
dtype: string
- name: rejected_response
dtype: string
splits:
- name: train
num_bytes: 74894606
num_examples: 21582
download_size: 41316834
dataset_size: 74894606
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Hack90/ncbi_genbank_part_49 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 33647454754
num_examples: 87952
download_size: 15354172665
dataset_size: 33647454754
---
# Dataset Card for "ncbi_genbank_part_49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_100000_Higgs_sgosdt_l256_dim10_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 2364400000
num_examples: 100000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 1584979274
dataset_size: 2600840000
---
# Dataset Card for "autotree_automl_100000_Higgs_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_s3nh__poorx32124 | ---
pretty_name: Evaluation run of s3nh/poorx32124
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [s3nh/poorx32124](https://huggingface.co/s3nh/poorx32124) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_s3nh__poorx32124\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-13T14:37:56.380929](https://huggingface.co/datasets/open-llm-leaderboard/details_s3nh__poorx32124/blob/main/results_2024-02-13T14-37-56.380929.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\": 0.526388001251259,\n\
\ \"acc_stderr\": 0.03423883931564769,\n \"acc_norm\": 0.532328851935962,\n\
\ \"acc_norm_stderr\": 0.03497682392887963,\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5026378052318262,\n\
\ \"mc2_stderr\": 0.015007516516438155\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5017064846416383,\n \"acc_stderr\": 0.01461130570505699,\n\
\ \"acc_norm\": 0.5315699658703071,\n \"acc_norm_stderr\": 0.014582236460866977\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5550687114120693,\n\
\ \"acc_stderr\": 0.004959425421382025,\n \"acc_norm\": 0.7358095996813384,\n\
\ \"acc_norm_stderr\": 0.00440000082274205\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421296,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421296\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\": 0.5526315789473685,\n \"acc_stderr\": 0.04046336883978252,\n\
\ \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.04046336883978252\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791197,\n\
\ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791197\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.5694444444444444,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\
\ \"acc_stderr\": 0.03809342081273958,\n \"acc_norm\": 0.4797687861271676,\n\
\ \"acc_norm_stderr\": 0.03809342081273958\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.451063829787234,\n \"acc_stderr\": 0.03252909619613197,\n\
\ \"acc_norm\": 0.451063829787234,\n \"acc_norm_stderr\": 0.03252909619613197\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.041657747757287644,\n\
\ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.041657747757287644\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36243386243386244,\n \"acc_stderr\": 0.024757473902752052,\n \"\
acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.024757473902752052\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04285714285714281\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\": 0.6096774193548387,\n\
\ \"acc_stderr\": 0.027751256636969576,\n \"acc_norm\": 0.6096774193548387,\n\
\ \"acc_norm_stderr\": 0.027751256636969576\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.03481904844438804,\n\
\ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.03481904844438804\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.6303030303030303,\n \"acc_stderr\": 0.03769430314512567,\n\
\ \"acc_norm\": 0.6303030303030303,\n \"acc_norm_stderr\": 0.03769430314512567\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6919191919191919,\n \"acc_stderr\": 0.032894773300986155,\n \"\
acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.032894773300986155\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.032922966391551414,\n\
\ \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.032922966391551414\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5205128205128206,\n \"acc_stderr\": 0.02532966316348994,\n \
\ \"acc_norm\": 0.5205128205128206,\n \"acc_norm_stderr\": 0.02532966316348994\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871937,\n \
\ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871937\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4831932773109244,\n \"acc_stderr\": 0.03246013680375308,\n \
\ \"acc_norm\": 0.4831932773109244,\n \"acc_norm_stderr\": 0.03246013680375308\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\"\
: 0.7064220183486238,\n \"acc_stderr\": 0.01952515112263967,\n \"\
acc_norm\": 0.7064220183486238,\n \"acc_norm_stderr\": 0.01952515112263967\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3333333333333333,\n \"acc_stderr\": 0.03214952147802749,\n \"\
acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.03214952147802749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6911764705882353,\n \"acc_stderr\": 0.03242661719827218,\n \"\
acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.03242661719827218\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6497890295358649,\n \"acc_stderr\": 0.031052391937584346,\n \
\ \"acc_norm\": 0.6497890295358649,\n \"acc_norm_stderr\": 0.031052391937584346\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\
\ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\
\ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\
\ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6446280991735537,\n \"acc_stderr\": 0.04369236326573981,\n \"\
acc_norm\": 0.6446280991735537,\n \"acc_norm_stderr\": 0.04369236326573981\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326468,\n\
\ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326468\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7435897435897436,\n\
\ \"acc_stderr\": 0.02860595370200425,\n \"acc_norm\": 0.7435897435897436,\n\
\ \"acc_norm_stderr\": 0.02860595370200425\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7100893997445722,\n\
\ \"acc_stderr\": 0.016225017944770964,\n \"acc_norm\": 0.7100893997445722,\n\
\ \"acc_norm_stderr\": 0.016225017944770964\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.026483392042098174,\n\
\ \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.026483392042098174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.30614525139664805,\n\
\ \"acc_stderr\": 0.015414494487903217,\n \"acc_norm\": 0.30614525139664805,\n\
\ \"acc_norm_stderr\": 0.015414494487903217\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.028541722692618874,\n\
\ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.028541722692618874\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\
\ \"acc_stderr\": 0.026981478043648043,\n \"acc_norm\": 0.6559485530546624,\n\
\ \"acc_norm_stderr\": 0.026981478043648043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6172839506172839,\n \"acc_stderr\": 0.027044538138402605,\n\
\ \"acc_norm\": 0.6172839506172839,\n \"acc_norm_stderr\": 0.027044538138402605\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.35106382978723405,\n \"acc_stderr\": 0.02847350127296377,\n \
\ \"acc_norm\": 0.35106382978723405,\n \"acc_norm_stderr\": 0.02847350127296377\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3500651890482399,\n\
\ \"acc_stderr\": 0.012182552313215179,\n \"acc_norm\": 0.3500651890482399,\n\
\ \"acc_norm_stderr\": 0.012182552313215179\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.03018753206032939,\n\
\ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.03018753206032939\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5049019607843137,\n \"acc_stderr\": 0.020226862710039463,\n \
\ \"acc_norm\": 0.5049019607843137,\n \"acc_norm_stderr\": 0.020226862710039463\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\
\ \"acc_stderr\": 0.04673752333670237,\n \"acc_norm\": 0.6090909090909091,\n\
\ \"acc_norm_stderr\": 0.04673752333670237\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893782,\n\
\ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893782\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.03333333333333333,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.03333333333333333\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691583,\n\
\ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691583\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5026378052318262,\n\
\ \"mc2_stderr\": 0.015007516516438155\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6937647987371744,\n \"acc_stderr\": 0.012954385972802471\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.21910538286580744,\n \
\ \"acc_stderr\": 0.01139370663497807\n }\n}\n```"
repo_url: https://huggingface.co/s3nh/poorx32124
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|arc:challenge|25_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|arc:challenge|25_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|gsm8k|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|gsm8k|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hellaswag|10_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hellaswag|10_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-31-52.359427.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-37-56.380929.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-37-56.380929.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-31-52.359427.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-31-52.359427.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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- split: 2024_02_13T14_37_56.380929
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T14-37-56.380929.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- '**/details_harness|winogrande|5_2024-02-13T14-31-52.359427.parquet'
- split: 2024_02_13T14_37_56.380929
path:
- '**/details_harness|winogrande|5_2024-02-13T14-37-56.380929.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-13T14-37-56.380929.parquet'
- config_name: results
data_files:
- split: 2024_02_13T14_31_52.359427
path:
- results_2024-02-13T14-31-52.359427.parquet
- split: 2024_02_13T14_37_56.380929
path:
- results_2024-02-13T14-37-56.380929.parquet
- split: latest
path:
- results_2024-02-13T14-37-56.380929.parquet
---
# Dataset Card for Evaluation run of s3nh/poorx32124
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [s3nh/poorx32124](https://huggingface.co/s3nh/poorx32124) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_s3nh__poorx32124",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-13T14:37:56.380929](https://huggingface.co/datasets/open-llm-leaderboard/details_s3nh__poorx32124/blob/main/results_2024-02-13T14-37-56.380929.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": 0.526388001251259,
"acc_stderr": 0.03423883931564769,
"acc_norm": 0.532328851935962,
"acc_norm_stderr": 0.03497682392887963,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756544,
"mc2": 0.5026378052318262,
"mc2_stderr": 0.015007516516438155
},
"harness|arc:challenge|25": {
"acc": 0.5017064846416383,
"acc_stderr": 0.01461130570505699,
"acc_norm": 0.5315699658703071,
"acc_norm_stderr": 0.014582236460866977
},
"harness|hellaswag|10": {
"acc": 0.5550687114120693,
"acc_stderr": 0.004959425421382025,
"acc_norm": 0.7358095996813384,
"acc_norm_stderr": 0.00440000082274205
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421296,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421296
},
"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": 0.5526315789473685,
"acc_stderr": 0.04046336883978252,
"acc_norm": 0.5526315789473685,
"acc_norm_stderr": 0.04046336883978252
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6037735849056604,
"acc_stderr": 0.030102793781791197,
"acc_norm": 0.6037735849056604,
"acc_norm_stderr": 0.030102793781791197
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4797687861271676,
"acc_stderr": 0.03809342081273958,
"acc_norm": 0.4797687861271676,
"acc_norm_stderr": 0.03809342081273958
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.04158307533083286,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.04158307533083286
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.451063829787234,
"acc_stderr": 0.03252909619613197,
"acc_norm": 0.451063829787234,
"acc_norm_stderr": 0.03252909619613197
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
"acc_stderr": 0.04559522141958216,
"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.04559522141958216
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
"acc_stderr": 0.041657747757287644,
"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.041657747757287644
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36243386243386244,
"acc_stderr": 0.024757473902752052,
"acc_norm": 0.36243386243386244,
"acc_norm_stderr": 0.024757473902752052
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04285714285714281,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04285714285714281
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6096774193548387,
"acc_stderr": 0.027751256636969576,
"acc_norm": 0.6096774193548387,
"acc_norm_stderr": 0.027751256636969576
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.03481904844438804,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.03481904844438804
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6303030303030303,
"acc_stderr": 0.03769430314512567,
"acc_norm": 0.6303030303030303,
"acc_norm_stderr": 0.03769430314512567
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6919191919191919,
"acc_stderr": 0.032894773300986155,
"acc_norm": 0.6919191919191919,
"acc_norm_stderr": 0.032894773300986155
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7046632124352331,
"acc_stderr": 0.032922966391551414,
"acc_norm": 0.7046632124352331,
"acc_norm_stderr": 0.032922966391551414
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5205128205128206,
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},
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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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"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm_stderr": 0.03424042924691583
},
"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.016724646380756544,
"mc2": 0.5026378052318262,
"mc2_stderr": 0.015007516516438155
},
"harness|winogrande|5": {
"acc": 0.6937647987371744,
"acc_stderr": 0.012954385972802471
},
"harness|gsm8k|5": {
"acc": 0.21910538286580744,
"acc_stderr": 0.01139370663497807
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Azarthehulk/hand_written_dataset | ---
license: other
---
|
Ji-Ha/deepseek-coder-7b-base-imatrix-wikitext | ---
license: apache-2.0
---
Help me, it is not working 😭
What is the syntax for using imatrix command?? It has to be .imatrix file? |
ak8618/my-image-captioning-dataset1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 171096.0
num_examples: 3
download_size: 163572
dataset_size: 171096.0
---
# Dataset Card for "my-image-captioning-dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aminahanna/Plant-identification-system | ---
license: apache-2.0
---
|
wbxlala/processed_Epilepsy_seizure_prediction | ---
dataset_info:
features:
- name: token_type_ids
sequence: int64
- name: label
dtype: int64
- name: input_ids
sequence: int64
- name: attention_mask
sequence: int64
splits:
- name: train
num_bytes: 403350492
num_examples: 32775
- name: test
num_bytes: 126060020
num_examples: 10243
download_size: 23432326
dataset_size: 529410512
---
# Dataset Card for "processed_Epilepsy_seizure_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
naiviv1000/poncho | ---
license: apache-2.0
---
|
autoevaluate/autoeval-staging-eval-anli-plain_text-1f482c-14395973 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: test_r2
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r2
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
bigbio/bionlp_st_2011_epi |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2011 EPI
homepage: https://github.com/openbiocorpora/bionlp-st-2011-epi
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2011 EPI
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2011-epi
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
The dataset of the Epigenetics and Post-translational Modifications (EPI) task
of BioNLP Shared Task 2011.
## Citation Information
```
@inproceedings{ohta-etal-2011-overview,
title = "Overview of the Epigenetics and Post-translational
Modifications ({EPI}) task of {B}io{NLP} Shared Task 2011",
author = "Ohta, Tomoko and
Pyysalo, Sampo and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of {B}io{NLP} Shared Task 2011 Workshop",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-1803",
pages = "16--25",
}
```
|
Helsinki-NLP/opus_dogc | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- ca
- es
license:
- cc0-1.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: dogc
pretty_name: OPUS DOGC
dataset_info:
config_name: tmx
features:
- name: translation
dtype:
translation:
languages:
- ca
- es
splits:
- name: train
num_bytes: 1258920648
num_examples: 4763575
download_size: 599902063
dataset_size: 1258920648
configs:
- config_name: tmx
data_files:
- split: train
path: tmx/train-*
default: true
---
# Dataset Card for OPUS DOGC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://opus.nlpl.eu/DOGC/corpus/version/DOGC
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is multilingual with parallel text in:
- Catalan
- Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
A data instance contains the following fields:
- `ca`: the Catalan text
- `es`: the aligned Spanish text
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is in the Public Domain under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
PanJianxiong/InboxPrivate | ---
license: mit
---
|
CyberHarem/majokko_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of majokko (Kantai Collection)
This is the dataset of majokko (Kantai Collection), containing 22 images and their tags.
The core tags of this character are `hat, long_hair, witch_hat, twintails, blonde_hair, very_long_hair, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 22 | 13.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/majokko_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 22 | 10.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/majokko_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 17.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/majokko_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 22 | 12.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/majokko_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 20.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/majokko_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/majokko_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, arrow_(symbol), pantyhose, print_legwear, skirt, solo, chibi, school_uniform, star_print, smile, :q, wand |
| 1 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, blush, print_legwear, serafuku, star_print, purple_eyes, skirt, tongue_out, ;q, arrow_(symbol), hair_ornament, one_eye_closed, purple_pantyhose, wand |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | arrow_(symbol) | pantyhose | print_legwear | skirt | solo | chibi | school_uniform | star_print | smile | :q | wand | looking_at_viewer | blush | serafuku | purple_eyes | tongue_out | ;q | hair_ornament | one_eye_closed | purple_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:------------|:----------------|:--------|:-------|:--------|:-----------------|:-------------|:--------|:-----|:-------|:--------------------|:--------|:-----------|:--------------|:-------------|:-----|:----------------|:-----------------|:-------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | X | | | X | X | | X | X | X | X | X | X | X | X | X | X |
|
orgcatorg/russia-ukraine-cnbc | ---
dataset_info:
features:
- name: '@type'
dtype: string
- name: headline
dtype: string
- name: url
dtype: string
- name: dateModified
dtype: string
- name: datePublished
dtype: string
- name: mainEntityOfPage
dtype: string
- name: articleBody
dtype: string
- name: publisher
dtype: string
- name: image
dtype: string
- name: thumbnailUrl
dtype: string
- name: video
dtype: string
splits:
- name: train
num_bytes: 6035507
num_examples: 2757
download_size: 0
dataset_size: 6035507
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "russia-ukraine-cnbc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KETI-AIR/kor_anli | ---
language:
- ko
license:
- cc-by-nc-4.0
size_categories:
- 100K<n<1M
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: anli
pretty_name: Adversarial NLI
dataset_info:
features:
- name: data_index_by_user
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
splits:
- name: train_r1
num_bytes: 8505556
num_examples: 16946
- name: train_r2
num_bytes: 22521662
num_examples: 45460
- name: train_r3
num_bytes: 48605206
num_examples: 100459
- name: dev_r1
num_bytes: 628891
num_examples: 1000
- name: dev_r2
num_bytes: 613763
num_examples: 1000
- name: dev_r3
num_bytes: 740840
num_examples: 1200
- name: test_r1
num_bytes: 626555
num_examples: 1000
- name: test_r2
num_bytes: 633241
num_examples: 1000
- name: test_r3
num_bytes: 736887
num_examples: 1200
download_size: 23386318
dataset_size: 83612601
---
# Dataset Card for anli
## Licensing Information
[cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE)
## Source Data Citation INformation
```
@InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
} |
open-llm-leaderboard/details_Delcos__NATE-7b | ---
pretty_name: Evaluation run of Delcos/NATE-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Delcos/NATE-7b](https://huggingface.co/Delcos/NATE-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Delcos__NATE-7b\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-12T03:21:56.889828](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__NATE-7b/blob/main/results_2023-10-12T03-21-56.889828.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\": 0.5894733182245001,\n\
\ \"acc_stderr\": 0.03414332313258469,\n \"acc_norm\": 0.5933830960354635,\n\
\ \"acc_norm_stderr\": 0.03412320397463523,\n \"mc1\": 0.3953488372093023,\n\
\ \"mc1_stderr\": 0.017115815632418197,\n \"mc2\": 0.571756969256499,\n\
\ \"mc2_stderr\": 0.01564827771634302\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326025,\n\
\ \"acc_norm\": 0.6092150170648464,\n \"acc_norm_stderr\": 0.014258563880513778\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.620991834295957,\n\
\ \"acc_stderr\": 0.004841486716855774,\n \"acc_norm\": 0.8209520015933081,\n\
\ \"acc_norm_stderr\": 0.0038260895866500536\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5111111111111111,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.04017901275981749,\n\
\ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.04017901275981749\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\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398177,\n\
\ \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398177\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6180555555555556,\n\
\ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n\
\ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5606936416184971,\n\
\ \"acc_stderr\": 0.03784271932887467,\n \"acc_norm\": 0.5606936416184971,\n\
\ \"acc_norm_stderr\": 0.03784271932887467\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.047240073523838876,\n\
\ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.047240073523838876\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.046882617226215055,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.046882617226215055\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5063829787234042,\n \"acc_stderr\": 0.032683358999363366,\n\
\ \"acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.032683358999363366\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\
\ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\
\ \"acc_norm_stderr\": 0.04404556157374767\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\"\
: 0.36243386243386244,\n \"acc_stderr\": 0.02475747390275206,\n \"\
acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.02475747390275206\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.043902592653775614,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.043902592653775614\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\": 0.7032258064516129,\n\
\ \"acc_stderr\": 0.025988500792411898,\n \"acc_norm\": 0.7032258064516129,\n\
\ \"acc_norm_stderr\": 0.025988500792411898\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\"\
: {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\
\ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217483,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217483\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306433,\n\
\ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306433\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094767,\n\
\ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094767\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6008403361344538,\n \"acc_stderr\": 0.03181110032413926,\n \
\ \"acc_norm\": 0.6008403361344538,\n \"acc_norm_stderr\": 0.03181110032413926\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7944954128440367,\n \"acc_stderr\": 0.017324352325016022,\n \"\
acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.017324352325016022\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653064,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653064\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159263,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159263\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699796,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6412213740458015,\n \"acc_stderr\": 0.04206739313864908,\n\
\ \"acc_norm\": 0.6412213740458015,\n \"acc_norm_stderr\": 0.04206739313864908\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6871165644171779,\n \"acc_stderr\": 0.03642914578292406,\n\
\ \"acc_norm\": 0.6871165644171779,\n \"acc_norm_stderr\": 0.03642914578292406\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n\
\ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\
\ \"acc_stderr\": 0.024662496845209825,\n \"acc_norm\": 0.8290598290598291,\n\
\ \"acc_norm_stderr\": 0.024662496845209825\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7752234993614304,\n\
\ \"acc_stderr\": 0.014927447101937153,\n \"acc_norm\": 0.7752234993614304,\n\
\ \"acc_norm_stderr\": 0.014927447101937153\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.025522474632121615,\n\
\ \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.025522474632121615\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43798882681564244,\n\
\ \"acc_stderr\": 0.016593394227564846,\n \"acc_norm\": 0.43798882681564244,\n\
\ \"acc_norm_stderr\": 0.016593394227564846\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6535947712418301,\n \"acc_stderr\": 0.02724561304721536,\n\
\ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.02724561304721536\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\
\ \"acc_stderr\": 0.026981478043648043,\n \"acc_norm\": 0.6559485530546624,\n\
\ \"acc_norm_stderr\": 0.026981478043648043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6697530864197531,\n \"acc_stderr\": 0.026168298456732852,\n\
\ \"acc_norm\": 0.6697530864197531,\n \"acc_norm_stderr\": 0.026168298456732852\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4574468085106383,\n \"acc_stderr\": 0.029719281272236837,\n \
\ \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.029719281272236837\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n\
\ \"acc_stderr\": 0.012695244711379778,\n \"acc_norm\": 0.44589308996088656,\n\
\ \"acc_norm_stderr\": 0.012695244711379778\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5661764705882353,\n \"acc_stderr\": 0.03010563657001663,\n\
\ \"acc_norm\": 0.5661764705882353,\n \"acc_norm_stderr\": 0.03010563657001663\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5915032679738562,\n \"acc_stderr\": 0.019886221037501862,\n \
\ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.019886221037501862\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n\
\ \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7810945273631841,\n\
\ \"acc_stderr\": 0.029239174636647,\n \"acc_norm\": 0.7810945273631841,\n\
\ \"acc_norm_stderr\": 0.029239174636647\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.032180937956023566,\n\
\ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.032180937956023566\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3953488372093023,\n\
\ \"mc1_stderr\": 0.017115815632418197,\n \"mc2\": 0.571756969256499,\n\
\ \"mc2_stderr\": 0.01564827771634302\n }\n}\n```"
repo_url: https://huggingface.co/Delcos/NATE-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|arc:challenge|25_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hellaswag|10_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T03-21-56.889828.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T03-21-56.889828.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T03-21-56.889828.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T03-21-56.889828.parquet'
- config_name: results
data_files:
- split: 2023_10_12T03_21_56.889828
path:
- results_2023-10-12T03-21-56.889828.parquet
- split: latest
path:
- results_2023-10-12T03-21-56.889828.parquet
---
# Dataset Card for Evaluation run of Delcos/NATE-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Delcos/NATE-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Delcos/NATE-7b](https://huggingface.co/Delcos/NATE-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_Delcos__NATE-7b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T03:21:56.889828](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__NATE-7b/blob/main/results_2023-10-12T03-21-56.889828.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": 0.5894733182245001,
"acc_stderr": 0.03414332313258469,
"acc_norm": 0.5933830960354635,
"acc_norm_stderr": 0.03412320397463523,
"mc1": 0.3953488372093023,
"mc1_stderr": 0.017115815632418197,
"mc2": 0.571756969256499,
"mc2_stderr": 0.01564827771634302
},
"harness|arc:challenge|25": {
"acc": 0.5784982935153583,
"acc_stderr": 0.014430197069326025,
"acc_norm": 0.6092150170648464,
"acc_norm_stderr": 0.014258563880513778
},
"harness|hellaswag|10": {
"acc": 0.620991834295957,
"acc_stderr": 0.004841486716855774,
"acc_norm": 0.8209520015933081,
"acc_norm_stderr": 0.0038260895866500536
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5111111111111111,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.5111111111111111,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.04017901275981749,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.04017901275981749
},
"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": 0.6415094339622641,
"acc_stderr": 0.02951470358398177,
"acc_norm": 0.6415094339622641,
"acc_norm_stderr": 0.02951470358398177
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6180555555555556,
"acc_stderr": 0.040629907841466674,
"acc_norm": 0.6180555555555556,
"acc_norm_stderr": 0.040629907841466674
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5606936416184971,
"acc_stderr": 0.03784271932887467,
"acc_norm": 0.5606936416184971,
"acc_norm_stderr": 0.03784271932887467
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3431372549019608,
"acc_stderr": 0.047240073523838876,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.047240073523838876
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215055,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215055
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5063829787234042,
"acc_stderr": 0.032683358999363366,
"acc_norm": 0.5063829787234042,
"acc_norm_stderr": 0.032683358999363366
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.32456140350877194,
"acc_stderr": 0.04404556157374767,
"acc_norm": 0.32456140350877194,
"acc_norm_stderr": 0.04404556157374767
},
"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": 0.36243386243386244,
"acc_stderr": 0.02475747390275206,
"acc_norm": 0.36243386243386244,
"acc_norm_stderr": 0.02475747390275206
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.043902592653775614,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.043902592653775614
},
"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": 0.7032258064516129,
"acc_stderr": 0.025988500792411898,
"acc_norm": 0.7032258064516129,
"acc_norm_stderr": 0.025988500792411898
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-high_school_european_history|5": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
zgcarvalho/oas-test | ---
license: cc-by-4.0
size_categories: 10M<n<100M
pretty_name: Observed Antibody Space
config_names:
- paired
- unpaired
tags:
- biology
- protein
dataset_info:
- config_name: paired
features:
- name: sequence_heavy
dtype: string
- name: sequence_light
dtype: string
- name: cdr1_heavy
dtype: string
- name: cdr2_heavy
dtype: string
- name: cdr3_heavy
dtype: string
- name: fwr1_heavy
dtype: string
- name: fwr2_heavy
dtype: string
- name: fwr3_heavy
dtype: string
- name: fwr4_heavy
dtype: string
- name: cdr1_light
dtype: string
- name: cdr2_light
dtype: string
- name: cdr3_light
dtype: string
- name: fwr1_light
dtype: string
- name: fwr2_light
dtype: string
- name: fwr3_light
dtype: string
- name: fwr4_light
dtype: string
- name: species
dtype: string
- name: vaccine
dtype: string
- name: disease
dtype: string
splits:
- name: train
num_bytes: 985822519
num_examples: 1777462
download_size: 0
dataset_size: 985822519
- config_name: unpaired
features:
- name: sequence
dtype: string
- name: chain
dtype: string
- name: cdr1
dtype: string
- name: cdr2
dtype: string
- name: cdr3
dtype: string
- name: fwr1
dtype: string
- name: fwr2
dtype: string
- name: fwr3
dtype: string
- name: fwr4
dtype: string
- name: species
dtype: string
- name: vaccine
dtype: string
- name: disease
dtype: string
splits:
- name: train
num_bytes: 4671469078
num_examples: 15925303
download_size: 0
dataset_size: 4671469078
configs:
- config_name: paired
data_files:
- split: train
path: paired/train-*
- config_name: unpaired
data_files:
- split: train
path: unpaired/train-*
---
# Dataset Card for Observed Antibody Space
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
tasksource/tracie | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- nli
---
https://github.com/allenai/aristo-leaderboard/tree/master/tracie/data
```
@inproceedings{ZRNKSR21,
author = {Ben Zhou and Kyle Richardson and Qiang Ning and Tushar Khot and Ashish Sabharwal and Dan Roth},
title = {Temporal Reasoning on Implicit Events from Distant Supervision},
booktitle = {NAACL},
year = {2021},
}
``` |
Raniahossam33/Badas | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 816014817.47
num_examples: 3143
download_size: 381649255
dataset_size: 816014817.47
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Harikrishnan46624/AI_QA_Data | ---
license: apache-2.0
---
|
Mahmutxx/sex | ---
license: cc-by-nc-nd-4.0
---
|
eBeyzaG/damaged_building | ---
license: mit
---
|
wenhanhan/SCIFACT_dev | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1702806
num_examples: 1189
download_size: 651099
dataset_size: 1702806
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "SCIFACT_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yuan-tian/chartgpt-dataset | ---
license: apache-2.0
---
# Dataset Card for ChartGPT-Dataset
## Dataset Details
### Dataset Description
This dataset is used to train the model [ChartGPT](https://huggingface.co/yuan-tian/chartgpt). For more information, please refer to the paper.
* **Language(s) (NLP)**: English
* **License**: Apache 2.0
* **Research paper**: [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://arxiv.org/abs/2311.01920)
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@article{tian2024chartgpt,
title={ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language},
author={Tian, Yuan and Cui, Weiwei and Deng, Dazhen and Yi, Xinjing and Yang, Yurun and Zhang, Haidong and Wu, Yingcai},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
pages={1-15},
doi={10.1109/TVCG.2024.3368621}
}
``` |
jlbaker361/flickr_humans_dim_128_20k | ---
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: src
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 586654859.0
num_examples: 20000
download_size: 585876097
dataset_size: 586654859.0
---
# Dataset Card for "flickr_humans_dim_128_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Deojoandco/ah_openai_dialog_v3 | ---
dataset_info:
features:
- name: url
dtype: int64
- name: id
dtype: int64
- name: num_comments
dtype: int64
- name: name
dtype: int64
- name: title
dtype: int64
- name: body
dtype: int64
- name: score
dtype: int64
- name: upvote_ratio
dtype: int64
- name: distinguished
dtype: int64
- name: over_18
dtype: int64
- name: created_utc
dtype: int64
- name: comments
dtype: int64
- name: best_num_comments
dtype: int64
- name: query
dtype: int64
- name: dialog
dtype: int64
- name: dialog_success
dtype: int64
splits:
- name: train
num_bytes: 332544
num_examples: 2598
download_size: 237297
dataset_size: 332544
---
# Dataset Card for "ah_openai_dialog_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hac541309/pg-ko-tknizer-ko | ---
language: ko
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9307769796
num_examples: 10508255
download_size: 5361735760
dataset_size: 9307769796
---
# Dataset Card for "pg-ko-tknizer-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
valehamiri/test-large-files-2 | ---
license: cc-by-4.0
---
|
EarthnDusk/Star_Marvel_comics | ---
license: creativeml-openrail-m
task_categories:
- text-to-image
language:
- en
tags:
- marvel
- comics
- duskfallcrew
- text to image
- stable diffusion
- lora
pretty_name: Star Villain Marvel Comics LoRa
size_categories:
- 1K<n<10K
---
# Dataset Card for Star Villain Marvel Comics LoRa
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://civitai.com/models/14831/star-ryan-ripley:**
- **https://civitai.com/models/14831/star-ryan-ripley**
# Data set for Duskfallcrew/Star_Marvel_comics_LoRa
Trained with: https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb
# Where else can i find this ?
Both safetensors files are at: https://civitai.com/models/14831/star-ryan-ripley
# The outputs aren't comic format.
I haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.
# YOU - YOU'RE A DATA SCRAPER!!!
And you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.
# You do way too much is there a way to pay you in... anyway?
Yea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE!
https://ko-fi.com/DUSKFALLcrew
# What's the deal, is there a lisc on this?
Creative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool. |
enzokro/some-test-dataset | ---
license: unknown
---
|
adalib/llama_index | ---
dataset_info:
features:
- name: code
dtype: string
- name: apis
sequence: string
- name: extract_api
dtype: string
splits:
- name: train
num_bytes: 7729527
num_examples: 827
- name: test
num_bytes: 2224065
num_examples: 217
download_size: 2486532
dataset_size: 9953592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
reyrg/thermal-camera | ---
license: cc-by-nc-4.0
---
|
Ketan3101/bot_train | ---
license: mit
---
|
hemachandher/fdataset | ---
dataset_info:
features:
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: text
dtype: string
splits:
- name: train
num_bytes: 138098403
num_examples: 1001
download_size: 100680724
dataset_size: 138098403
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
skrishna/boolq_transformed | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: bool
- name: passage
dtype: string
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 12710260
num_examples: 9427
- name: test
num_bytes: 4360958
num_examples: 3270
download_size: 10120344
dataset_size: 17071218
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "boolq_transformed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_NeuralNovel__Gecko-7B-v0.1-DPO | ---
pretty_name: Evaluation run of NeuralNovel/Gecko-7B-v0.1-DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NeuralNovel/Gecko-7B-v0.1-DPO](https://huggingface.co/NeuralNovel/Gecko-7B-v0.1-DPO)\
\ 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NeuralNovel__Gecko-7B-v0.1-DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-16T21:04:36.728046](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Gecko-7B-v0.1-DPO/blob/main/results_2024-01-16T21-04-36.728046.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\": 0.6033992573552084,\n\
\ \"acc_stderr\": 0.033215570468910556,\n \"acc_norm\": 0.6072820051491797,\n\
\ \"acc_norm_stderr\": 0.033890375696348236,\n \"mc1\": 0.40758873929008566,\n\
\ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5742349007721483,\n\
\ \"mc2_stderr\": 0.015183840204619218\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5187713310580204,\n \"acc_stderr\": 0.014601090150633964,\n\
\ \"acc_norm\": 0.5674061433447098,\n \"acc_norm_stderr\": 0.014478005694182524\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6263692491535551,\n\
\ \"acc_stderr\": 0.004827786289074842,\n \"acc_norm\": 0.8238398725353515,\n\
\ \"acc_norm_stderr\": 0.003801777779809583\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797611,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797611\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6319444444444444,\n\
\ \"acc_stderr\": 0.04032999053960719,\n \"acc_norm\": 0.6319444444444444,\n\
\ \"acc_norm_stderr\": 0.04032999053960719\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.49,\n \"acc_stderr\": 0.05024183937956914,\n \"acc_norm\"\
: 0.49,\n \"acc_norm_stderr\": 0.05024183937956914\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\
\ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\
\ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\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\": 0.5191489361702127,\n \"acc_stderr\": 0.032662042990646796,\n\
\ \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.032662042990646796\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.046774730044911984\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\"\
: 0.35185185185185186,\n \"acc_stderr\": 0.02459497512892094,\n \"\
acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02459497512892094\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.04343525428949097,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.04343525428949097\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.6612903225806451,\n\
\ \"acc_stderr\": 0.02692344605930284,\n \"acc_norm\": 0.6612903225806451,\n\
\ \"acc_norm_stderr\": 0.02692344605930284\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7373737373737373,\n \"acc_stderr\": 0.03135305009533086,\n \"\
acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.03135305009533086\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306433,\n\
\ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306433\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6,\n \"acc_stderr\": 0.02483881198803316,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.02483881198803316\n },\n \"harness|hendrycksTest-high_school_mathematics|5\"\
: {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n\
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.03128217706368461,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.03128217706368461\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.038969819642573754,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.038969819642573754\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154343,\n \"\
acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154343\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\": 0.7745098039215687,\n\
\ \"acc_stderr\": 0.029331162294251735,\n \"acc_norm\": 0.7745098039215687,\n\
\ \"acc_norm_stderr\": 0.029331162294251735\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676166,\n\
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676166\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\
\ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\
\ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\
\ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\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\": 0.7314814814814815,\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\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.789272030651341,\n\
\ \"acc_stderr\": 0.01458381246586254,\n \"acc_norm\": 0.789272030651341,\n\
\ \"acc_norm_stderr\": 0.01458381246586254\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6791907514450867,\n \"acc_stderr\": 0.025131000233647897,\n\
\ \"acc_norm\": 0.6791907514450867,\n \"acc_norm_stderr\": 0.025131000233647897\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34301675977653634,\n\
\ \"acc_stderr\": 0.015876912673057738,\n \"acc_norm\": 0.34301675977653634,\n\
\ \"acc_norm_stderr\": 0.015876912673057738\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6720257234726688,\n\
\ \"acc_stderr\": 0.026664410886937617,\n \"acc_norm\": 0.6720257234726688,\n\
\ \"acc_norm_stderr\": 0.026664410886937617\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.026462487777001865,\n\
\ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.026462487777001865\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4217731421121252,\n\
\ \"acc_stderr\": 0.012612974369390977,\n \"acc_norm\": 0.4217731421121252,\n\
\ \"acc_norm_stderr\": 0.012612974369390977\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6066176470588235,\n \"acc_stderr\": 0.029674288281311155,\n\
\ \"acc_norm\": 0.6066176470588235,\n \"acc_norm_stderr\": 0.029674288281311155\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6094771241830066,\n \"acc_stderr\": 0.0197370089980946,\n \
\ \"acc_norm\": 0.6094771241830066,\n \"acc_norm_stderr\": 0.0197370089980946\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\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\": 0.7263681592039801,\n\
\ \"acc_stderr\": 0.03152439186555401,\n \"acc_norm\": 0.7263681592039801,\n\
\ \"acc_norm_stderr\": 0.03152439186555401\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \
\ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\
acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40758873929008566,\n\
\ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5742349007721483,\n\
\ \"mc2_stderr\": 0.015183840204619218\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698341\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.45034116755117515,\n \
\ \"acc_stderr\": 0.013704390498582816\n }\n}\n```"
repo_url: https://huggingface.co/NeuralNovel/Gecko-7B-v0.1-DPO
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|arc:challenge|25_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|gsm8k|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hellaswag|10_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-04-36.728046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-16T21-04-36.728046.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- '**/details_harness|winogrande|5_2024-01-16T21-04-36.728046.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-16T21-04-36.728046.parquet'
- config_name: results
data_files:
- split: 2024_01_16T21_04_36.728046
path:
- results_2024-01-16T21-04-36.728046.parquet
- split: latest
path:
- results_2024-01-16T21-04-36.728046.parquet
---
# Dataset Card for Evaluation run of NeuralNovel/Gecko-7B-v0.1-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [NeuralNovel/Gecko-7B-v0.1-DPO](https://huggingface.co/NeuralNovel/Gecko-7B-v0.1-DPO) 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_NeuralNovel__Gecko-7B-v0.1-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-16T21:04:36.728046](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Gecko-7B-v0.1-DPO/blob/main/results_2024-01-16T21-04-36.728046.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": 0.6033992573552084,
"acc_stderr": 0.033215570468910556,
"acc_norm": 0.6072820051491797,
"acc_norm_stderr": 0.033890375696348236,
"mc1": 0.40758873929008566,
"mc1_stderr": 0.017201949234553107,
"mc2": 0.5742349007721483,
"mc2_stderr": 0.015183840204619218
},
"harness|arc:challenge|25": {
"acc": 0.5187713310580204,
"acc_stderr": 0.014601090150633964,
"acc_norm": 0.5674061433447098,
"acc_norm_stderr": 0.014478005694182524
},
"harness|hellaswag|10": {
"acc": 0.6263692491535551,
"acc_stderr": 0.004827786289074842,
"acc_norm": 0.8238398725353515,
"acc_norm_stderr": 0.003801777779809583
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464242,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464242
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.03878139888797611,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.03878139888797611
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6319444444444444,
"acc_stderr": 0.04032999053960719,
"acc_norm": 0.6319444444444444,
"acc_norm_stderr": 0.04032999053960719
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956914,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956914
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5838150289017341,
"acc_stderr": 0.03758517775404947,
"acc_norm": 0.5838150289017341,
"acc_norm_stderr": 0.03758517775404947
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"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": 0.5191489361702127,
"acc_stderr": 0.032662042990646796,
"acc_norm": 0.5191489361702127,
"acc_norm_stderr": 0.032662042990646796
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.046774730044911984,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.046774730044911984
},
"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": 0.35185185185185186,
"acc_stderr": 0.02459497512892094,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.02459497512892094
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.04343525428949097,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.04343525428949097
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6612903225806451,
"acc_stderr": 0.02692344605930284,
"acc_norm": 0.6612903225806451,
"acc_norm_stderr": 0.02692344605930284
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885417,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885417
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7373737373737373,
"acc_stderr": 0.03135305009533086,
"acc_norm": 0.7373737373737373,
"acc_norm_stderr": 0.03135305009533086
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8549222797927462,
"acc_stderr": 0.025416343096306433,
"acc_norm": 0.8549222797927462,
"acc_norm_stderr": 0.025416343096306433
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6,
"acc_stderr": 0.02483881198803316,
"acc_norm": 0.6,
"acc_norm_stderr": 0.02483881198803316
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.02889774874113114,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.02889774874113114
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.03128217706368461,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.03128217706368461
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.038969819642573754,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.038969819642573754
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8036697247706422,
"acc_stderr": 0.017030719339154343,
"acc_norm": 0.8036697247706422,
"acc_norm_stderr": 0.017030719339154343
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7745098039215687,
"acc_stderr": 0.029331162294251735,
"acc_norm": 0.7745098039215687,
"acc_norm_stderr": 0.029331162294251735
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.026750826994676166,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.026750826994676166
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6457399103139013,
"acc_stderr": 0.032100621541349864,
"acc_norm": 0.6457399103139013,
"acc_norm_stderr": 0.032100621541349864
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7175572519083969,
"acc_stderr": 0.03948406125768361,
"acc_norm": 0.7175572519083969,
"acc_norm_stderr": 0.03948406125768361
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990946,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990946
},
"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": 0.7239263803680982,
"acc_stderr": 0.035123852837050475,
"acc_norm": 0.7239263803680982,
"acc_norm_stderr": 0.035123852837050475
},
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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|truthfulqa:mc|0": {
"mc1": 0.40758873929008566,
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},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
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},
"harness|gsm8k|5": {
"acc": 0.45034116755117515,
"acc_stderr": 0.013704390498582816
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
khalidalt/flores_text | ---
dataset_info:
features:
- name: id
dtype: int32
- name: URL
dtype: string
- name: domain
dtype: string
- name: topic
dtype: string
- name: has_image
dtype: int32
- name: has_hyperlink
dtype: int32
- name: sentence_arb_Arab
dtype: string
- name: sentence_eng_Latn
dtype: string
- name: text
dtype: string
splits:
- name: dev
num_bytes: 816795
num_examples: 997
download_size: 435355
dataset_size: 816795
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
---
# Dataset Card for "flores_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Akshay-Sai/UA_Speech | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 5378040000
num_examples: 5600
download_size: 737238219
dataset_size: 5378040000
---
# Dataset Card for "UA_Speech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-xsum-a94d90d8-10385372 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-wikihow
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-wikihow
* Dataset: xsum
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Scofield](https://huggingface.co/Scofield) for evaluating this model. |
DLIlab/webarena_two_shot_instructions | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: task
dtype: string
- name: web_name
dtype: string
splits:
- name: train
num_bytes: 16798
num_examples: 130
download_size: 8477
dataset_size: 16798
---
Each instruction is generated from Instruction_generation_two_shot in question_generation file.
These are the number of tasks for each site.
gitlab (15), openstreetmap (35), reddit (10), Magento (35), One Stop Market (35)
---
dataset_info:
features:
- name: task
dtype: string
- name: web_name
dtype: string
splits:
- name: train
num_bytes: 16798
num_examples: 130
download_size: 8477
dataset_size: 16798
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
euclaise/naturalinstructions2_preferences | ---
dataset_info:
features:
- name: input
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
- name: positive_explanation
dtype: string
- name: negative_explanation
dtype: string
- name: source
sequence: string
- name: instruction
sequence: string
splits:
- name: train
num_bytes: 1577137
num_examples: 2040
download_size: 430836
dataset_size: 1577137
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
[Natural Instructions v2](https://instructions.apps.allenai.org/) processed for preference learning, using the negatives from the example outputs |
DanielHesslow/SwissProt-Pfam | ---
language:
- protein sequences
datasets:
- Swissprot
tags:
- Protein
- PFam
---
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with PFam labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The Pfam-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without Pfam tags are removed.
|
Ash-Hun/Welfare-QA | ---
license: mit
task_categories:
- question-answering
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Documents
dtype: string
splits:
- name: train
num_bytes: 3207687
num_examples: 9547
language:
- ko
tags:
- Ask-for-Welfare
- WelSSiSKo
pretty_name: AskWelfare-v1.0
---
# Dataset Card for Welfare-QA
## Description
대한민국 보건복지부에서 발간하였으며 2023년 5월 11일에 [복지로](https://www.bokjiro.go.kr/ssis-tbu/index.do)에 등록된 안내책자를 바탕으로 만들어졌습니다.
총 413페이지의 비정형 PDF에 담긴 약 460여개의 복지제도에 대한 Question-Answering-Documents 데이터셋입니다.
원본은 다음 링크에서 확인해보실 수 있습니다. [👉 '2023 나에게 힘이되는 복지서비스 PDF 책자'](https://www.bokjiro.go.kr/ssis-tbu/twatxa/wlfarePr/selectWlfareSubMain.do?dmMnuParam=column27)
## Project Repo
- Github Repo : [Ask-for-Welfare](https://github.com/ssisOneTeam/Ask-for-Welfare)
## How to Uses
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Ash-Hun/Welfare-QA", split='train')
>>> dataset
Dataset({
features: ['Question', 'Answer', 'Documents'],
num_rows: 9547
})
```
```python
>>> dataset[0]
{'Question': 'LPG 사용 가정의 고무호스를 교체하려면 어떤 지원을 받을 수 있나요?',
'Answer': 'LPG용기 사용가구 시설개선 사업을 통해 LPG 고무호스를 금속배관으로 교체하는 데 필요한 지원을 받으실 수 있습니다.',
'Documents': 'LPG용기 사용가구 시설개선'}
```
<!---
### Contributors
<table align="center">
<tr>
<td align="center">
<a href="https://github.com/PangPangGod">
<img src="https://github.com/PangPangGod.png" width="100px;" alt="송준호"/><br />
<sub><b>송준호</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/ash-hun">
<img src="https://github.com/ash-hun.png" width="100px;" alt="최재훈"/><br />
<sub><b>최재훈</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/MoonHeesun">
<img src="https://github.com/MoonHeesun.png" width="100px;" alt="문희선"/><br />
<sub><b>문희선</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/Noveled">
<img src="https://github.com/Noveled.png" width="100px;" alt="김민식"/><br />
<sub><b>김민식</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/myeongjun1007">
<img src="https://github.com/myeongjun1007.png" width="100px;" alt="현명준"/><br />
<sub><b>현명준</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/kha-jaejun">
<img src="https://github.com/kha-jaejun.png" width="100px;" alt="가재준"/><br />
<sub><b>가재준</b></sub>
</a>
</td>
</tr>
</table>
--->
|
qmeeus/vp-er-14l | ---
dataset_info:
- config_name: multilang
features:
- name: audio_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: task
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 9267861770.0
num_examples: 28000
download_size: 5714236793
dataset_size: 9267861770.0
- config_name: transcribe_cs
features:
- name: audio_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: task
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 3967671467.0
num_examples: 12000
download_size: 3962625704
dataset_size: 3967671467.0
- config_name: transcribe_de
features:
- name: audio_id
dtype: string
- name: audio
dtype:
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sampling_rate: 16000
- name: text
dtype: string
- name: task
dtype: string
- name: language
dtype: string
splits:
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num_bytes: 3496757286.0
num_examples: 12000
download_size: 3486791342
dataset_size: 3496757286.0
- config_name: transcribe_en
features:
- name: audio_id
dtype: string
- name: audio
dtype:
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sampling_rate: 16000
- name: text
dtype: string
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dtype: string
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dtype: string
splits:
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num_examples: 12000
download_size: 3984271576
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- config_name: transcribe_es
features:
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dtype:
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sampling_rate: 16000
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splits:
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- config_name: transcribe_fr
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- config_name: translate_fr
features:
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dtype:
audio:
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- name: language
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- config_name: translate_hu
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- config_name: translate_nl
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dataset_size: 357484418.0
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features:
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- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
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dtype: string
- name: language
dtype: string
splits:
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dataset_size: 326989680.0
- config_name: translate_sl
features:
- name: audio_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
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dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 327012282.0
num_examples: 1000
download_size: 325739820
dataset_size: 327012282.0
configs:
- config_name: multilang
data_files:
- split: train
path: multilang/train-*
- config_name: transcribe_cs
data_files:
- split: train
path: transcribe_cs/train-*
- config_name: transcribe_de
data_files:
- split: train
path: transcribe_de/train-*
- config_name: transcribe_en
data_files:
- split: train
path: transcribe_en/train-*
- config_name: transcribe_es
data_files:
- split: train
path: transcribe_es/train-*
- config_name: transcribe_fi
data_files:
- split: train
path: transcribe_fi/train-*
- config_name: transcribe_fr
data_files:
- split: train
path: transcribe_fr/train-*
- config_name: transcribe_hr
data_files:
- split: train
path: transcribe_hr/train-*
- config_name: transcribe_hu
data_files:
- split: train
path: transcribe_hu/train-*
- config_name: transcribe_it
data_files:
- split: train
path: transcribe_it/train-*
- config_name: transcribe_nl
data_files:
- split: train
path: transcribe_nl/train-*
- config_name: transcribe_pl
data_files:
- split: train
path: transcribe_pl/train-*
- config_name: transcribe_ro
data_files:
- split: train
path: transcribe_ro/train-*
- config_name: transcribe_sk
data_files:
- split: train
path: transcribe_sk/train-*
- config_name: transcribe_sl
data_files:
- split: train
path: transcribe_sl/train-*
- config_name: translate_cs
data_files:
- split: train
path: translate_cs/train-*
- config_name: translate_de
data_files:
- split: train
path: translate_de/train-*
- config_name: translate_en
data_files:
- split: train
path: translate_en/train-*
- config_name: translate_es
data_files:
- split: train
path: translate_es/train-*
- config_name: translate_fi
data_files:
- split: train
path: translate_fi/train-*
- config_name: translate_fr
data_files:
- split: train
path: translate_fr/train-*
- config_name: translate_hr
data_files:
- split: train
path: translate_hr/train-*
- config_name: translate_hu
data_files:
- split: train
path: translate_hu/train-*
- config_name: translate_it
data_files:
- split: train
path: translate_it/train-*
- config_name: translate_nl
data_files:
- split: train
path: translate_nl/train-*
- config_name: translate_pl
data_files:
- split: train
path: translate_pl/train-*
- config_name: translate_ro
data_files:
- split: train
path: translate_ro/train-*
- config_name: translate_sk
data_files:
- split: train
path: translate_sk/train-*
- config_name: translate_sl
data_files:
- split: train
path: translate_sl/train-*
---
# Dataset Card for "vp-er-14l"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vibhorag101/phr-mental-therapy-dataset-conversational-format-1024-tokens | ---
dataset_info:
features:
- name: id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 104785305.89661506
num_examples: 22282
- name: test
num_bytes: 22455337.745998427
num_examples: 4775
- name: val
num_bytes: 22455337.745998427
num_examples: 4775
download_size: 54150466
dataset_size: 149695981.3886119
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
aditijha/instruct_v3_subset_2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3930962.2554168818
num_examples: 1000
download_size: 2048066
dataset_size: 3930962.2554168818
---
# Dataset Card for "instruct_v3_subset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Denissilva88/baseman | ---
license: openrail
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/22441020 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 186
num_examples: 10
download_size: 1339
dataset_size: 186
---
# Dataset Card for "22441020"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aagoluoglu/AI_HW4_frames | ---
dataset_info:
features:
- name: video_id
dtype: string
- name: frame_num
dtype: int64
- name: frame
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: timestamp
dtype: float64
splits:
- name: train
num_bytes: 746705523
num_examples: 2043
download_size: 746300812
dataset_size: 746705523
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
antiven0m/physical-reasoning-dpo | ---
license: unknown
language:
- en
tags:
- DPO
pretty_name: Physical Reasoning DPO
task_categories:
- question-answering
size_categories:
- n<1K
---

v1: First generation run, needs a bunch of cleaning. 03/16
---
<style>
html,
body {
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
}
svg {
width: 50%;
}
.path {
fill: none;
stroke: red;
stroke-width: 5;
stroke-linecap: round;
stroke-linejoin: round;
stroke-dasharray: 5, 7, 9, 11, 13, 15, 13, 11, 9, 7;
stroke-dashoffset: 0;
animation: dash 10s linear infinite alternate;
}
@keyframes dash {
to {
stroke-dashoffset: 500;
}
}
</style> |
willwade/txt-sms-abbreviations | ---
license: apache-2.0
language:
- en
---
# Dataset Card for TXT/SMS Abbreviations
1539 sms/text speak abbreviations. Note we have parsed through NLTK to give a field of whether its generally recognised a whole word. **NB: Numbers are NOT noted as words. Be careful with numbers like 4 2 etc in your training data**
## Dataset Details
### Dataset Description
A curated list of abbreviations commonly used in text messages and gaming chat and other places. We have tried to remove explanations and explicitly put a full expansion of the abbreviation.
Be careful - some items are whole words already.
- **Curated by:** Will Wade
- **Language(s) (NLP):** English
- **License:** Apache-2
## Uses
Use as a training set for abbreviations or as part of a GEC task
```python
import csv
# Load abbreviations and their expansions from sms.csv into a dictionary
abbreviations_dict = {}
with open('sms.csv', mode='r', encoding='utf-8') as infile:
reader = csv.reader(infile, delimiter=',')
next(reader, None) # Skip the header if present
for row in reader:
if len(row) >= 3:
abbreviation = row[0].strip().upper()
expansion = row[1].strip()
is_word = row[2].strip().lower() in ['true', 'yes', '1'] # Convert to boolean
abbreviations_dict[abbreviation] = (expansion, is_word)
else:
print(f"Skipping malformed row: {row}")
```
|
TheBlackAbbot/provahabbo | ---
license: mit
---
|
jay401521/label0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: domain
dtype: string
- name: label
dtype: int64
- name: rank
dtype: int64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 922790.3333333334
num_examples: 10007
download_size: 441033
dataset_size: 922790.3333333334
---
# Dataset Card for "label0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yukiamenta/Franja | ---
license: apache-2.0
---
|
wdc/products-2017 | ---
annotations_creators:
- weak supervision
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: products-2017
size_categories:
- 1K<n<10K
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- data-integration
task_ids:
- entity-matching
- identity-resolution
- product-matching
paperswithcode_id: wdc-products
---
# Dataset Card for [products-2017]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [LSPCv2 Homepage](http://webdatacommons.org/largescaleproductcorpus/v2/index.html)
- **Point of Contact:** [Ralph Peeters](mailto:ralph.peeters@uni-mannheim.de)
### Dataset Summary
Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.
### Supported Tasks and Leaderboards
Entity Matching, Product Matching
### Languages
English
## Dataset Structure
### Data Instances
The data is structured as pairs of product offers with the corresponding match/non-match label. This is an example instance from the computers category:
```
{"pair_id":"581109#16637861","label":0,"id_left":581109,"category_left":"Computers_and_Accessories","cluster_id_left":1324529,"brand_left":"\"Gigabyte\"@en","title_left":" \"Gigabyte Radeon RX 480 G1 Gaming 4096MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_left":"\"GV-RX480G1 GAMING-4GD, Core Clock: 1202MHz, Boost Clock: 1290MHz, Memory: 4096MB 7000MHz GDDR5, Stream Processors: 2304, Crossfire Ready, VR Ready, FreeSync Ready, 3 Years Warranty\"@en ","price_left":null,"specTableContent_left":null,"id_right":16637861,"category_right":"Computers_and_Accessories","cluster_id_right":107415,"brand_right":"\"Gigabyte\"@en","title_right":" \"Gigabyte Radeon RX 550 Gaming OC 2048MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_right":"\"GV-RX550GAMING OC-2GD, Boost: 1219MHz, Memory: 2048MB 7000MHz GDDR5, Stream Processors: 512, DirectX 12 Support, 3 Years Warranty\"@en ","price_right":null,"specTableContent_right":null}
```
### Data Fields
- pair_id: unique identifier of a pair (string)
- label: binary label, match or non-match (int)
The following attributes are contained twice, once for the first and once for the second product offer
- id: unique id of the product offer (int)
- category: product category (string)
- cluster_id: id of the product cluster from the original corpus this offer belongs to (int)
- brand: brand of the product (string)
- title: product title (string)
- description: longer product description (string)
- price: price of the product offer (string)
- specTableContent: additional data found in specification tables on the webpage that contains the product offer (string)
### Data Splits
- Computers
- Test set - 1100 pairs
- Small Train set - 2267 pairs
- Small Validation set - 567 pairs
- Medium Train set - 6475 pairs
- Medium Validation set - 1619 pairs
- Large Train set - 26687 pairs
- Large Validation set - 6672 pairs
- XLarge Train set - 54768 pairs
- Xlarge Validation set - 13693 pairs
- Cameras
- Test set - 1100 pairs
- Small Train set - 1508 pairs
- Small Validation set - 378 pairs
- Medium Train set - 4204 pairs
- Medium Validation set - 1051 pairs
- Large Train set - 16028 pairs
- Large Validation set - 4008 pairs
- XLarge Train set - 33821 pairs
- Xlarge Validation set - 8456 pairs
- Watches
- Test set - 1100 pairs
- Small Train set - 1804 pairs
- Small Validation set - 451 pairs
- Medium Train set - 5130 pairs
- Medium Validation set - 1283 pairs
- Large Train set - 21621 pairs
- Large Validation set - 5406 pairs
- XLarge Train set - 49255 pairs
- Xlarge Validation set - 12314 pairs
- Shoes
- Test set - 1100 pairs
- Small Train set - 1650 pairs
- Small Validation set - 413 pairs
- Medium Train set - 4644 pairs
- Medium Validation set - 1161 pairs
- Large Train set - 18391 pairs
- Large Validation set - 4598 pairs
- XLarge Train set - 33943 pairs
- Xlarge Validation set - 8486 pairs
## Dataset Creation
### Annotations
#### Annotation process
- Training and Validation sets: distant supervision via shared schema.org product IDs
- Test sets: Single expert annotator
#### Who are the annotators?
[Ralph Peeters](https://www.uni-mannheim.de/dws/people/researchers/phd-students/ralph-peeters/)
## Additional Information
### Citation Information
```
@inproceedings{primpeli2019wdc,
title={The WDC training dataset and gold standard for large-scale product matching},
author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={381--386},
year={2019}
}
```
|
maxidl/LeoLM-HellaSwag_de-fixed | ---
dataset_info:
features:
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
- name: activity_label_de
dtype: string
- name: ctx_de
dtype: string
- name: endings_de
sequence: string
- name: translation_de
dtype: string
splits:
- name: train
num_bytes: 1592717.0
num_examples: 1000
- name: validation
num_bytes: 31395374.887970522
num_examples: 10035
download_size: 18513367
dataset_size: 32988091.887970522
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
The [LeoLM/HellaSwag_de](https://huggingface.co/datasets/LeoLM/HellaSwag_de) dataset, but removed rows where the number of endings differs between English and German.
The fix applied:
```
import datasets as hfds
ds = hfds.load_dataset("LeoLM/HellaSwag_de")
ds = ds.filter(lambda row: len(row["endings"]) == len(row["endings_de"]))
ds.push_to_hub("maxidl/LeoLM-HellaSwag_de-fixed")
```
This removes 7 examples from the validation set.
|
open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-v3 | ---
pretty_name: Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheTravellingEngineer/llama2-7b-chat-hf-v3](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-v3)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T22:05:13.149826](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-v3/blob/main/results_2023-10-21T22-05-13.149826.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 \"em\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.00041913301788269335,\n \"f1\": 0.057038590604026815,\n\
\ \"f1_stderr\": 0.0013245352325167287,\n \"acc\": 0.3977448774004846,\n\
\ \"acc_stderr\": 0.009465436524136249\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269335,\n\
\ \"f1\": 0.057038590604026815,\n \"f1_stderr\": 0.0013245352325167287\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05989385898407885,\n \
\ \"acc_stderr\": 0.006536148151288697\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T19_25_46.830235
path:
- '**/details_harness|drop|3_2023-10-16T19-25-46.830235.parquet'
- split: 2023_10_21T22_05_13.149826
path:
- '**/details_harness|drop|3_2023-10-21T22-05-13.149826.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T22-05-13.149826.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T19_25_46.830235
path:
- '**/details_harness|gsm8k|5_2023-10-16T19-25-46.830235.parquet'
- split: 2023_10_21T22_05_13.149826
path:
- '**/details_harness|gsm8k|5_2023-10-21T22-05-13.149826.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T22-05-13.149826.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:37:31.585910.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:37:31.585910.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:37:31.585910.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T19_25_46.830235
path:
- '**/details_harness|winogrande|5_2023-10-16T19-25-46.830235.parquet'
- split: 2023_10_21T22_05_13.149826
path:
- '**/details_harness|winogrande|5_2023-10-21T22-05-13.149826.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T22-05-13.149826.parquet'
- config_name: results
data_files:
- split: 2023_08_17T18_37_31.585910
path:
- results_2023-08-17T18:37:31.585910.parquet
- split: 2023_10_16T19_25_46.830235
path:
- results_2023-10-16T19-25-46.830235.parquet
- split: 2023_10_21T22_05_13.149826
path:
- results_2023-10-21T22-05-13.149826.parquet
- split: latest
path:
- results_2023-10-21T22-05-13.149826.parquet
---
# Dataset Card for Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-v3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheTravellingEngineer/llama2-7b-chat-hf-v3](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 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 agregated 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_TheTravellingEngineer__llama2-7b-chat-hf-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T22:05:13.149826](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-v3/blob/main/results_2023-10-21T22-05-13.149826.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": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269335,
"f1": 0.057038590604026815,
"f1_stderr": 0.0013245352325167287,
"acc": 0.3977448774004846,
"acc_stderr": 0.009465436524136249
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269335,
"f1": 0.057038590604026815,
"f1_stderr": 0.0013245352325167287
},
"harness|gsm8k|5": {
"acc": 0.05989385898407885,
"acc_stderr": 0.006536148151288697
},
"harness|winogrande|5": {
"acc": 0.7355958958168903,
"acc_stderr": 0.012394724896983799
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
michaelginn/guarani | ---
dataset_info:
features:
- name: glottocode
dtype: string
- name: metalang_glottocode
dtype: string
- name: is_segmented
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: ID
dtype: string
- name: transcription
dtype: string
- name: glosses
dtype: string
- name: translation
dtype: string
splits:
- name: train
num_bytes: 412977
num_examples: 1606
download_size: 116518
dataset_size: 412977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guarani"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wbxlala/Dreamer_Valence_shuffled | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 499671504.0
num_examples: 414
download_size: 492782677
dataset_size: 499671504.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
brainer/law-term | ---
configs:
- config_name: default
data_files:
- split: raw
path: data/raw-*
- split: label
path: data/label-*
- config_name: law/train
data_files:
- split: raw
path: law/train/raw-*
- split: label
path: law/train/label-*
- config_name: law/validation
data_files:
- split: raw
path: law/validation/raw-*
- split: label
path: law/validation/label-*
- config_name: term/train
data_files:
- split: train
path: term/train/train-*
- config_name: term/validation
data_files:
- split: train
path: term/validation/train-*
- config_name: train
data_files:
- split: raw
path: train/raw-*
- split: label
path: train/label-*
- config_name: validation
data_files:
- split: raw
path: validation/raw-*
- split: label
path: validation/label-*
dataset_info:
- config_name: default
features:
- name: info
struct:
- name: caseField
dtype: string
- name: caseNm
dtype: string
- name: caseNo
dtype: string
- name: courtNm
dtype: string
- name: detailField
dtype: string
- name: judmnAdjuDe
dtype: string
- name: qotatPrcdnt
sequence: string
- name: relateLaword
sequence: string
- name: trailField
dtype: string
- name: concerned
struct:
- name: acusr
dtype: string
- name: dedat
dtype: string
- name: org
struct:
- name: orgJdgmnAdjuDe
dtype: string
- name: orgJdgmnCaseNo
dtype: string
- name: orgJdgmnCourtNm
dtype: string
- name: disposal
struct:
- name: disposalcontent
sequence: string
- name: disposalform
dtype: string
- name: mentionedItems
struct:
- name: rqestObjet
sequence: string
- name: assrs
struct:
- name: acusrAssrs
sequence: string
- name: dedatAssrs
sequence: string
- name: facts
struct:
- name: bsisFacts
sequence: string
- name: dcss
struct:
- name: courtDcss
sequence: string
- name: close
struct:
- name: cnclsns
sequence: string
splits:
- name: raw
num_bytes: 10516539
num_examples: 1000
- name: label
num_bytes: 10516539
num_examples: 1000
download_size: 9908098
dataset_size: 21033078
- config_name: law/train
features:
- name: info
struct:
- name: caseField
dtype: string
- name: caseNm
dtype: string
- name: caseNo
dtype: string
- name: courtNm
dtype: string
- name: detailField
dtype: string
- name: judmnAdjuDe
dtype: string
- name: qotatPrcdnt
sequence: string
- name: relateLaword
sequence: string
- name: trailField
dtype: string
- name: concerned
struct:
- name: acusr
dtype: string
- name: dedat
dtype: string
- name: org
struct:
- name: orgJdgmnAdjuDe
dtype: string
- name: orgJdgmnCaseNo
dtype: string
- name: orgJdgmnCourtNm
dtype: string
- name: disposal
struct:
- name: disposalcontent
sequence: string
- name: disposalform
dtype: string
- name: mentionedItems
struct:
- name: rqestObjet
sequence: string
- name: assrs
struct:
- name: acusrAssrs
sequence: string
- name: dedatAssrs
sequence: string
- name: facts
struct:
- name: bsisFacts
sequence: string
- name: dcss
struct:
- name: courtDcss
sequence: string
- name: close
struct:
- name: cnclsns
sequence: string
splits:
- name: raw
num_bytes: 1245935
num_examples: 107
- name: label
num_bytes: 1245935
num_examples: 107
download_size: 1217908
dataset_size: 2491870
- config_name: law/validation
features:
- name: info
struct:
- name: caseField
dtype: string
- name: caseNm
dtype: string
- name: caseNo
dtype: string
- name: courtNm
dtype: string
- name: detailField
dtype: string
- name: judmnAdjuDe
dtype: string
- name: qotatPrcdnt
sequence: string
- name: relateLaword
sequence: string
- name: trailField
dtype: string
- name: concerned
struct:
- name: acusr
dtype: string
- name: dedat
dtype: string
- name: org
struct:
- name: orgJdgmnAdjuDe
dtype: string
- name: orgJdgmnCaseNo
dtype: string
- name: orgJdgmnCourtNm
dtype: string
- name: disposal
struct:
- name: disposalcontent
sequence: string
- name: disposalform
dtype: string
- name: mentionedItems
struct:
- name: rqestObjet
sequence: string
- name: assrs
struct:
- name: acusrAssrs
sequence: string
- name: dedatAssrs
sequence: string
- name: facts
struct:
- name: bsisFacts
sequence: string
- name: dcss
struct:
- name: courtDcss
sequence: string
- name: close
struct:
- name: cnclsns
sequence: string
splits:
- name: raw
num_bytes: 10516539
num_examples: 1000
- name: label
num_bytes: 10516539
num_examples: 1000
download_size: 9908098
dataset_size: 21033078
- config_name: term/train
features:
- name: category
dtype: string
- name: name
dtype: string
- name: cn
dtype: string
splits:
- name: train
num_bytes: 186613726
num_examples: 7847
download_size: 82250437
dataset_size: 186613726
- config_name: term/validation
features:
- name: category
dtype: string
- name: name
dtype: string
- name: cn
dtype: string
splits:
- name: train
num_bytes: 19944422
num_examples: 986
download_size: 8955609
dataset_size: 19944422
- config_name: train
features:
- name: info
struct:
- name: caseField
dtype: string
- name: caseNm
dtype: string
- name: caseNo
dtype: string
- name: courtNm
dtype: string
- name: detailField
dtype: string
- name: judmnAdjuDe
dtype: string
- name: qotatPrcdnt
sequence: string
- name: relateLaword
sequence: string
- name: trailField
dtype: string
- name: concerned
struct:
- name: acusr
dtype: string
- name: dedat
dtype: string
- name: org
struct:
- name: orgJdgmnAdjuDe
dtype: string
- name: orgJdgmnCaseNo
dtype: string
- name: orgJdgmnCourtNm
dtype: string
- name: disposal
struct:
- name: disposalcontent
sequence: string
- name: disposalform
dtype: string
- name: mentionedItems
struct:
- name: rqestObjet
sequence: string
- name: assrs
struct:
- name: acusrAssrs
sequence: string
- name: dedatAssrs
sequence: string
- name: facts
struct:
- name: bsisFacts
sequence: string
- name: dcss
struct:
- name: courtDcss
sequence: string
- name: close
struct:
- name: cnclsns
sequence: string
splits:
- name: raw
num_bytes: 1245935
num_examples: 107
- name: label
num_bytes: 1245935
num_examples: 107
download_size: 1217908
dataset_size: 2491870
- config_name: validation
features:
- name: info
struct:
- name: caseField
dtype: string
- name: caseNm
dtype: string
- name: caseNo
dtype: string
- name: courtNm
dtype: string
- name: detailField
dtype: string
- name: judmnAdjuDe
dtype: string
- name: qotatPrcdnt
sequence: string
- name: relateLaword
sequence: string
- name: trailField
dtype: string
- name: concerned
struct:
- name: acusr
dtype: string
- name: dedat
dtype: string
- name: org
struct:
- name: orgJdgmnAdjuDe
dtype: string
- name: orgJdgmnCaseNo
dtype: string
- name: orgJdgmnCourtNm
dtype: string
- name: disposal
struct:
- name: disposalcontent
sequence: string
- name: disposalform
dtype: string
- name: mentionedItems
struct:
- name: rqestObjet
sequence: string
- name: assrs
struct:
- name: acusrAssrs
sequence: string
- name: dedatAssrs
sequence: string
- name: facts
struct:
- name: bsisFacts
sequence: string
- name: dcss
struct:
- name: courtDcss
sequence: string
- name: close
struct:
- name: cnclsns
sequence: string
splits:
- name: raw
num_bytes: 10516539
num_examples: 1000
- name: label
num_bytes: 10516539
num_examples: 1000
download_size: 9908098
dataset_size: 21033078
---
# Dataset Card for "law"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
```
DatasetDict({
law: DatasetDict({
train: DatasetDict({
raw: Dataset({
features: ['info', 'concerned', 'org', 'disposal', 'mentionedItems', 'assrs', 'facts', 'dcss', 'close'],
num_rows: 107
})
label: Dataset({
features: ['info', 'concerned', 'org', 'disposal', 'mentionedItems', 'assrs', 'facts', 'dcss', 'close'],
num_rows: 107
})
})
validation: DatasetDict({
raw: Dataset({
features: ['info', 'concerned', 'org', 'disposal', 'mentionedItems', 'assrs', 'facts', 'dcss', 'close'],
num_rows: 1000
})
label: Dataset({
features: ['info', 'concerned', 'org', 'disposal', 'mentionedItems', 'assrs', 'facts', 'dcss', 'close'],
num_rows: 1000
})
})
})
term: DatasetDict({
train: Dataset({
features: ['category', 'name', 'cn'],
num_rows: 7847
})
validation: Dataset({
features: ['category', 'name', 'cn'],
num_rows: 986
})
})
})
``` |
presencesw/dataset_2000_decompese_question_0 | ---
dataset_info:
features:
- name: entities
sequence: 'null'
- name: triplets
list:
- name: question
dtype: string
- name: answer
dtype: string
- name: complex_question
dtype: string
splits:
- name: train
num_bytes: 70060
num_examples: 199
download_size: 26888
dataset_size: 70060
---
# Dataset Card for "dataset_2000_decompese_question_0"
The dataset has struct
```json
{
"complex_question": "Does Mercury help detect coronavirus?",
"entities": ["Mercury", "coronavirus"],
"triples": [
{
"question": "What is the name of the coronavirus?",
"evidence": "str...",
"answer": "The coronavirus is called COVID-19"
},
{
"question": "Does Mercury help detect COVID-19?",
"evidence": [
"",
"",
""
],
"answer": "Mercury does not help detect COVID-19"
},
{
"question": "What is mercury used to detect?",
"evidence": "str...",
"answer": "Mercury is used to detect the temperature of things"
},
{
"question": "What are some symtoms of coronavirus?",
"evidence": "str...",
"answer": "Common symtoms of coronavirus are fever..."
}
],
"answer": "Yes, ..."
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edsealing/reverse_squad | ---
dataset_info:
features:
- name: numqas
dtype: int64
- name: context
dtype: string
- name: qas
dtype: string
splits:
- name: train
num_bytes: 28879823
num_examples: 18896
download_size: 15715321
dataset_size: 28879823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "reverse_squad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Felladrin/ChatML-H4rmony_dpo | ---
license: mit
task_categories:
- question-answering
- text-classification
- reinforcement-learning
- text-generation
tags:
- ecolinguistics
- ecology
- sustainability
- environment
- synthetic
size_categories:
- 1K<n<10K
---
[neovalle/H4rmony_dpo](https://huggingface.co/datasets/neovalle/H4rmony_dpo) in ChatML format, ready to use in [HuggingFace TRL's DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer).
Python code used for conversion:
```python
from datasets import load_dataset
dataset = load_dataset("neovalle/H4rmony_dpo", split="train")
def format(columns):
return {
"prompt": f"<|im_start|>user\n{columns['prompt']}<|im_end|>\n<|im_start|>assistant\n",
"chosen": f"{columns['chosen']}<|im_end|>",
"rejected": f"{columns['rejected']}<|im_end|>",
}
dataset.map(format).to_parquet("train.parquet")
```
|
CyberHarem/smia_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of smia (Fire Emblem)
This is the dataset of smia (Fire Emblem), containing 111 images and their tags.
The core tags of this character are `long_hair, brown_hair, breasts, brown_eyes, large_breasts, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 111 | 125.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 111 | 74.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 258 | 156.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 111 | 110.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 258 | 214.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/smia_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | hetero, blush, nipples, 1boy, 1girl, penis, sex, solo_focus, vaginal, open_mouth, cum_in_pussy, navel, completely_nude, straddling, sweat, girl_on_top, spread_legs, uncensored |
| 1 | 6 |  |  |  |  |  | 1girl, navel, nipples, solo, female_pubic_hair, looking_at_viewer, armpits, smile, blush, completely_nude, open_mouth, pussy |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hetero | blush | nipples | 1boy | 1girl | penis | sex | solo_focus | vaginal | open_mouth | cum_in_pussy | navel | completely_nude | straddling | sweat | girl_on_top | spread_legs | uncensored | solo | female_pubic_hair | looking_at_viewer | armpits | smile | pussy |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:--------|:----------|:-------|:--------|:--------|:------|:-------------|:----------|:-------------|:---------------|:--------|:------------------|:-------------|:--------|:--------------|:--------------|:-------------|:-------|:--------------------|:--------------------|:----------|:--------|:--------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 6 |  |  |  |  |  | | X | X | | X | | | | | X | | X | X | | | | | | X | X | X | X | X | X |
|
archie-kay/Gen-ZifAI | ---
license: openrail
---
|
ReacherTN/malawi-llm-datasetv2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 431323
num_examples: 748
download_size: 212285
dataset_size: 431323
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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