datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
asgaardlab/SampleDataset2 | ---
dataset_info:
features:
- name: Buggy Image
dtype: image
- name: Correct Image
dtype: image
- name: Segmentation Image (Bug)
dtype: image
- name: Segmentation Image (Correct)
dtype: image
- name: Description
dtype: string
- name: Tag
dtype: string
- name: Objects JSON (Bug)
dtype: string
- name: Objects JSON (Correct)
dtype: string
- name: Victim Name
dtype: string
- name: Victim Color
sequence: int64
splits:
- name: validation
num_bytes: 404705601.0
num_examples: 751
download_size: 379820267
dataset_size: 404705601.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "SampleDataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
matlok/python-text-copilot-training-instruct-ai-research-2024-02-11 | ---
license:
- other
pretty_name: >-
2024-02-11 - python copilot instructions on how to code using alpaca and yaml
dataset_info:
- config_name: autogen
splits:
- name: view_schema
configs:
- config_name: autogen
data_files:
- split: view_schema
path: schema/train-0001-autogen-autogen.parquet
size_categories:
- 1M<n<10M
tags:
- python-copilot
- python-coding
- python-architecture
- knowledge-graphs
- multimodal
- text-image-audio
- fine-tuning
- training
- question-answering
- image-knowledge-graph
- alpaca
- mp3
- png
- text
- instruct
- coding
- task
- prompt
- response
- yaml
# supported task_categories
# text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other
task_categories:
- text-generation
- question-answering
# supported task_ids
# acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
task_ids:
- parsing
---
## Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the [Autogen](https://github.com/microsoft/autogen/tree/main) and multimodal **Qwen AI** project:
- [Qwen](https://github.com/QwenLM/Qwen)
- [Qwen Agent](https://github.com/QwenLM/Qwen-Agent)
- [Qwen VL Chat](https://github.com/QwenLM/Qwen-VL)
- [Qwen Audio](https://github.com/QwenLM/Qwen-Audio)
This dataset is the 2024-02-11 update for the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset.
### Details
Each row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.
- Rows: 1075795
- Size: 1.8 GB
- Data type: instruct
- Format: Introduction on code usage using alpaca and yaml response
- Number of python repos: 1275
### How to use the datasets
#### Load Autogen Schema Dataset
```python
from datasets import load_dataset
ds_name = (
"matlok"
"/"
"python-text-copilot-training-"
"instruct-ai-research-"
"2024-02-11"
)
dc = "autogen"
ds = load_dataset(ds_name, dc, verification_mode="no_checks")
print(f"ds={ds_name} dataset_config={dc} has {len(ds['view_schema']['file_path'])} unique python modules")
```
```
dataset_config=autogen has 130 unique python modules
```
### Schema
The instruction alpaca text with yaml response is in the **desc** column:
```json
{
"active": "bool",
"args": "string",
"args_len": "float64",
"audio_file": "string",
"audio_path": "string",
"class_bases": "string",
"class_name": "string",
"code": "string",
"code_len": "float64",
"desc": "string",
"desc_docstr": "string",
"desc_docstr_len": "float64",
"desc_len": "int64",
"docstr": "string",
"docstr_len": "int64",
"file_path": "string",
"file_type": "string",
"function_names": "string",
"gen_bytes": "int64",
"gen_data_type": "string",
"gen_mode": "string",
"gen_size": "int64",
"gen_valid": "bool",
"height": "int64",
"image_file": "string",
"image_path": "string",
"method_names": "string",
"name": "string",
"num_all_bases": "int64",
"num_bases": "int64",
"num_classes": "int64",
"num_functions": "float64",
"num_imports": "int64",
"num_methods": "float64",
"prompts": "string",
"raises": "string",
"raises_len": "float64",
"recsize": "int64",
"repo": "string",
"returns": "string",
"returns_len": "float64",
"size": "int64",
"src_object": "string",
"total_objects": "int64",
"usage": "string",
"usages": "string",
"width": "int64"
}
```
|
HarborYuan/Few-Shot-Class-Incremental-Learning | ---
license: other
---
|
open-llm-leaderboard/details_vicgalle__gpt2-alpaca-gpt4 | ---
pretty_name: Evaluation run of vicgalle/gpt2-alpaca-gpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vicgalle/gpt2-alpaca-gpt4](https://huggingface.co/vicgalle/gpt2-alpaca-gpt4)\
\ 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 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 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_vicgalle__gpt2-alpaca-gpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T08:11:17.165801](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca-gpt4/blob/main/results_2023-10-13T08-11-17.165801.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\": 0.003145973154362416,\n\
\ \"em_stderr\": 0.0005734993648436451,\n \"f1\": 0.0483462667785236,\n\
\ \"f1_stderr\": 0.0013978558370896523,\n \"acc\": 0.26236870748869207,\n\
\ \"acc_stderr\": 0.007776906388854586\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436451,\n\
\ \"f1\": 0.0483462667785236,\n \"f1_stderr\": 0.0013978558370896523\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \
\ \"acc_stderr\": 0.0015145735612245457\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5217048145224941,\n \"acc_stderr\": 0.014039239216484626\n\
\ }\n}\n```"
repo_url: https://huggingface.co/vicgalle/gpt2-alpaca-gpt4
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_07_19T10_37_55.436253
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T08_11_17.165801
path:
- '**/details_harness|drop|3_2023-10-13T08-11-17.165801.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T08-11-17.165801.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T08_11_17.165801
path:
- '**/details_harness|gsm8k|5_2023-10-13T08-11-17.165801.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T08-11-17.165801.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:37:55.436253.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:37:55.436253.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:37:55.436253.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T08_11_17.165801
path:
- '**/details_harness|winogrande|5_2023-10-13T08-11-17.165801.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T08-11-17.165801.parquet'
- config_name: results
data_files:
- split: 2023_07_19T10_37_55.436253
path:
- results_2023-07-19T10:37:55.436253.parquet
- split: 2023_10_13T08_11_17.165801
path:
- results_2023-10-13T08-11-17.165801.parquet
- split: latest
path:
- results_2023-10-13T08-11-17.165801.parquet
---
# Dataset Card for Evaluation run of vicgalle/gpt2-alpaca-gpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vicgalle/gpt2-alpaca-gpt4
- **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 [vicgalle/gpt2-alpaca-gpt4](https://huggingface.co/vicgalle/gpt2-alpaca-gpt4) 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 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 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_vicgalle__gpt2-alpaca-gpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T08:11:17.165801](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca-gpt4/blob/main/results_2023-10-13T08-11-17.165801.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": 0.003145973154362416,
"em_stderr": 0.0005734993648436451,
"f1": 0.0483462667785236,
"f1_stderr": 0.0013978558370896523,
"acc": 0.26236870748869207,
"acc_stderr": 0.007776906388854586
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436451,
"f1": 0.0483462667785236,
"f1_stderr": 0.0013978558370896523
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245457
},
"harness|winogrande|5": {
"acc": 0.5217048145224941,
"acc_stderr": 0.014039239216484626
}
}
```
### 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] |
VerminRed/Ngin | ---
license: openrail
---
|
dim/law_stackexchange | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: tags
sequence: string
- name: score
dtype: int64
- name: license
dtype: string
- name: link
dtype: string
- name: question_title
dtype: string
- name: question_body
dtype: string
- name: answers
list:
- name: answer_id
dtype: int64
- name: body
dtype: string
- name: score
dtype: int64
splits:
- name: train
num_bytes: 95966652
num_examples: 24370
download_size: 53517367
dataset_size: 95966652
---
# Dataset Card for "law_stackexchange"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xaviviro/FEDERICO-GARCIA-LORCA-canciones-poemas-romances | ---
language:
- es
pretty_name: Federico García Lorca. Canciones, Poemas y Romances
license: apache-2.0
size_categories:
- n<1K
tags:
- poesia
- lorca
---
# Federico García Lorca. Canciones, Poemas y Romances |
Sohsa/Vozes | ---
license: openrail
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-66000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1051751
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sumsam/QnA_for_Non-Technical_Roles | ---
license: mit
---
1. **Columns:**
- **Non-Technical Role:** Specifies the role being assessed (e.g., Content Developer).
- **Assessment Domain:** Denotes the skill or attribute being evaluated (e.g., Adaptability).
- **Question:** The actual assessment question.
2. **Content Overview:**
- The dataset is focused on assessing various competencies and skills relevant to non-technical roles.
- Questions are tailored to evaluate how individuals in these roles handle various situations and challenges.
3. **Example Entries:**
- Role: Content Developer, Domain: Adaptability, Question: "How do you adapt your content strategy in response to audience feedback?"
- Role: Content Developer, Domain: Adaptability, Question: "How do you handle unexpected changes in project requirements?"
- Role: Content Developer, Domain: Adaptability, Question: "Can you provide an example of a time you had to quickly adjust your work priorities?"
|
gugaio/notas-fiscais | ---
license: mit
---
|
CyberHarem/fiona_frost_spyxfamily | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Fiona Frost
This is the dataset of Fiona Frost, containing 69 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 69 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 135 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 69 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 69 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 69 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 69 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 69 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 135 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 135 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 135 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
rubendow/xsa | ---
license: artistic-2.0
---
|
shrikant11/myra6-test | ---
dataset_info:
features:
- name: image
dtype: image
- name: agnostic-mask
dtype: image
- name: agnostic-v3.2
dtype: image
- name: cloth
dtype: image
- name: cloth-mask
dtype: image
- name: image-densepose
dtype: image
- name: image-parse-agnostic
dtype: image
- name: image-parse
dtype: image
- name: openpose-image
dtype: image
- name: openpose-json
struct:
- name: people
list:
- name: face_keypoints_2d
sequence: float64
- name: face_keypoints_3d
sequence: 'null'
- name: hand_left_keypoints_2d
sequence: float64
- name: hand_left_keypoints_3d
sequence: 'null'
- name: hand_right_keypoints_2d
sequence: float64
- name: hand_right_keypoints_3d
sequence: 'null'
- name: person_id
sequence: int64
- name: pose_keypoints_2d
sequence: float64
- name: pose_keypoints_3d
sequence: 'null'
- name: version
dtype: float64
splits:
- name: train
num_bytes: 789490313.008
num_examples: 2032
download_size: 731128180
dataset_size: 789490313.008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tr416/dataset_20231007_033716 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73888
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_033716"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nguyenthanhdo/patent_v3.1_switched | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: lang
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 121149124.95088126
num_examples: 100488
download_size: 81169121
dataset_size: 121149124.95088126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "patent_v3.1_switched"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_statistics-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
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: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 9060
num_examples: 5
- name: test
num_bytes: 779208
num_examples: 216
download_size: 18867
dataset_size: 788268
---
# Dataset Card for "mmlu-high_school_statistics-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
insanemyrr/mitochondria_cropped_with_markup | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': testing
'1': training
splits:
- name: train
num_bytes: 77603741.696
num_examples: 1024
- name: test
num_bytes: 77676351.488
num_examples: 1024
download_size: 81276472
dataset_size: 155280093.18400002
---
# Dataset Card for "test-diploma-lucchi-cropped-new-mix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hieule/news_corpus_v2 | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: sapo
dtype: string
- name: cates
sequence: string
- name: publish
dtype: timestamp[us]
- name: text_content
dtype: string
splits:
- name: train
num_bytes: 3228940922
num_examples: 1000001
download_size: 1616424455
dataset_size: 3228940922
---
# Dataset Card for "news_corpus_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_OpenAssistant__llama2-13b-orca-8k-3319 | ---
pretty_name: Evaluation run of OpenAssistant/llama2-13b-orca-8k-3319
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenAssistant/llama2-13b-orca-8k-3319](https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319)\
\ 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 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 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_OpenAssistant__llama2-13b-orca-8k-3319\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T09:37:05.639025](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__llama2-13b-orca-8k-3319/blob/main/results_2023-10-19T09-37-05.639025.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\": 0.07235738255033557,\n\
\ \"em_stderr\": 0.002653208755575334,\n \"f1\": 0.1714293204697988,\n\
\ \"f1_stderr\": 0.0030613909144533535,\n \"acc\": 0.44091694875395904,\n\
\ \"acc_stderr\": 0.010204605702764508\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.07235738255033557,\n \"em_stderr\": 0.002653208755575334,\n\
\ \"f1\": 0.1714293204697988,\n \"f1_stderr\": 0.0030613909144533535\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10993176648976498,\n \
\ \"acc_stderr\": 0.008616195587865418\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663597\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319
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_07_25T11_12_31.858304
path:
- '**/details_harness|arc:challenge|25_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T09_37_05.639025
path:
- '**/details_harness|drop|3_2023-10-19T09-37-05.639025.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T09-37-05.639025.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T09_37_05.639025
path:
- '**/details_harness|gsm8k|5_2023-10-19T09-37-05.639025.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T09-37-05.639025.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hellaswag|10_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T11:12:31.858304.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-25T11:12:31.858304.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-25T11:12:31.858304.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T09_37_05.639025
path:
- '**/details_harness|winogrande|5_2023-10-19T09-37-05.639025.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T09-37-05.639025.parquet'
- config_name: results
data_files:
- split: 2023_07_25T11_12_31.858304
path:
- results_2023-07-25T11:12:31.858304.parquet
- split: 2023_10_19T09_37_05.639025
path:
- results_2023-10-19T09-37-05.639025.parquet
- split: latest
path:
- results_2023-10-19T09-37-05.639025.parquet
---
# Dataset Card for Evaluation run of OpenAssistant/llama2-13b-orca-8k-3319
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319
- **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 [OpenAssistant/llama2-13b-orca-8k-3319](https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319) 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 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 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_OpenAssistant__llama2-13b-orca-8k-3319",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T09:37:05.639025](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__llama2-13b-orca-8k-3319/blob/main/results_2023-10-19T09-37-05.639025.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": 0.07235738255033557,
"em_stderr": 0.002653208755575334,
"f1": 0.1714293204697988,
"f1_stderr": 0.0030613909144533535,
"acc": 0.44091694875395904,
"acc_stderr": 0.010204605702764508
},
"harness|drop|3": {
"em": 0.07235738255033557,
"em_stderr": 0.002653208755575334,
"f1": 0.1714293204697988,
"f1_stderr": 0.0030613909144533535
},
"harness|gsm8k|5": {
"acc": 0.10993176648976498,
"acc_stderr": 0.008616195587865418
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.011793015817663597
}
}
```
### 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] |
communityai/apt_pretrain_textbook_16k-100 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 10168718.903313944
num_examples: 100
download_size: 5120308
dataset_size: 10168718.903313944
---
# Dataset Card for "apt_pretrain_textbook_16k-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Dineth1222/Nova | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_Kukedlc__NeuralStockFusion-7b | ---
pretty_name: Evaluation run of Kukedlc/NeuralStockFusion-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Kukedlc/NeuralStockFusion-7b](https://huggingface.co/Kukedlc/NeuralStockFusion-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Kukedlc__NeuralStockFusion-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T23:41:20.914808](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralStockFusion-7b/blob/main/results_2024-04-15T23-41-20.914808.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\": 0.6537669294625683,\n\
\ \"acc_stderr\": 0.03200535369815168,\n \"acc_norm\": 0.65282963770597,\n\
\ \"acc_norm_stderr\": 0.0326790836047191,\n \"mc1\": 0.5973072215422277,\n\
\ \"mc1_stderr\": 0.01716883093518721,\n \"mc2\": 0.7492020138399351,\n\
\ \"mc2_stderr\": 0.014242292385907867\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7090443686006825,\n \"acc_stderr\": 0.013273077865907592,\n\
\ \"acc_norm\": 0.734641638225256,\n \"acc_norm_stderr\": 0.012902554762313962\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7143995220075682,\n\
\ \"acc_stderr\": 0.004507768029590097,\n \"acc_norm\": 0.8893646683927504,\n\
\ \"acc_norm_stderr\": 0.0031303894668332005\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\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\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.032400380867927465,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.032400380867927465\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944433,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944433\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\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\": 0.7870967741935484,\n\
\ \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n\
\ \"acc_norm_stderr\": 0.023287665127268545\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8529411764705882,\n \"acc_stderr\": 0.024857478080250447,\n \"\
acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.024857478080250447\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.013547415658662255,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.013547415658662255\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069367,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069367\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4491620111731844,\n\
\ \"acc_stderr\": 0.01663583834163192,\n \"acc_norm\": 0.4491620111731844,\n\
\ \"acc_norm_stderr\": 0.01663583834163192\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\
\ \"acc_stderr\": 0.012752858346533126,\n \"acc_norm\": 0.47392438070404175,\n\
\ \"acc_norm_stderr\": 0.012752858346533126\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\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\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5973072215422277,\n\
\ \"mc1_stderr\": 0.01716883093518721,\n \"mc2\": 0.7492020138399351,\n\
\ \"mc2_stderr\": 0.014242292385907867\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8484609313338595,\n \"acc_stderr\": 0.010077698907571764\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7149355572403336,\n \
\ \"acc_stderr\": 0.012435042334904004\n }\n}\n```"
repo_url: https://huggingface.co/Kukedlc/NeuralStockFusion-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: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|arc:challenge|25_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|gsm8k|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hellaswag|10_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T23-41-20.914808.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T23-41-20.914808.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- '**/details_harness|winogrande|5_2024-04-15T23-41-20.914808.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T23-41-20.914808.parquet'
- config_name: results
data_files:
- split: 2024_04_15T23_41_20.914808
path:
- results_2024-04-15T23-41-20.914808.parquet
- split: latest
path:
- results_2024-04-15T23-41-20.914808.parquet
---
# Dataset Card for Evaluation run of Kukedlc/NeuralStockFusion-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kukedlc/NeuralStockFusion-7b](https://huggingface.co/Kukedlc/NeuralStockFusion-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Kukedlc__NeuralStockFusion-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T23:41:20.914808](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralStockFusion-7b/blob/main/results_2024-04-15T23-41-20.914808.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": 0.6537669294625683,
"acc_stderr": 0.03200535369815168,
"acc_norm": 0.65282963770597,
"acc_norm_stderr": 0.0326790836047191,
"mc1": 0.5973072215422277,
"mc1_stderr": 0.01716883093518721,
"mc2": 0.7492020138399351,
"mc2_stderr": 0.014242292385907867
},
"harness|arc:challenge|25": {
"acc": 0.7090443686006825,
"acc_stderr": 0.013273077865907592,
"acc_norm": 0.734641638225256,
"acc_norm_stderr": 0.012902554762313962
},
"harness|hellaswag|10": {
"acc": 0.7143995220075682,
"acc_stderr": 0.004507768029590097,
"acc_norm": 0.8893646683927504,
"acc_norm_stderr": 0.0031303894668332005
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.04858083574266345,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.032400380867927465,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.032400380867927465
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
"acc_stderr": 0.025331202438944433,
"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.025331202438944433
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.023287665127268545,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268545
},
"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": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
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"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
"acc_stderr": 0.01989934131572178,
"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8529411764705882,
"acc_stderr": 0.024857478080250447,
"acc_norm": 0.8529411764705882,
"acc_norm_stderr": 0.024857478080250447
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290916,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290916
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.046840993210771065,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.046840993210771065
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
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"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406964,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406964
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
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"acc_norm": 0.8263090676883781,
"acc_norm_stderr": 0.013547415658662255
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
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"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069367
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4491620111731844,
"acc_stderr": 0.01663583834163192,
"acc_norm": 0.4491620111731844,
"acc_norm_stderr": 0.01663583834163192
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818733,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818733
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818763,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818763
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
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"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135114
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
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"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.47392438070404175,
"acc_norm_stderr": 0.012752858346533126
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
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"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.028332959514031208
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6781045751633987,
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"acc_norm": 0.6781045751633987,
"acc_norm_stderr": 0.018901015322093092
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
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"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
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"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.02650859065623327
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
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"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5973072215422277,
"mc1_stderr": 0.01716883093518721,
"mc2": 0.7492020138399351,
"mc2_stderr": 0.014242292385907867
},
"harness|winogrande|5": {
"acc": 0.8484609313338595,
"acc_stderr": 0.010077698907571764
},
"harness|gsm8k|5": {
"acc": 0.7149355572403336,
"acc_stderr": 0.012435042334904004
}
}
```
## 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed] |
surrey-nlp/SDU-test | ---
license: cc-by-sa-4.0
---
|
ruanchaves/hatebr_por_Latn_to_eng_Latn | ---
dataset_info:
features:
- name: instagram_comments
dtype: string
- name: offensive_language
dtype: bool
- name: offensiveness_levels
dtype: int32
- name: antisemitism
dtype: bool
- name: apology_for_the_dictatorship
dtype: bool
- name: fatphobia
dtype: bool
- name: homophobia
dtype: bool
- name: partyism
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
- name: offensive_&_non-hate_speech
dtype: bool
- name: non-offensive
dtype: bool
- name: specialist_1_hate_speech
dtype: bool
- name: specialist_2_hate_speech
dtype: bool
- name: specialist_3_hate_speech
dtype: bool
splits:
- name: train
num_bytes: 391589
num_examples: 4480
- name: validation
num_bytes: 86759
num_examples: 1120
- name: test
num_bytes: 111044
num_examples: 1400
download_size: 0
dataset_size: 589392
---
# Dataset Card for "hatebr_por_Latn_to_eng_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Pidornakrilce/76544 | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_fblgit__LUNA-SOLARkrautLM-Instruct | ---
pretty_name: Evaluation run of fblgit/LUNA-SOLARkrautLM-Instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fblgit/LUNA-SOLARkrautLM-Instruct](https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct)\
\ 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_fblgit__LUNA-SOLARkrautLM-Instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-27T13:04:58.261893](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__LUNA-SOLARkrautLM-Instruct/blob/main/results_2023-12-27T13-04-58.261893.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\": 0.6642541203854099,\n\
\ \"acc_stderr\": 0.0317093464542955,\n \"acc_norm\": 0.6656901555387255,\n\
\ \"acc_norm_stderr\": 0.03234983203431538,\n \"mc1\": 0.5826193390452876,\n\
\ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7336752254501507,\n\
\ \"mc2_stderr\": 0.014886399154960954\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6868600682593856,\n \"acc_stderr\": 0.013552671543623497,\n\
\ \"acc_norm\": 0.71160409556314,\n \"acc_norm_stderr\": 0.013238394422428173\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7130053774148576,\n\
\ \"acc_stderr\": 0.004514345547780332,\n \"acc_norm\": 0.8827922724556861,\n\
\ \"acc_norm_stderr\": 0.003210102507177248\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\
\ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\
\ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4894179894179894,\n \"acc_stderr\": 0.025745542276045478,\n \"\
acc_norm\": 0.4894179894179894,\n \"acc_norm_stderr\": 0.025745542276045478\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267826,\n \"\
acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267826\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\
\ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8585858585858586,\n \"acc_stderr\": 0.024825909793343343,\n \"\
acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.024825909793343343\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097113,\n \
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097113\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37777777777777777,\n \"acc_stderr\": 0.029560707392465715,\n \
\ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.029560707392465715\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.028657491285071973,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.028657491285071973\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650157,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650157\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\
acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.02615686752393104,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02615686752393104\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n \
\ \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\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\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\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\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\
\ \"acc_stderr\": 0.014419123980931894,\n \"acc_norm\": 0.7956577266922095,\n\
\ \"acc_norm_stderr\": 0.014419123980931894\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.02353292543104429,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.02353292543104429\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34972067039106147,\n\
\ \"acc_stderr\": 0.015949308790233645,\n \"acc_norm\": 0.34972067039106147,\n\
\ \"acc_norm_stderr\": 0.015949308790233645\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998482,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998482\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262192,\n\
\ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262192\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5212765957446809,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.5212765957446809,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4810951760104302,\n\
\ \"acc_stderr\": 0.012761104871472655,\n \"acc_norm\": 0.4810951760104302,\n\
\ \"acc_norm_stderr\": 0.012761104871472655\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.026917481224377204,\n\
\ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.026917481224377204\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162666,\n \
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162666\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174923,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174923\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5903614457831325,\n\
\ \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n\
\ \"acc_norm_stderr\": 0.038284011150790206\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\": 0.5826193390452876,\n\
\ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7336752254501507,\n\
\ \"mc2_stderr\": 0.014886399154960954\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825897\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6087945413191812,\n \
\ \"acc_stderr\": 0.0134425024027943\n }\n}\n```"
repo_url: https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct
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_12_27T13_04_58.261893
path:
- '**/details_harness|arc:challenge|25_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|gsm8k|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hellaswag|10_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-27T13-04-58.261893.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-27T13-04-58.261893.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- '**/details_harness|winogrande|5_2023-12-27T13-04-58.261893.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-27T13-04-58.261893.parquet'
- config_name: results
data_files:
- split: 2023_12_27T13_04_58.261893
path:
- results_2023-12-27T13-04-58.261893.parquet
- split: latest
path:
- results_2023-12-27T13-04-58.261893.parquet
---
# Dataset Card for Evaluation run of fblgit/LUNA-SOLARkrautLM-Instruct
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [fblgit/LUNA-SOLARkrautLM-Instruct](https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct) 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_fblgit__LUNA-SOLARkrautLM-Instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-27T13:04:58.261893](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__LUNA-SOLARkrautLM-Instruct/blob/main/results_2023-12-27T13-04-58.261893.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": 0.6642541203854099,
"acc_stderr": 0.0317093464542955,
"acc_norm": 0.6656901555387255,
"acc_norm_stderr": 0.03234983203431538,
"mc1": 0.5826193390452876,
"mc1_stderr": 0.017262891063272164,
"mc2": 0.7336752254501507,
"mc2_stderr": 0.014886399154960954
},
"harness|arc:challenge|25": {
"acc": 0.6868600682593856,
"acc_stderr": 0.013552671543623497,
"acc_norm": 0.71160409556314,
"acc_norm_stderr": 0.013238394422428173
},
"harness|hellaswag|10": {
"acc": 0.7130053774148576,
"acc_stderr": 0.004514345547780332,
"acc_norm": 0.8827922724556861,
"acc_norm_stderr": 0.003210102507177248
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595853,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595853
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.743421052631579,
"acc_stderr": 0.0355418036802569,
"acc_norm": 0.743421052631579,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.048108401480826346,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.048108401480826346
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.625531914893617,
"acc_stderr": 0.03163910665367291,
"acc_norm": 0.625531914893617,
"acc_norm_stderr": 0.03163910665367291
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.039966295748767186,
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"acc_norm": 0.4894179894179894,
"acc_norm_stderr": 0.025745542276045478
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.028657491285071973
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm_stderr": 0.03943966699183629
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"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.8366972477064221,
"acc_norm_stderr": 0.01584825580650157
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.03356787758160831,
"acc_norm": 0.5879629629629629,
"acc_norm_stderr": 0.03356787758160831
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.02615686752393104,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.02615686752393104
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.02388438092596567,
"acc_norm": 0.8396624472573839,
"acc_norm_stderr": 0.02388438092596567
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.03114679648297246,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.03114679648297246
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7404580152671756,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.7404580152671756,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.03586594738573974,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.03586594738573974
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.023086635086841407,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.023086635086841407
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7956577266922095,
"acc_stderr": 0.014419123980931894,
"acc_norm": 0.7956577266922095,
"acc_norm_stderr": 0.014419123980931894
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.02353292543104429,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.02353292543104429
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.34972067039106147,
"acc_stderr": 0.015949308790233645,
"acc_norm": 0.34972067039106147,
"acc_norm_stderr": 0.015949308790233645
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7581699346405228,
"acc_stderr": 0.024518195641879334,
"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.02558306248998482,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.02558306248998482
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7808641975308642,
"acc_stderr": 0.023016705640262192,
"acc_norm": 0.7808641975308642,
"acc_norm_stderr": 0.023016705640262192
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5212765957446809,
"acc_stderr": 0.029800481645628693,
"acc_norm": 0.5212765957446809,
"acc_norm_stderr": 0.029800481645628693
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4810951760104302,
"acc_stderr": 0.012761104871472655,
"acc_norm": 0.4810951760104302,
"acc_norm_stderr": 0.012761104871472655
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7316176470588235,
"acc_stderr": 0.026917481224377204,
"acc_norm": 0.7316176470588235,
"acc_norm_stderr": 0.026917481224377204
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162666,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162666
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.04350271442923243,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7551020408163265,
"acc_stderr": 0.027529637440174923,
"acc_norm": 0.7551020408163265,
"acc_norm_stderr": 0.027529637440174923
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482707,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482707
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.032659863237109066,
"acc_norm": 0.88,
"acc_norm_stderr": 0.032659863237109066
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5903614457831325,
"acc_stderr": 0.038284011150790206,
"acc_norm": 0.5903614457831325,
"acc_norm_stderr": 0.038284011150790206
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7719298245614035,
"acc_stderr": 0.032180937956023566,
"acc_norm": 0.7719298245614035,
"acc_norm_stderr": 0.032180937956023566
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5826193390452876,
"mc1_stderr": 0.017262891063272164,
"mc2": 0.7336752254501507,
"mc2_stderr": 0.014886399154960954
},
"harness|winogrande|5": {
"acc": 0.829518547750592,
"acc_stderr": 0.010569021122825897
},
"harness|gsm8k|5": {
"acc": 0.6087945413191812,
"acc_stderr": 0.0134425024027943
}
}
```
## 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. -->
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## Glossary [optional]
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liuyanchen1015/MULTI_VALUE_rte_perfect_slam | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 247897
num_examples: 575
- name: train
num_bytes: 212879
num_examples: 452
download_size: 301596
dataset_size: 460776
---
# Dataset Card for "MULTI_VALUE_rte_perfect_slam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_beberik__Nyxene-v1-11B | ---
pretty_name: Evaluation run of beberik/Nyxene-v1-11B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beberik/Nyxene-v1-11B](https://huggingface.co/beberik/Nyxene-v1-11B) 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_beberik__Nyxene-v1-11B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-08T01:32:44.134734](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-v1-11B/blob/main/results_2023-12-08T01-32-44.134734.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\": 0.6514001005008682,\n\
\ \"acc_stderr\": 0.032003872263626075,\n \"acc_norm\": 0.6548783767702248,\n\
\ \"acc_norm_stderr\": 0.03263791027558859,\n \"mc1\": 0.40636474908200737,\n\
\ \"mc1_stderr\": 0.017193835812093897,\n \"mc2\": 0.5727980289823402,\n\
\ \"mc2_stderr\": 0.015500934892748477\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6476109215017065,\n \"acc_stderr\": 0.01396014260059868,\n\
\ \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729119\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6592312288388767,\n\
\ \"acc_stderr\": 0.004729990807895058,\n \"acc_norm\": 0.8452499502091216,\n\
\ \"acc_norm_stderr\": 0.003609271000593056\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n\
\ \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337145,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337145\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411018,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411018\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43386243386243384,\n \"acc_stderr\": 0.025525034382474898,\n \"\
acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.025525034382474898\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8032258064516129,\n \"acc_stderr\": 0.022616409420742025,\n \"\
acc_norm\": 0.8032258064516129,\n \"acc_norm_stderr\": 0.022616409420742025\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5073891625615764,\n \"acc_stderr\": 0.0351760354036101,\n \"acc_norm\"\
: 0.5073891625615764,\n \"acc_norm_stderr\": 0.0351760354036101\n },\n\
\ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\
: 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473072,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473072\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513536,\n \
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513536\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\
acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\
\ \"acc_stderr\": 0.01346820161406631,\n \"acc_norm\": 0.8288633461047255,\n\
\ \"acc_norm_stderr\": 0.01346820161406631\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257803,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257803\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.36983240223463687,\n\
\ \"acc_stderr\": 0.016145881256056215,\n \"acc_norm\": 0.36983240223463687,\n\
\ \"acc_norm_stderr\": 0.016145881256056215\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875192,\n\
\ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875192\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460852,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460852\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.4634941329856584,\n \"acc_stderr\": 0.012736153390214963,\n\
\ \"acc_norm\": 0.4634941329856584,\n \"acc_norm_stderr\": 0.012736153390214963\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.7095588235294118,\n \"acc_stderr\": 0.027576468622740546,\n \"\
acc_norm\": 0.7095588235294118,\n \"acc_norm_stderr\": 0.027576468622740546\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.01897542792050721,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.01897542792050721\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960238,\n\
\ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960238\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40636474908200737,\n\
\ \"mc1_stderr\": 0.017193835812093897,\n \"mc2\": 0.5727980289823402,\n\
\ \"mc2_stderr\": 0.015500934892748477\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.01144628062926263\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5208491281273692,\n \
\ \"acc_stderr\": 0.013760506094029866\n }\n}\n```"
repo_url: https://huggingface.co/beberik/Nyxene-v1-11B
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_12_08T01_32_44.134734
path:
- '**/details_harness|arc:challenge|25_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|gsm8k|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hellaswag|10_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T01-32-44.134734.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T01-32-44.134734.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- '**/details_harness|winogrande|5_2023-12-08T01-32-44.134734.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-08T01-32-44.134734.parquet'
- config_name: results
data_files:
- split: 2023_12_08T01_32_44.134734
path:
- results_2023-12-08T01-32-44.134734.parquet
- split: latest
path:
- results_2023-12-08T01-32-44.134734.parquet
---
# Dataset Card for Evaluation run of beberik/Nyxene-v1-11B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beberik/Nyxene-v1-11B
- **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 [beberik/Nyxene-v1-11B](https://huggingface.co/beberik/Nyxene-v1-11B) 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_beberik__Nyxene-v1-11B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-08T01:32:44.134734](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-v1-11B/blob/main/results_2023-12-08T01-32-44.134734.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.032003872263626075,
"acc_norm": 0.6548783767702248,
"acc_norm_stderr": 0.03263791027558859,
"mc1": 0.40636474908200737,
"mc1_stderr": 0.017193835812093897,
"mc2": 0.5727980289823402,
"mc2_stderr": 0.015500934892748477
},
"harness|arc:challenge|25": {
"acc": 0.6476109215017065,
"acc_stderr": 0.01396014260059868,
"acc_norm": 0.6749146757679181,
"acc_norm_stderr": 0.013688147309729119
},
"harness|hellaswag|10": {
"acc": 0.6592312288388767,
"acc_stderr": 0.004729990807895058,
"acc_norm": 0.8452499502091216,
"acc_norm_stderr": 0.003609271000593056
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
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"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337145,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337145
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.0358687928008034,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.0358687928008034
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411018
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
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"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
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"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909282
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
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"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
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"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.43386243386243384,
"acc_norm_stderr": 0.025525034382474898
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8032258064516129,
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"acc_norm": 0.8032258064516129,
"acc_norm_stderr": 0.022616409420742025
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.0351760354036101
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
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"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
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"acc_norm_stderr": 0.028057791672989017
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
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"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.020986854593289733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
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"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.028578348365473072,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.028578348365473072
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6638655462184874,
"acc_stderr": 0.03068473711513536,
"acc_norm": 0.6638655462184874,
"acc_norm_stderr": 0.03068473711513536
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5416666666666666,
"acc_stderr": 0.03398110890294636,
"acc_norm": 0.5416666666666666,
"acc_norm_stderr": 0.03398110890294636
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.02450980392156862,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.02450980392156862
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.025530100460233494,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233494
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
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"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
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"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
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"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406957,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406957
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.71,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8288633461047255,
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"acc_norm": 0.8288633461047255,
"acc_norm_stderr": 0.01346820161406631
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6994219653179191,
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"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.024685316867257803
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.36983240223463687,
"acc_stderr": 0.016145881256056215,
"acc_norm": 0.36983240223463687,
"acc_norm_stderr": 0.016145881256056215
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7483660130718954,
"acc_stderr": 0.024848018263875192,
"acc_norm": 0.7483660130718954,
"acc_norm_stderr": 0.024848018263875192
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818763,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818763
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7345679012345679,
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"acc_norm": 0.7345679012345679,
"acc_norm_stderr": 0.024569223600460852
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.029827499313594685
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4634941329856584,
"acc_stderr": 0.012736153390214963,
"acc_norm": 0.4634941329856584,
"acc_norm_stderr": 0.012736153390214963
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7095588235294118,
"acc_stderr": 0.027576468622740546,
"acc_norm": 0.7095588235294118,
"acc_norm_stderr": 0.027576468622740546
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.01897542792050721,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.01897542792050721
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
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"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7510204081632653,
"acc_stderr": 0.027682979522960238,
"acc_norm": 0.7510204081632653,
"acc_norm_stderr": 0.027682979522960238
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.40636474908200737,
"mc1_stderr": 0.017193835812093897,
"mc2": 0.5727980289823402,
"mc2_stderr": 0.015500934892748477
},
"harness|winogrande|5": {
"acc": 0.7900552486187845,
"acc_stderr": 0.01144628062926263
},
"harness|gsm8k|5": {
"acc": 0.5208491281273692,
"acc_stderr": 0.013760506094029866
}
}
```
### 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] |
LangChainDatasets/llm-math | ---
license: mit
---
|
yzhuang/metatree_BNG_hepatitis_ | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 47555800
num_examples: 699350
- name: validation
num_bytes: 20444200
num_examples: 300650
download_size: 62669300
dataset_size: 68000000
---
# Dataset Card for "metatree_BNG_hepatitis_"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DKYoon/slimpajama-200k | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1652798826
num_examples: 200000
download_size: 973077904
dataset_size: 1652798826
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload | ---
pretty_name: Evaluation run of Lajonbot/tableBeluga-7B-instruct-pl-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Lajonbot/tableBeluga-7B-instruct-pl-lora_unload](https://huggingface.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload)\
\ 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 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 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_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T19:20:10.302969](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload/blob/main/results_2023-09-17T19-20-10.302969.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\": 0.07602768456375839,\n\
\ \"em_stderr\": 0.0027142822886132433,\n \"f1\": 0.14862416107382526,\n\
\ \"f1_stderr\": 0.0030033713869214236,\n \"acc\": 0.4151299715828343,\n\
\ \"acc_stderr\": 0.009762520250486784\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.07602768456375839,\n \"em_stderr\": 0.0027142822886132433,\n\
\ \"f1\": 0.14862416107382526,\n \"f1_stderr\": 0.0030033713869214236\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \
\ \"acc_stderr\": 0.007390654481108218\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload
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_03T09_13_12.299308
path:
- '**/details_harness|arc:challenge|25_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T19_20_10.302969
path:
- '**/details_harness|drop|3_2023-09-17T19-20-10.302969.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T19-20-10.302969.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T19_20_10.302969
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-20-10.302969.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-20-10.302969.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hellaswag|10_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T09:13:12.299308.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-03T09:13:12.299308.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-03T09:13:12.299308.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T19_20_10.302969
path:
- '**/details_harness|winogrande|5_2023-09-17T19-20-10.302969.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T19-20-10.302969.parquet'
- config_name: results
data_files:
- split: 2023_08_03T09_13_12.299308
path:
- results_2023-08-03T09:13:12.299308.parquet
- split: 2023_09_17T19_20_10.302969
path:
- results_2023-09-17T19-20-10.302969.parquet
- split: latest
path:
- results_2023-09-17T19-20-10.302969.parquet
---
# Dataset Card for Evaluation run of Lajonbot/tableBeluga-7B-instruct-pl-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload
- **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 [Lajonbot/tableBeluga-7B-instruct-pl-lora_unload](https://huggingface.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload) 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 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 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_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T19:20:10.302969](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload/blob/main/results_2023-09-17T19-20-10.302969.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": 0.07602768456375839,
"em_stderr": 0.0027142822886132433,
"f1": 0.14862416107382526,
"f1_stderr": 0.0030033713869214236,
"acc": 0.4151299715828343,
"acc_stderr": 0.009762520250486784
},
"harness|drop|3": {
"em": 0.07602768456375839,
"em_stderr": 0.0027142822886132433,
"f1": 0.14862416107382526,
"f1_stderr": 0.0030033713869214236
},
"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108218
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.01213438601986535
}
}
```
### 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] |
CyberHarem/modernia_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of modernia/モダニア/神罚/모더니아 (Nikke: Goddess of Victory)
This is the dataset of modernia/モダニア/神罚/모더니아 (Nikke: Goddess of Victory), containing 135 images and their tags.
The core tags of this character are `long_hair, breasts, red_eyes, large_breasts, bangs, grey_hair, ribbon, white_hair, hair_ribbon`, 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 | 135 | 232.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/modernia_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 135 | 116.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/modernia_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 333 | 254.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/modernia_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 135 | 197.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/modernia_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 333 | 392.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/modernia_nikke/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/modernia_nikke',
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 |  |  |  |  |  | 1boy, 1girl, blush, hetero, mosaic_censoring, penis, fingerless_gloves, looking_at_viewer, pov, solo_focus, open_mouth, fellatio, nude, bandages, tongue |
| 1 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, blush, bodysuit, cleavage, smile, solo, upper_body, closed_mouth, simple_background, white_background, bandages, black_gloves, covered_navel, dated |
| 2 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, smile, black_gloves, covered_navel, hairband, blush, armor, bodysuit, closed_mouth, fingerless_gloves, open_mouth |
| 3 | 20 |  |  |  |  |  | 1girl, solo, white_shirt, cleavage, smile, looking_at_viewer, collarbone, blush, long_sleeves, off_shoulder, open_mouth, bare_shoulders, white_background, simple_background, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | blush | hetero | mosaic_censoring | penis | fingerless_gloves | looking_at_viewer | pov | solo_focus | open_mouth | fellatio | nude | bandages | tongue | bodysuit | cleavage | smile | solo | upper_body | closed_mouth | simple_background | white_background | black_gloves | covered_navel | dated | hairband | armor | white_shirt | collarbone | long_sleeves | off_shoulder | bare_shoulders |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:--------|:---------|:-------------------|:--------|:--------------------|:--------------------|:------|:-------------|:-------------|:-----------|:-------|:-----------|:---------|:-----------|:-----------|:--------|:-------|:-------------|:---------------|:--------------------|:-------------------|:---------------|:----------------|:--------|:-----------|:--------|:--------------|:-------------|:---------------|:---------------|:-----------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | | X | X | | | | | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 2 | 12 |  |  |  |  |  | | X | X | | | | X | X | | | X | | | | | X | X | X | X | | X | | | X | X | | X | X | | | | | |
| 3 | 20 |  |  |  |  |  | | X | X | | | | | X | | | X | | | | | | X | X | X | X | | X | X | | | | | | X | X | X | X | X |
|
joey234/mmlu-high_school_european_history-dev | ---
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: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 22339
num_examples: 5
download_size: 0
dataset_size: 22339
---
# Dataset Card for "mmlu-high_school_european_history-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jacksprat/TCGA_TNM_examples | ---
license: apache-2.0
---
|
Back-up/ds_100 | ---
dataset_info:
features:
- name: url
dtype: string
- name: text
dtype: string
- name: perplexity
dtype: float64
- name: num_char
dtype: string
- name: num_word
dtype: string
splits:
- name: train
num_bytes: 248497860.6836914
num_examples: 10391
download_size: 127597007
dataset_size: 248497860.6836914
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sacralet/llama_chat_nesting_dataset | ---
license: apache-2.0
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 188532878
num_examples: 28000
download_size: 29064280
dataset_size: 188532878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
timm/objectnet | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': air_freshener
'1': alarm_clock
'2': backpack
'3': baking_sheet
'4': banana
'5': band_aid
'6': baseball_bat
'7': baseball_glove
'8': basket
'9': bathrobe
'10': battery
'11': bed_sheet
'12': beer_bottle
'13': beer_can
'14': belt
'15': bench
'16': bicycle
'17': bike_pump
'18': bills_money
'19': binder_closed
'20': biscuits
'21': blanket
'22': blender
'23': blouse
'24': board_game
'25': book_closed
'26': bookend
'27': boots
'28': bottle_cap
'29': bottle_opener
'30': bottle_stopper
'31': box
'32': bracelet
'33': bread_knife
'34': bread_loaf
'35': briefcase
'36': brooch
'37': broom
'38': bucket
'39': butchers_knife
'40': butter
'41': button
'42': calendar
'43': can_opener
'44': candle
'45': canned_food
'46': cd_case
'47': cellphone
'48': cellphone_case
'49': cellphone_charger
'50': cereal
'51': chair
'52': cheese
'53': chess_piece
'54': chocolate
'55': chopstick
'56': clothes_hamper
'57': clothes_hanger
'58': coaster
'59': coffee_beans
'60': coffee_french_press
'61': coffee_grinder
'62': coffee_machine
'63': coffee_table
'64': coin_money
'65': comb
'66': combination_lock
'67': computer_mouse
'68': contact_lens_case
'69': cooking_oil_bottle
'70': cork
'71': cutting_board
'72': deodorant
'73': desk_lamp
'74': detergent
'75': dish_soap
'76': document_folder_closed
'77': dog_bed
'78': doormat
'79': drawer_open
'80': dress
'81': dress_pants
'82': dress_shirt
'83': dress_shoe_men
'84': dress_shoe_women
'85': drill
'86': drinking_cup
'87': drinking_straw
'88': drying_rack_for_clothes
'89': drying_rack_for_dishes
'90': dust_pan
'91': dvd_player
'92': earbuds
'93': earring
'94': egg
'95': egg_carton
'96': envelope
'97': eraser_white_board
'98': extension_cable
'99': eyeglasses
'100': fan
'101': figurine_or_statue
'102': first_aid_kit
'103': flashlight
'104': floss_container
'105': flour_container
'106': fork
'107': frying_pan
'108': full_sized_towel
'109': glue_container
'110': hair_brush
'111': hair_dryer
'112': hairclip
'113': hairtie
'114': hammer
'115': hand_mirror
'116': hand_towel_or_rag
'117': handbag
'118': hat
'119': headphones_over_ear
'120': helmet
'121': honey_container
'122': ice
'123': ice_cube_tray
'124': iron_for_clothes
'125': ironing_board
'126': jam
'127': jar
'128': jeans
'129': kettle
'130': key_chain
'131': keyboard
'132': ladle
'133': lampshade
'134': laptop_charger
'135': laptop_open
'136': leaf
'137': leggings
'138': lemon
'139': letter_opener
'140': lettuce
'141': light_bulb
'142': lighter
'143': lipstick
'144': loofah
'145': magazine
'146': makeup
'147': makeup_brush
'148': marker
'149': match
'150': measuring_cup
'151': microwave
'152': milk
'153': mixing_salad_bowl
'154': monitor
'155': mouse_pad
'156': mouthwash
'157': mug
'158': multitool
'159': nail_clippers
'160': nail_fastener
'161': nail_file
'162': nail_polish
'163': napkin
'164': necklace
'165': newspaper
'166': night_light
'167': nightstand
'168': notebook
'169': notepad
'170': nut_for_screw
'171': orange
'172': oven_mitts
'173': padlock
'174': paint_can
'175': paintbrush
'176': paper
'177': paper_bag
'178': paper_plates
'179': paper_towel
'180': paperclip
'181': peeler
'182': pen
'183': pencil
'184': pepper_shaker
'185': pet_food_container
'186': phone_landline
'187': photograph_printed
'188': pill_bottle
'189': pill_organizer
'190': pillow
'191': pitcher
'192': placemat
'193': plastic_bag
'194': plastic_cup
'195': plastic_wrap
'196': plate
'197': playing_cards
'198': pliers
'199': plunger
'200': pop_can
'201': portable_heater
'202': poster
'203': power_bar
'204': power_cable
'205': printer
'206': raincoat
'207': rake
'208': razor
'209': receipt
'210': remote_control
'211': removable_blade
'212': ribbon
'213': ring
'214': rock
'215': rolling_pin
'216': ruler
'217': running_shoe
'218': safety_pin
'219': salt_shaker
'220': sandal
'221': scarf
'222': scissors
'223': screw
'224': scrub_brush
'225': sewing_kit
'226': shampoo_bottle
'227': shoelace
'228': shorts
'229': shovel
'230': skateboard
'231': skirt
'232': sleeping_bag
'233': slipper
'234': soap_bar
'235': soap_dispenser
'236': sock
'237': soup_bowl
'238': spatula
'239': speaker
'240': sponge
'241': spoon
'242': spray_bottle
'243': squeegee
'244': squeeze_bottle
'245': standing_lamp
'246': stapler
'247': step_stool
'248': still_camera
'249': stopper_sink_tub
'250': strainer
'251': stuffed_animal
'252': sugar_container
'253': suit_jacket
'254': suitcase
'255': sunglasses
'256': sweater
'257': swimming_trunks
'258': t-shirt
'259': table_knife
'260': tablecloth
'261': tablet_ipad
'262': tanktop
'263': tape
'264': tape_measure
'265': tarp
'266': teabag
'267': teapot
'268': tennis_racket
'269': thermometer
'270': thermos
'271': throw_pillow
'272': tie
'273': tissue
'274': toaster
'275': toilet_paper_roll
'276': tomato
'277': tongs
'278': toothbrush
'279': toothpaste
'280': tote_bag
'281': toy
'282': trash_bag
'283': trash_bin
'284': travel_case
'285': tray
'286': trophy
'287': tv
'288': tweezers
'289': umbrella
'290': usb_cable
'291': usb_flash_drive
'292': vacuum_cleaner
'293': vase
'294': video_camera
'295': walker
'296': walking_cane
'297': wallet
'298': watch
'299': water_bottle
'300': water_filter
'301': webcam
'302': weight_exercise
'303': weight_scale
'304': wheel
'305': whisk
'306': whistle
'307': wine_bottle
'308': wine_glass
'309': winter_glove
'310': wok
'311': wrench
'312': ziploc_bag
- name: imagenet_labels
sequence: int64
- name: imagenet_synsets
sequence: string
splits:
- name: test
num_bytes: 127647283245.571
num_examples: 50273
download_size: 125292547404
dataset_size: 127647283245.571
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: other
task_categories:
- image-classification
pretty_name: ObjectNet
size_categories:
- 10K<n<100K
extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ObjectNet Terms:
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
1. ObjectNet may never be used to tune the parameters of any model.
2. Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border.
If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.'
---
# ObjectNet
A webp (lossless) encoded version of [ObjectNet-1.0](https://objectnet.dev/index.html) at original resolution.
## License / Usage Terms
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
1. **ObjectNet may never be used to tune the parameters of any model.**
2. **Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border**.
If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.
## About
What is ObjectNet?
* A new kind of vision dataset borrowing the idea of controls from other areas of science.
* No training set, only a test set! Put your vision system through its paces.
* Collected to intentionally show objects from new viewpoints on new backgrounds.
* 50,000 image test set, same as ImageNet, with controls for rotation, background, and viewpoint.
* 313 object classes with 113 overlapping ImageNet
* Large performance drop, what you can expect from vision systems in the real world!
* Robust to fine-tuning and a very difficult transfer learning problem
## Why the Red Borders / How do I recognize if an image is in ObjectNet?
As training sets become huge, the risk that test and training sets overlap is serious. We provide ObjectNet with a 2 pixel red border around each image which must be removed before performing inference. The ObjectNet license requires that if you post images from ObjectNet to the web, you include this border. Any time you see an image with a solid 2 pixel red border, that's an indication it's in someone's test set and you should be careful about training on it. Reverse image search will allow you to figure out which test set it is from.
NOTE: original ObjectNet PNG files actually have a 2 pixel red border while their descriptions say 1.
## Preprocessing Steps for This timm Version
1. Re-encode PNG images with lossless WebP (~32% reduction in size), keeping red border.
2. Add `imagenet_labels` and `imagenet_synsets` consisting of lists of ImageNet-1k classes that overlap with ObjectNet class.
## Citation
```bibtex
@incollection{NIPS2019_9142,
title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models},
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {9448--9458},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf}
}
``` |
RaiBP/openwebtext2-first-30-chunks-bilingual-examples | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 124631280
num_examples: 38823
download_size: 80459389
dataset_size: 124631280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
weirdjet/scottish-councils-sentence-embeddings | ---
license: unknown
task_categories:
- sentence-similarity
language:
- en
tags:
- councils
- scotland
- scottish
- public sector
pretty_name: Scottish Council Site Content Embeddings
---
# Scottish Council Embeddings
Site content from all* Scottish council sites scraped and embedded using Sentence Transformers and **all-mpnet-base-v2** model.
\* Some councils were unable to be scraped effectively, resulting in little or no embeddings:
- Aberdeenshire Council
- Aberdeen City Council
- Angus Council
- Glasgow City Council |
hails/agieval-gaokao-geography | ---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 116612
num_examples: 199
download_size: 52886
dataset_size: 116612
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "agieval-gaokao-geography"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.
This dataset contains the contents of the Gaokao Geography subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .
Citation:
```
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:
```
@inproceedings{ling-etal-2017-program,
title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
author = "Ling, Wang and
Yogatama, Dani and
Dyer, Chris and
Blunsom, Phil",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1015",
doi = "10.18653/v1/P17-1015",
pages = "158--167",
abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.",
}
@inproceedings{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
@inproceedings{Liu2020LogiQAAC,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
booktitle={International Joint Conference on Artificial Intelligence},
year={2020}
}
@inproceedings{zhong2019jec,
title={JEC-QA: A Legal-Domain Question Answering Dataset},
author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong},
booktitle={Proceedings of AAAI},
year={2020},
}
@article{Wang2021FromLT,
title={From LSAT: The Progress and Challenges of Complex Reasoning},
author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2021},
volume={30},
pages={2201-2216}
}
``` |
micsell/hebrew_kan_sentence70000 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: language
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1798805809.0
num_examples: 10000
download_size: 1798047480
dataset_size: 1798805809.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
J3nsenn/Output-features_10k | ---
license: apache-2.0
---
|
Sofoklis/RF00002_short | ---
dataset_info:
features:
- name: number
dtype: int64
- name: name
dtype: string
- name: sequence
dtype: string
- name: spaced_sequence
dtype: string
- name: array
sequence:
sequence: float64
- name: image
dtype: image
splits:
- name: train
num_bytes: 2955519.789473684
num_examples: 85
- name: test
num_bytes: 347708.2105263158
num_examples: 10
- name: validation
num_bytes: 591103.9578947368
num_examples: 17
download_size: 960996
dataset_size: 3894331.957894737
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
GitMylo/bark-semantic-training | ---
license: mit
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_27 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 890769020.0
num_examples: 174935
download_size: 908171025
dataset_size: 890769020.0
---
# Dataset Card for "chunk_27"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rapanha/vozfarinha | ---
license: openrail
---
|
m-ric/huggingface_doc_qa_eval | ---
license: apache-2.0
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: source_doc
dtype: string
- name: standalone_score
dtype: int64
- name: standalone_eval
dtype: string
- name: relatedness_score
dtype: int64
- name: relatedness_eval
dtype: string
- name: relevance_score
dtype: int64
- name: relevance_eval
dtype: string
splits:
- name: train
num_bytes: 611615.7037037037
num_examples: 67
download_size: 296501
dataset_size: 611615.7037037037
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Synthetic dataset with question/answers couples extracted from [A-Roucher/huggingface_doc](https://huggingface.co/datasets/A-Roucher/huggingface_doc): use it with this dataset to evaluate your RAG systems! ⭐️⭐️⭐️ |
dmayhem93/self-critiquing-base-test | ---
dataset_info:
features:
- name: id
dtype: string
- name: split
dtype: string
- name: time
dtype: float64
- name: labeler
dtype: string
- name: is_topic_based_summarization
dtype: bool
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 73005699
num_examples: 10647
download_size: 18327206
dataset_size: 73005699
---
# Dataset Card for "self-critiquing-base-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rmihiranga/sinhala-text-fullfill-v1 | ---
dataset_info:
features:
- name: Human
dtype: string
- name: Assistant
dtype: string
splits:
- name: train
num_bytes: 2554794
num_examples: 469
download_size: 971796
dataset_size: 2554794
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sinhala-text-fullfill-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chemNLP/msds_sigma_aldrich | ---
license: mit
---
|
nightaway/pixelart | ---
license: openrail
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 560641.0
num_examples: 176
download_size: 273903
dataset_size: 560641.0
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/12eae292 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1332
dataset_size: 176
---
# Dataset Card for "12eae292"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hzrr/audio7z | ---
license: mit
---
|
1rsh/translate-braj-hi-karya | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 120269681.90363261
num_examples: 3115
- name: test
num_bytes: 10390775.046367396
num_examples: 271
download_size: 123506320
dataset_size: 130660456.95
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
belloIsMiaoMa/meow-spec-image-99 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 44633566.0
num_examples: 191
download_size: 44660926
dataset_size: 44633566.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
strkan/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1712954851 | ---
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: 2811
num_examples: 6
download_size: 7409
dataset_size: 2811
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712954851"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
victorzarzu/interior-design-prompt-editing-dataset-unchanged | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: designed_image
dtype: image
splits:
- name: train
num_bytes: 190195645.0
num_examples: 528
download_size: 170759014
dataset_size: 190195645.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_9_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 1046
num_examples: 32
download_size: 2044
dataset_size: 1046
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_9_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
khalidalt/ar_commensense | ---
dataset_info:
features:
- name: id
dtype: int64
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 134510
num_examples: 1000
download_size: 74314
dataset_size: 134510
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- ar
--- |
autoevaluate/autoeval-eval-phpthinh__examplehsd-raw-ff3db7-1730160387 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplehsd
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
metrics: ['f1']
dataset_name: phpthinh/examplehsd
dataset_config: raw
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-1b7
* Dataset: phpthinh/examplehsd
* Config: raw
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model. |
frenchtext/bank-es-2401 | ---
pretty_name: "bank es websites - 2401"
tags:
- wordslab-webscraper
task_categories:
- text-generation
task_ids:
- language-modeling
size_categories: 10K<n<100K
language: es
multilinguality: monolingual
license: apache-2.0
source_datasets: original
language_creators:
- found
annotations_creators:
- no-annotation
configs:
- config_name: default
data_files:
- split: train
path: "bank_es_2401_train_*.parquet"
- split: valid
path: "bank_es_2401_valid_*.parquet"
- split: test
path: "bank_es_2401_test_*.parquet"
dataset_info:
features:
- name: Uri
dtype: string
- name: ExtractedFromPDF
dtype: bool
- name: Timestamp
dtype: string
- name: Lang
dtype: string
- name: Title
dtype: string
- name: Text
dtype: string
- name: Words
dtype: int32
- name: AvgWordsLength
dtype: int32
- name: Chars
dtype: int32
- name: LetterChars
dtype: int32
- name: NumberChars
dtype: int32
- name: OtherChars
dtype: int32
config_name: default
splits:
- name: train
num_examples: 20350
- name: valid
num_examples: 2545
- name: test
num_examples: 2560
download_size: 110598767
---
# Dataset Card for "bank es websites - 2401"
Dataset extracted from public websites by [wordslab-webscraper](https://github.com/wordslab-org/wordslab-webscraper) in 2401:
- domain: bank
- language: es
- license: Apache 2.0
## Dataset Sources
wordslab-webscraper follows the industry best practices for **polite web scraping**:
- clearly identifies itself as a known text indexing bot: "bingbot"
- doesn't try to hide the user IP address behind proxies
- doesn't try to circumvent bots protection solutions
- waits for a minimum delay between two pages to avoid generating too much load
- respects the website "robots.txt" indexing directives
- respects the web page Meta Robots HTML tag
- respects the web page X-Robots-Tag HTTP header
- respects the web page links rel=nofollow HTML attributes
The text data was extracted from the following websites:
| Website | HTML pages | PDF docs | Words |
|:---|:---:|:---:|:---:|
| elpais.com | 648 | 0 | 859572 |
| orangebank.es | 230 | 2 | 83540 |
| selectra.es | 1781 | 14 | 2358891 |
| www.20minutos.es | 565 | 0 | 445259 |
| www.bancamarch.es | 134 | 74 | 391999 |
| www.bancobig.es | 83 | 18 | 56040 |
| www.bancocooperativo.es | 348 | 139 | 1140507 |
| www.bancodepositos.es | 112 | 111 | 429784 |
| www.bancomediolanum.es | 186 | 281 | 1631194 |
| www.bancomundial.org | 123 | 0 | 198375 |
| www.bancosantander.es | 514 | 0 | 426308 |
| www.bankinter.com | 1289 | 126 | 1170979 |
| www.bbva.es | 796 | 174 | 1159709 |
| www.bcc.es | 429 | 188 | 2213459 |
| www.bde.es | 147 | 0 | 75821 |
| www.bnpparibas.es | 107 | 15 | 187157 |
| www.caixabank.es | 331 | 7 | 275867 |
| www.cetelem.es | 277 | 12 | 191143 |
| www.cnmv.es | 304 | 164 | 763320 |
| www.deutsche-bank.es | 424 | 155 | 1278585 |
| www.ebnbanco.com | 314 | 10 | 226346 |
| www.elperiodico.com | 1997 | 0 | 1464925 |
| www.evobanco.com | 610 | 2 | 502912 |
| www.finanzas.com | 1389 | 0 | 612969 |
| www.fundacionbancosabadell.com | 482 | 33 | 237122 |
| www.fundacionbancosantander.com | 192 | 59 | 277875 |
| www.grupbancsabadell.com | 280 | 142 | 3587581 |
| www.ibercaja.es | 368 | 8 | 247437 |
| www.lavanguardia.com | 1417 | 0 | 1138809 |
| www.openbank.es | 97 | 0 | 89034 |
| www.r4.com | 5944 | 524 | 3347625 |
| www.santander.com | 165 | 48 | 186743 |
| www.triodos.es | 385 | 92 | 919441 |
| www.unicajabanco.es | 401 | 188 | 1004625 |
## Uses
**WARNING**
- **the text included in this dataset belongs to its original authors** and is protected by copyright laws
- you are not allowed to use this dataset for anything else than **training a large language model**
- when using a large language model trained on this dataset, you will need to ensure that you comply with the law
- if you benefit from this large language model, you should try to share the value with the original text authors
wordslab-webscraper uses an advanced Html to text conversion algorithm optimized for **long context language modeling**:
- tries to recover the logical structure of the document from the Html or PDF layout
- preserves document / section / list / table grouping and nesting information
- **deduplicates text at the website level while preserving the document structure**
Each example in this dataset is a **markdown text conversion of a full HTML page or PDF document**:
- the document structure is preserved by markdown syntax: headers, lists, tables, paragraphs
- all duplicate paragraphs are removed
## Dataset Structure
The dataset is divided in 3 splits:
- train: 80% of the data
- valid: 10% of the data
- test: 10% of the data
wordslab-webscraper generates **one parquet file per website and per split**.
The parquet files are named with the following pattern:
- bank_es_2401_[split]_[website].parquet
Note than you can load individual splits or websites with HuggingFace datasets using the following commands:
```python
from datasets import load_dataset
# Load a single plit
dataset = load_dataset("namespace/bank-es-2401", split="train")
# Load a single website
data_files = { "train": "bank_es_2401_train_[website].parquet", "valid": "bank_es_2401_valid_[website].parquet", "test": "bank_es_2401_test_[website].parquet" }
dataset = load_dataset("namespace/bank-es-2401", data_files=data_files)
```
Each example in the dataset contains the text of a full web page or PDF document, with the following features:
- Uri: string
- ExtractedFromPDF: bool
- Timestamp: string
- Lang: string
- Title: string
- Text: string
- Words: int32
- AvgWordsLength: int32
- Chars: int32
- LetterChars: int32
- NumberChars: int32
- OtherChars: int32
Note that beause each example is a full page or document, the "Text" feature can be a pretty long string containing thousands of words (as measured by the "Words" feature): you will typically need to chunk it down to the context size of your large language model before using it.
## Bias, Risks, and Limitations
This dataset is a direct extraction from the source websites.
It was not manually curated to remove misleading, offensive, or harmful content.
**Please add a filtering step before using it to train a large language model** if the source websites can't be trusted.
## Dataset Card Contact
Please add a comment in the community section of this repository if you want the maintainer to add or remove websites from this dataset.
|
argilla/ultrafeedback-binarized-avg-rating-for-dpo-filtered | ---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: chosen_response
dtype: string
- name: rejected_response
dtype: string
- name: chosen_avg_rating
dtype: float64
- name: rejected_avg_rating
dtype: float64
- name: chosen_model
dtype: string
splits:
- name: train
num_bytes: 184744511.83915183
num_examples: 57741
download_size: 102559579
dataset_size: 184744511.83915183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
shidowake/augmxnt_ultra-orca-boros-en-ja-v1_split_7 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: source
dtype: string
splits:
- name: train
num_bytes: 20639999.933149945
num_examples: 9397
download_size: 10494418
dataset_size: 20639999.933149945
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Parth/Code-Llama-Custom | ---
license: apache-2.0
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2623548
num_examples: 5000
download_size: 1324115
dataset_size: 2623548
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liamvbetts/sarcastic-news-headlines-v2 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1947706
num_examples: 26709
download_size: 1328187
dataset_size: 1947706
---
# Dataset Card for "sarcastic-news-headlines-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_71 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 112867945
num_examples: 11869
download_size: 32687818
dataset_size: 112867945
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_71"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
peterschmidt85/samsum | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 10789305
num_examples: 14732
download_size: 5844166
dataset_size: 10789305
---
# Dataset Card for "samsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gagan3012/dolphin-retrival-LAREQA-QA-corpus | ---
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 33001
num_examples: 25
- name: queries
num_bytes: 14373
num_examples: 119
download_size: 35050
dataset_size: 47374
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
---
|
Orange/rdfdial | ---
configs:
- config_name: bundle-converted
description: Merge of all rdf converted datasets
data_files:
- path: ["dstc2-rdf/train.jsonl","multiwoz-rdf/train.jsonl","sfxdial-rdf/train.jsonl"]
split: train
- path: ["dstc2-rdf/test.jsonl","multiwoz-rdf/test.jsonl","sfxdial-rdf/test.jsonl"]
split: test
- path: ["dstc2-rdf/validation.jsonl","multiwoz-rdf/validation.jsonl","sfxdial-rdf/validation.jsonl"]
split: validation
- config_name: bundle-simulated
description: Merge of all rdf simulated datasets
data_files:
- path: ["camrest-sim-rdf/train.jsonl","multiwoz-sim-rdf/train.jsonl"]
split: train
- path: ["camrest-sim-rdf/test.jsonl","camrest-sim-rdf/test.jsonl"]
split: test
- path: ["camrest-sim-rdf/validation.jsonl","multiwoz-sim-rdf/validation.jsonl"]
split: validation
- config_name: dstc2
description: DSTC2 converted to rdf format
data_files:
- path: "dstc2-rdf/train.jsonl"
split: train
- path: "dstc2-rdf/test.jsonl"
split: test
- path: "dstc2-rdf/validation.jsonl"
split: validation
- config_name: sfxdial
description: Sfxdial converted to rdf format
data_files:
- path: "sfxdial-rdf/train.jsonl"
split: train
- path: "sfxdial-rdf/test.jsonl"
split: test
- path: "sfxdial-rdf/validation.jsonl"
split: validation
- config_name: multiwoz
description: MultiWoz converted to rdf format
data_files:
- path: "multiwoz-rdf/train.jsonl"
split: train
- path: "multiwoz-rdf/test.jsonl"
split: test
- path: "multiwoz-rdf/validation.jsonl"
split: validation
- config_name: camrest-sim
description: Synthetic dialogs on the Cambridge restaurant search domain
data_files:
- path: "camrest-sim-rdf/train.jsonl"
split: train
- path: "camrest-sim-rdf/test.jsonl"
split: test
- path: "camrest-sim-rdf/validation.jsonl"
split: validation
- config_name: multiwoz-sim
description: Synthetic dialogs on the Multiwoz domains
data_files:
- path: "multiwoz-sim-rdf/train.jsonl"
split: train
- path: "multiwoz-sim-rdf/test.jsonl"
split: test
- path: "multiwoz-sim-rdf/validation.jsonl"
split: validation
tags:
- dialogue
- rdf
- dst
task_categories:
- text-generation
- text2text-generation
task_ids:
- conversational
- rdf-to-text
- dialogue-generation
license:
- other
packages:
- python-gitlab
language:
- en
---
# Dataset Card for rdfdial
## 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)
- [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://huggingface.co/Orange
- **Repository:** https://huggingface.co/Orange/rdfdial
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Morgan VEYRET; Lina Maria ROJAS BARAHONA
### Dataset Summary
This dataset provides dialogues annotated in dialogue acts and dialogue
state in and RDF based formalism.
There is a conversion of `sfxdial`, `dstc2` and `multiwoz2.3` datasets
as well as two fully synthetic datasets created from simulated conversations:
`camrest-sim` and `multiwoz-sim`.
Original dataset before conversion are available here:
- DSTC2: https://github.com/matthen/dstc
- Multiwoz 2.3: https://github.com/thu-coai/ConvLab-2/tree/master/data/multiwoz2.3
- SfxDial: https://www.repository.cam.ac.uk/items/62011578-23d4-4355-8878-5a150fb72b43
### Supported Tasks and Leaderboards
This dataset was used for the following tasks:
- Natural Language Generation
- Dialogue State Tracking
### Languages
This dataset includes the following languages:
- English
## Dataset Structure
### Data Instances
For all datasets, each item has this schema:
```python
{
"dialogue_id": "string", # dialog identifier
"turns": [{ # list of dialog turns
"id": "int8", # dialog turn index in the conversation
"speaker": "string", # speaker identifier ('user' or 'system')
"text": "string", # speaker utterance
"rdf-acts": ["string"], # string representation of dialog acts
}],
"states": [{ # dialog states for each turn
"id": "int8",
"multi_relations": "bool", # are multiple instances of relations allowed ?
"triples": [["string"]], # triples representing the state
"turn_ids": ["int8"], # ids of turns contributing to this state
}],
}
```
### Data Fields
For each dataset item, the following fields are provided:
- `dialogue_id`: unique dialogue identifier
- `turns`: list of speech turns, each turn contains the following fields:
- `id`: turn index in the dialogue
- `speaker`: identifier for the speaker (`user` or `system`)
- `text`: turn utterance
- `rdf-acts`: list of dialogue acts using string representation of rdf formalism
each act has the form: `act(triple;...)` where `triple` is formatted as
`(subject,predicate,object)`
- `states`: list of states for the dialogue, each entry contains the following fields:
- `id`: state index in the dialogue
- `multi_relations`: boolean indicating if multiple instances of the same predicate are
allowed or not
- `triples`: list of triples representing the graph state, each triple is a list of 3 string like
`[subject,predicate,object]`
- `turn_ids`: list of turn ids that contributed to this state
### Data Splits
For each dataset, splits were generated randomly in the following proportions:
- *train*: 80%
- *validation*: 16%
- *test*: 4%
## Dataset Creation
### Curation Rationale
This dataset has been created to work with graph base dialog state representation using
generative models (T5 family).
### Source Data
#### Initial Data Collection and Normalization
- *Converted datasets*:
- DSTC2: https://github.com/matthen/dstc
- Multiwoz 2.3: https://github.com/thu-coai/ConvLab-2
- SfxDial: https://www.repository.cam.ac.uk/handle/1810/251304
- *Synthetic datasets*: rule-based simulations
#### Who are the source language producers?
- *Converted datasets*: see original datasets documentation
- *Synthetic datasets*: conversations were generated using an agenda-based user simulator and
a rule based agent working directly with dialogue acts. These conversations were then augmented
with natural language user/system utterances. Natural language generation was done using
a T5-base model fine-tuned on the converted datasets.
### Annotations
#### Annotation process
- *Converted datasets*: rule-based conversion of the user/system dialogue acts from slot-value
to RDF based format. The dialogue state is created automatically using another rule based
tracked working with triples. Some conversations could not be converted automatically and/or
contained wrong/confusing annotations and were removed from the dataset compared to the
original ones.
- *Synthetic datasets*: simulation work at the annotation level and the dataset was augmented
to include natural language information.
#### Who are the annotators?
All annotations were generated automatically.
For dialogue acts:
- converted data: rules were applied to convert slot-value based dialogue acts
to rdf-based ones
- synthetic data: rdf-based dialogue acts were directly generated by the dialogue simulation.
For dialogue states, a rule based system was using taking rdf-based dialogue acts as
its inputs.
### Personal and Sensitive Information
This dataset does not contains any personal or sensitive information.
## 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
Converted datasets follow their original licenses:
- DSTC2: [GPL 3.0](https://github.com/matthen/dstc/blob/master/LICENSE)
- Multiwoz 2.3: [Apache 2.0](https://github.com/thu-coai/ConvLab-2/blob/master/LICENSE)
- SfxDial: [Attribution 2.0 UK: England & Wales](https://creativecommons.org/licenses/by/2.0/uk/)
Simulated conversation are provided with the following licenses:
- camrest-sim: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
- multiwoz-sim: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
### Citation Information
[More Information Needed]
### Contributions
- Morgan Veyret
|
sessex/tabi-styles | ---
license: apache-2.0
---
|
duongnghia222/vietnam_finance_news_company_tagged | ---
license: mit
---
|
cfahlgren1/openhermes-2k | ---
license: mit
---
|
acdzh/jiaran-voice | ---
license: mit
---
|
bayandashnan/tmp-translation | ---
dataset_info:
features:
- name: arabic
dtype: string
- name: english
dtype: string
splits:
- name: train
num_bytes: 39
num_examples: 1
- name: test
num_bytes: 39
num_examples: 1
download_size: 2648
dataset_size: 78
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
zouharvi/pwesuite-eval | ---
language:
- en
- am
- bn
- sw
- uz
- es
- pl
- fr
- de
multilinguality:
- multilingual
tags:
- words
- word
- embedding
- phonetic
- phonological
- cognates
- rhyme
- analogy
pretty_name: PWESuite Evaluation v1
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: token_ort
dtype: string
- name: token_ipa
dtype: string
- name: token_arp
dtype: string
- name: lang
dtype: string
- name: purpose
dtype: string
splits:
- name: train
num_examples: 1738008
license: apache-2.0
---
<p align="center">
<img src="https://github.com/zouharvi/pwesuite/assets/7661193/e8db7af0-cccf-425a-8a3c-4f260d5abab7" width="500em">
</p>
# PWESuite-Eval
Dataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.
See code for evaluation [here](https://github.com/zouharvi/pwesuite).
If you use this dataset/evaluation, please cite the [paper at LREC-COLING 2024](https://arxiv.org/abs/2304.02541):
```
@article{zouhar2023pwesuite,
title={{PWESuite}: {P}honetic Word Embeddings and Tasks They Facilitate},
author={Zouhar, Vil{\'e}m and Chang, Kalvin and Cui, Chenxuan and Carlson, Nathaniel and Robinson, Nathaniel and Sachan, Mrinmaya and Mortensen, David},
journal={arXiv preprint arXiv:2304.02541},
year={2023},
url={https://arxiv.org/abs/2304.02541}
}
```
> **Abstract:** Mapping words into a fixed-dimensional vector space is the backbone of modern NLP. While most word embedding methods successfully encode semantic information, they overlook phonetic information that is crucial for many tasks. We develop three methods that use articulatory features to build phonetically informed word embeddings. To address the inconsistent evaluation of existing phonetic word embedding methods, we also contribute a task suite to fairly evaluate past, current, and future methods. We evaluate both (1) intrinsic aspects of phonetic word embeddings, such as word retrieval and correlation with sound similarity, and (2) extrinsic performance on tasks such as rhyme and cognate detection and sound analogies. We hope our task suite will promote reproducibility and inspire future phonetic embedding research.
Used datasets:
- [CMU Pronunciation dictionary](http://www.speech.cs.cmu.edu/cgi-bin/cmudict)
- [CC-100](https://data.statmt.org/cc-100/)
- [CogNet v0](https://aclanthology.org/P19-1302/)
- [Vitz and Winkler (1973)](https://www.sciencedirect.com/science/article/pii/S0022537173800167)
Authors:
- Vilém Zouhar (ETH Zürich, [contact](mailto:vzouhar@ethz.ch))
- Kalvin Chang (CMU LTI, [contact](mailto:kalvinc@cs.cmu.edu))
- Chenxuan Cui (CMU LTI, [contact](mailto:cxcui@cs.cmu.edu))
- Nathaniel Robinson (CMU LTI, [contact](mailto:nrrobins@cs.cmu.edu))
- Nathaniel Carlson (BYU, [contact](mailto:natec18@byu.edu))
- David Mortensen (CMU LTI, [contact](mailto:dmortens@cs.cmu.edu)) |
andersonbcdefg/dup_pairs_12m_jaccard_low | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: __index_level_0__
dtype: int64
- name: jaccard
dtype: float64
splits:
- name: train
num_bytes: 533777023.6498108
num_examples: 4475915
download_size: 352610426
dataset_size: 533777023.6498108
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SinKove/synthetic_mammography_csaw | ---
task_categories:
- image-classification
tags:
- medical
pretty_name: C
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Synthetic CSAW 100k Mammograms
## Dataset Description
This is a synthetic mammogram dataset created with the latent diffusion model from *Generative AI for Medical Imaging: extending the MONAI Framework* paper.
The generative model was trained on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).
- **Paper: https://arxiv.org/abs/2307.15208
- **Point of Contact: walter.diaz_sanz@kcl.ac.uk
### Dataset Summary
### Supported Tasks
Classification masking of cancer in mammogram.
The dataset contains 100k synthetic mammograms with 3 labels:
- "Low masking level" (score <= 2),
- "Medium masking level" (2 < score <= 6),
- "High masking level" (score > 6).
## Dataset Structure
- Images
- CSAW-M Labels
### Data Splits
We did not define data splits.
## Dataset Creation
We generated the synthetic data samples using the diffusion model finetuned on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).
### Personal and Sensitive Information
Following GDPR "Personal data is any information that relates to an identified or identifiable living individual."
We make sure that there are not "personal data" (re-identifiable information) by filtering with a deep learning model trained for identifying patients.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset can used to enhance AI models training for cancer masking.
### Discussion of Biases
There are biases towards specific pathologies.
## Additional Information
### Dataset Curators
### Licensing Information
This dataset is released under the [Open & Responsible AI license ("OpenRAIL")](https://huggingface.co/blog/open_rail)
### Citation Information
Pinaya, W. H., Graham, M. S., Kerfoot, E., Tudosiu, P. D., Dafflon, J., Fernandez, V., ... & Cardoso, M. J. (2023). Generative ai for medical imaging: extending the monai framework. arXiv preprint arXiv:2307.15208.
https://arxiv.org/abs/2307.15208
|
ctoraman/BilTweetNews-event-detection | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- text classification
- event detection
- topic detection
- tweets
- social media
- topic classification
size_categories:
- n<1K
---
# Turkish Event Detection Tweet Dataset: BilTweetNews
The dataset contains tweets related to six major events from Turkish news sources between May 4, 2015
and Jan 8, 2017.
There are 7 event classes:
- E1: May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015
Turkish Super League.
- E2: Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Dağlıca, a village in
southeastern Turkey.
- E3: Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with
his studies on DNA repair.
- E4: May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for
the first time in their history.
- E5: Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called
the 10th Year Anthem, was forbidden in schools by the director of national
education in the Black Sea province of Bolu.
- E6: Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written
in 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar
Madonna’s life. The book tells a story between a Turkish student and German
singer after the World War I.
- Other: Not related to any news topic
For each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are
removed due to detecting near-duplicates later. The dataset has 995 tweets in total.
The task of this dataset is event detection. The sentiment analysis labels can be found at https://huggingface.co/datasets/ctoraman/BilTweetNews-Sentiment
All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 7 event classes. We also provide the majority class at the last column. There are no cases where multiple classes have the highest score.
Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilTweetNews2017
# If you would like to use any material in this repository, please cite the following papers:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.
- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167
**** |
RomilsonB/henryfreitasss | ---
license: openrail
---
|
HuggingFaceM4/M3IT_upsampled | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: inputs
dtype: string
- name: outputs
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 122475491693.125
num_examples: 1486271
download_size: 21371551697
dataset_size: 122475491693.125
---
# Dataset Card for "M3IT_upsampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JyotiNayak/political_ideologies | ---
dataset_info:
features:
- name: statement
dtype: string
- name: label
dtype: int64
- name: issue_type
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1138069
num_examples: 2560
- name: test
num_bytes: 141128
num_examples: 320
- name: validation
num_bytes: 145033
num_examples: 320
download_size: 699580
dataset_size: 1424230
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- text-classification
- question-answering
- zero-shot-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card contains very short paragraphs (2-3 sentences) which are labelled as either 'liberal' or 'conservative'. It has been generated using GPT-4.
## Dataset Details
### Dataset Description
The code to generate the data can be found here: https://github.com/jyotisn79/Labelled_data_generator
All the entries has also been manually checked to ensure that the paragraph accurately maps to the labels. Note that the lables may not be representative of political discourses outside of the United States.
Label Mapping: {'conservative': 0, 'liberal': 1}
Issue Type Mapping: {'economic': 0, 'environmental': 1, 'family/gender': 2, 'geo-political and foreign policy': 3, 'political': 4, 'racial justice and immigration': 5, 'religious': 6, 'social, health and education': 7}
- **Curated by:** Jyoti Shankar Nayak
- **Language(s) (NLP):** English
- **License:** Apache
### Dataset Sources [optional]
GPT-4
- **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. -->
This dataset can be a great starting point to train models to anaylyse political speeches and legal and political documents.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2 | ---
pretty_name: Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [luffycodes/vicuna-mmlu-val-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-mcq-7b-ep2)\
\ 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_luffycodes__vicuna-mmlu-val-mcq-7b-ep2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-18T08:19:40.489086](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2/blob/main/results_2023-12-18T08-19-40.489086.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\": 0.4687909606959751,\n\
\ \"acc_stderr\": 0.034482691498229606,\n \"acc_norm\": 0.474821731384865,\n\
\ \"acc_norm_stderr\": 0.03528101342729721,\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.4386596963219029,\n\
\ \"mc2_stderr\": 0.014931837062941003\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4931740614334471,\n \"acc_stderr\": 0.014610029151379813,\n\
\ \"acc_norm\": 0.5332764505119454,\n \"acc_norm_stderr\": 0.01457899585960581\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5841465843457478,\n\
\ \"acc_stderr\": 0.004918612098944032,\n \"acc_norm\": 0.7773351921927902,\n\
\ \"acc_norm_stderr\": 0.00415184825793471\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n\
\ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n\
\ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249034,\n\
\ \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249034\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.4830188679245283,\n \"acc_stderr\": 0.030755120364119898,\n\
\ \"acc_norm\": 0.4830188679245283,\n \"acc_norm_stderr\": 0.030755120364119898\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5138888888888888,\n\
\ \"acc_stderr\": 0.04179596617581,\n \"acc_norm\": 0.5138888888888888,\n\
\ \"acc_norm_stderr\": 0.04179596617581\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"\
acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.41040462427745666,\n\
\ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.41040462427745666,\n\
\ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.1568627450980392,\n \"acc_stderr\": 0.03618664819936246,\n\
\ \"acc_norm\": 0.1568627450980392,\n \"acc_norm_stderr\": 0.03618664819936246\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n\
\ \"acc_stderr\": 0.04372748290278008,\n \"acc_norm\": 0.3157894736842105,\n\
\ \"acc_norm_stderr\": 0.04372748290278008\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.32275132275132273,\n \"acc_stderr\": 0.024078943243597016,\n \"\
acc_norm\": 0.32275132275132273,\n \"acc_norm_stderr\": 0.024078943243597016\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.5193548387096775,\n \"acc_stderr\": 0.028422687404312107,\n \"\
acc_norm\": 0.5193548387096775,\n \"acc_norm_stderr\": 0.028422687404312107\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3694581280788177,\n \"acc_stderr\": 0.03395970381998574,\n \"\
acc_norm\": 0.3694581280788177,\n \"acc_norm_stderr\": 0.03395970381998574\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\
: 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\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\"\
: 0.6060606060606061,\n \"acc_stderr\": 0.034812853382329624,\n \"\
acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.034812853382329624\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6424870466321243,\n \"acc_stderr\": 0.034588160421810114,\n\
\ \"acc_norm\": 0.6424870466321243,\n \"acc_norm_stderr\": 0.034588160421810114\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.40512820512820513,\n \"acc_stderr\": 0.024890471769938145,\n\
\ \"acc_norm\": 0.40512820512820513,\n \"acc_norm_stderr\": 0.024890471769938145\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \
\ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3907563025210084,\n \"acc_stderr\": 0.031693802357129965,\n\
\ \"acc_norm\": 0.3907563025210084,\n \"acc_norm_stderr\": 0.031693802357129965\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6018348623853211,\n \"acc_stderr\": 0.020987989422654268,\n \"\
acc_norm\": 0.6018348623853211,\n \"acc_norm_stderr\": 0.020987989422654268\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.30092592592592593,\n \"acc_stderr\": 0.03128039084329881,\n \"\
acc_norm\": 0.30092592592592593,\n \"acc_norm_stderr\": 0.03128039084329881\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5882352941176471,\n \"acc_stderr\": 0.03454236585380609,\n \"\
acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.03454236585380609\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6413502109704642,\n \"acc_stderr\": 0.031219569445301847,\n \
\ \"acc_norm\": 0.6413502109704642,\n \"acc_norm_stderr\": 0.031219569445301847\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5605381165919282,\n\
\ \"acc_stderr\": 0.03331092511038179,\n \"acc_norm\": 0.5605381165919282,\n\
\ \"acc_norm_stderr\": 0.03331092511038179\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\
\ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.5537190082644629,\n \"acc_stderr\": 0.0453793517794788,\n \"acc_norm\"\
: 0.5537190082644629,\n \"acc_norm_stderr\": 0.0453793517794788\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.0471282125742677,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.0471282125742677\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.4662576687116564,\n \"acc_stderr\": 0.03919415545048411,\n\
\ \"acc_norm\": 0.4662576687116564,\n \"acc_norm_stderr\": 0.03919415545048411\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.5242718446601942,\n \"acc_stderr\": 0.049449010929737795,\n\
\ \"acc_norm\": 0.5242718446601942,\n \"acc_norm_stderr\": 0.049449010929737795\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7264957264957265,\n\
\ \"acc_stderr\": 0.029202540153431173,\n \"acc_norm\": 0.7264957264957265,\n\
\ \"acc_norm_stderr\": 0.029202540153431173\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6360153256704981,\n\
\ \"acc_stderr\": 0.01720568480903223,\n \"acc_norm\": 0.6360153256704981,\n\
\ \"acc_norm_stderr\": 0.01720568480903223\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5317919075144508,\n \"acc_stderr\": 0.02686462436675665,\n\
\ \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.02686462436675665\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n\
\ \"acc_stderr\": 0.014551553659369922,\n \"acc_norm\": 0.2536312849162011,\n\
\ \"acc_norm_stderr\": 0.014551553659369922\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02861462475280544,\n\
\ \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02861462475280544\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5755627009646302,\n\
\ \"acc_stderr\": 0.028071928247946205,\n \"acc_norm\": 0.5755627009646302,\n\
\ \"acc_norm_stderr\": 0.028071928247946205\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.49691358024691357,\n \"acc_stderr\": 0.02782021415859437,\n\
\ \"acc_norm\": 0.49691358024691357,\n \"acc_norm_stderr\": 0.02782021415859437\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759415,\n \
\ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759415\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3428943937418514,\n\
\ \"acc_stderr\": 0.0121234632715859,\n \"acc_norm\": 0.3428943937418514,\n\
\ \"acc_norm_stderr\": 0.0121234632715859\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4522058823529412,\n \"acc_stderr\": 0.03023375855159645,\n\
\ \"acc_norm\": 0.4522058823529412,\n \"acc_norm_stderr\": 0.03023375855159645\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.44281045751633985,\n \"acc_stderr\": 0.02009508315457735,\n \
\ \"acc_norm\": 0.44281045751633985,\n \"acc_norm_stderr\": 0.02009508315457735\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5363636363636364,\n\
\ \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n\
\ \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5551020408163265,\n \"acc_stderr\": 0.031814251181977865,\n\
\ \"acc_norm\": 0.5551020408163265,\n \"acc_norm_stderr\": 0.031814251181977865\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5970149253731343,\n\
\ \"acc_stderr\": 0.034683432951111266,\n \"acc_norm\": 0.5970149253731343,\n\
\ \"acc_norm_stderr\": 0.034683432951111266\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.43373493975903615,\n\
\ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.43373493975903615,\n\
\ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.03565079670708311,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.03565079670708311\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.4386596963219029,\n\
\ \"mc2_stderr\": 0.014931837062941003\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.712707182320442,\n \"acc_stderr\": 0.01271748105247803\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1023502653525398,\n \
\ \"acc_stderr\": 0.008349110996208834\n }\n}\n```"
repo_url: https://huggingface.co/luffycodes/vicuna-mmlu-val-mcq-7b-ep2
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_12_18T08_19_40.489086
path:
- '**/details_harness|arc:challenge|25_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|gsm8k|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hellaswag|10_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-19-40.489086.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-18T08-19-40.489086.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- '**/details_harness|winogrande|5_2023-12-18T08-19-40.489086.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-18T08-19-40.489086.parquet'
- config_name: results
data_files:
- split: 2023_12_18T08_19_40.489086
path:
- results_2023-12-18T08-19-40.489086.parquet
- split: latest
path:
- results_2023-12-18T08-19-40.489086.parquet
---
# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-mcq-7b-ep2) 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_luffycodes__vicuna-mmlu-val-mcq-7b-ep2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-18T08:19:40.489086](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2/blob/main/results_2023-12-18T08-19-40.489086.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": 0.4687909606959751,
"acc_stderr": 0.034482691498229606,
"acc_norm": 0.474821731384865,
"acc_norm_stderr": 0.03528101342729721,
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.4386596963219029,
"mc2_stderr": 0.014931837062941003
},
"harness|arc:challenge|25": {
"acc": 0.4931740614334471,
"acc_stderr": 0.014610029151379813,
"acc_norm": 0.5332764505119454,
"acc_norm_stderr": 0.01457899585960581
},
"harness|hellaswag|10": {
"acc": 0.5841465843457478,
"acc_stderr": 0.004918612098944032,
"acc_norm": 0.7773351921927902,
"acc_norm_stderr": 0.00415184825793471
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45185185185185184,
"acc_stderr": 0.04299268905480864,
"acc_norm": 0.45185185185185184,
"acc_norm_stderr": 0.04299268905480864
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4605263157894737,
"acc_stderr": 0.04056242252249034,
"acc_norm": 0.4605263157894737,
"acc_norm_stderr": 0.04056242252249034
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4830188679245283,
"acc_stderr": 0.030755120364119898,
"acc_norm": 0.4830188679245283,
"acc_norm_stderr": 0.030755120364119898
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.04179596617581,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.04179596617581
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.41040462427745666,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.41040462427745666,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.1568627450980392,
"acc_stderr": 0.03618664819936246,
"acc_norm": 0.1568627450980392,
"acc_norm_stderr": 0.03618664819936246
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.425531914893617,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.425531914893617,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3157894736842105,
"acc_stderr": 0.04372748290278008,
"acc_norm": 0.3157894736842105,
"acc_norm_stderr": 0.04372748290278008
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.47586206896551725,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.47586206896551725,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.32275132275132273,
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"acc_norm": 0.32275132275132273,
"acc_norm_stderr": 0.024078943243597016
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04216370213557835
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5193548387096775,
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"acc_norm": 0.5193548387096775,
"acc_norm_stderr": 0.028422687404312107
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3694581280788177,
"acc_stderr": 0.03395970381998574,
"acc_norm": 0.3694581280788177,
"acc_norm_stderr": 0.03395970381998574
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.6303030303030303,
"acc_norm_stderr": 0.03769430314512567
},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6424870466321243,
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"acc_norm_stderr": 0.034588160421810114
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.40512820512820513,
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"acc_norm": 0.40512820512820513,
"acc_norm_stderr": 0.024890471769938145
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.24814814814814815,
"acc_norm_stderr": 0.0263357394040558
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3907563025210084,
"acc_stderr": 0.031693802357129965,
"acc_norm": 0.3907563025210084,
"acc_norm_stderr": 0.031693802357129965
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.03802039760107903
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6018348623853211,
"acc_stderr": 0.020987989422654268,
"acc_norm": 0.6018348623853211,
"acc_norm_stderr": 0.020987989422654268
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.30092592592592593,
"acc_stderr": 0.03128039084329881,
"acc_norm": 0.30092592592592593,
"acc_norm_stderr": 0.03128039084329881
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5882352941176471,
"acc_stderr": 0.03454236585380609,
"acc_norm": 0.5882352941176471,
"acc_norm_stderr": 0.03454236585380609
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6413502109704642,
"acc_stderr": 0.031219569445301847,
"acc_norm": 0.6413502109704642,
"acc_norm_stderr": 0.031219569445301847
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5605381165919282,
"acc_stderr": 0.03331092511038179,
"acc_norm": 0.5605381165919282,
"acc_norm_stderr": 0.03331092511038179
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5419847328244275,
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"acc_norm": 0.5419847328244275,
"acc_norm_stderr": 0.04369802690578756
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5537190082644629,
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"acc_norm": 0.5537190082644629,
"acc_norm_stderr": 0.0453793517794788
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6111111111111112,
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"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.0471282125742677
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.4662576687116564,
"acc_norm_stderr": 0.03919415545048411
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.375,
"acc_stderr": 0.04595091388086298,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04595091388086298
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.049449010929737795,
"acc_norm": 0.5242718446601942,
"acc_norm_stderr": 0.049449010929737795
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7264957264957265,
"acc_stderr": 0.029202540153431173,
"acc_norm": 0.7264957264957265,
"acc_norm_stderr": 0.029202540153431173
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6360153256704981,
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"acc_norm": 0.6360153256704981,
"acc_norm_stderr": 0.01720568480903223
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm_stderr": 0.02686462436675665
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2536312849162011,
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"acc_norm": 0.2536312849162011,
"acc_norm_stderr": 0.014551553659369922
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5163398692810458,
"acc_stderr": 0.02861462475280544,
"acc_norm": 0.5163398692810458,
"acc_norm_stderr": 0.02861462475280544
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5755627009646302,
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"acc_norm": 0.5755627009646302,
"acc_norm_stderr": 0.028071928247946205
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.49691358024691357,
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"acc_norm": 0.49691358024691357,
"acc_norm_stderr": 0.02782021415859437
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.35815602836879434,
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"acc_norm": 0.35815602836879434,
"acc_norm_stderr": 0.028602085862759415
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm_stderr": 0.0121234632715859
},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04776449162396197
},
"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.5551020408163265,
"acc_norm_stderr": 0.031814251181977865
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.034683432951111266
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.43373493975903615,
"acc_norm_stderr": 0.03858158940685516
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.03565079670708311,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.03565079670708311
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.4386596963219029,
"mc2_stderr": 0.014931837062941003
},
"harness|winogrande|5": {
"acc": 0.712707182320442,
"acc_stderr": 0.01271748105247803
},
"harness|gsm8k|5": {
"acc": 0.1023502653525398,
"acc_stderr": 0.008349110996208834
}
}
```
## 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
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### 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. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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patnaikshekhar/gitlab-code | ---
license: mit
---
|
DL3DV/DL3DV-ALL-video | ---
tags:
- 3D Vision
- NeRF
- 3D Gaussian
- Dataset
- Novel View Synthesis
- Text to 3D
- Image to 3D
pretty_name: Dl3DV-Dataset
size_categories:
- n>1T
---
# DL3DV-Dataset
This repo has all the original videos of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience.
# Download
If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading).
If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage:
```Bash
usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH]
[--clean_cache]
optional arguments:
-h, --help show this help message and exit
--odir ODIR output directory
--subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K}
The subset of the benchmark to download
--resolution {4K,2K,960P,480P}
The resolution to donwnload
--file_type {images+poses,video,colmap_cache}
The file type to download
--hash HASH If set subset=hash, this is the hash code of the scene to download
--clean_cache If set, will clean the huggingface cache to save space
```
Here are some examples:
```Bash
# Make sure you have applied for the access.
# Use this to download the download.py script
wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py
# Download video, 0~1K subset, output to DL3DV-10K directory
python download.py --odir DL3DV-10K --subset 1K --resolution 4K --file_type video --clean_cache
```
You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html).
```Bash
python download.py --odir DL3DV-10K --subset 1K --resolution 4K --file_type video --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache
```
# News
- [x] DL3DV-1K, 2K, 3K, 4K
- [ ] DL3DV-5K ~ 10K |
gayanin/gcd-native-v8 | ---
dataset_info:
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 45339
num_examples: 213
- name: test
num_bytes: 6231
num_examples: 27
- name: validation
num_bytes: 5969
num_examples: 27
download_size: 39855
dataset_size: 57539
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
wh03lse/models | ---
license: mit
---
|
CyberHarem/yunaka_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yunaka/ユナカ (Fire Emblem)
This is the dataset of yunaka/ユナカ (Fire Emblem), containing 285 images and their tags.
The core tags of this character are `long_hair, red_hair, breasts, red_eyes, large_breasts, bangs, 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 | 285 | 454.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 285 | 225.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 714 | 504.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 285 | 386.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 714 | 783.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_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/yunaka_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 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, star_(symbol), white_shirt, blush, collared_shirt, black_skirt, simple_background, tattoo, white_background, medium_breasts, open_mouth |
| 1 | 11 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, simple_background, solo, star_(symbol), white_background, open_mouth, cape, choker, facial_mark, one_eye_closed, upper_body, blush, :d, ;d |
| 2 | 7 |  |  |  |  |  | 1girl, christmas, gloves, looking_at_viewer, santa_hat, smile, solo, star_(symbol), cleavage, santa_costume, bell, open_mouth, blush, candy_cane, holding, official_alternate_costume, one_eye_closed, cape, fur_trim, medium_breasts, sack |
| 3 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cape, holding_weapon, cleavage, bodysuit, smile, dagger, holding_knife, white_background, simple_background, one_eye_closed, open_mouth, star_hair_ornament |
| 4 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, star_(symbol), nipples, open_mouth, penis, sex, tattoo, blush, nude, vaginal, facial_mark, mosaic_censoring, pussy, smile, torn_clothes, choker, collarbone, pubic_hair, spread_legs, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | star_(symbol) | white_shirt | blush | collared_shirt | black_skirt | simple_background | tattoo | white_background | medium_breasts | open_mouth | cleavage | cape | choker | facial_mark | one_eye_closed | upper_body | :d | ;d | christmas | gloves | santa_hat | santa_costume | bell | candy_cane | holding | official_alternate_costume | fur_trim | sack | holding_weapon | bodysuit | dagger | holding_knife | star_hair_ornament | 1boy | hetero | solo_focus | nipples | penis | sex | nude | vaginal | mosaic_censoring | pussy | torn_clothes | collarbone | pubic_hair | spread_legs | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:----------------|:--------------|:--------|:-----------------|:--------------|:--------------------|:---------|:-------------------|:-----------------|:-------------|:-----------|:-------|:---------|:--------------|:-----------------|:-------------|:-----|:-----|:------------|:---------|:------------|:----------------|:-------|:-------------|:----------|:-----------------------------|:-----------|:-------|:-----------------|:-----------|:---------|:----------------|:---------------------|:-------|:---------|:-------------|:----------|:--------|:------|:-------|:----------|:-------------------|:--------|:---------------|:-------------|:-------------|:--------------|:--------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | X | X | | X | | | X | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | | | | | X | X | X | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | X | X | X | | | | | | X | | X | | X | X | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | X | | X | | X | | | | X | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
AdapterOcean/med_alpaca_standardized_cluster_84_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 11744974
num_examples: 6087
download_size: 6180689
dataset_size: 11744974
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_84_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
XieMo/Furina_Genshin | ---
license: apache-2.0
---
|
AtlasUnified/Atlas-Reasoning | ---
license: mit
language:
- en
pretty_name: 15k Reasoning
size_categories:
- 10K<n<100K
---
# ATLAS-REASONING
This dataset derives from the code here: [atlasunified/atlas-reasoning](https://github.com/atlasunified/atlas-reasoning) and is synthetically generated by GPT-3.5-turbo.
## Categories
The main 42 (See the repo to check the JSONL) categories below were human derived while the subcategories were synthetically generated by GPT-4.
## 1 Deductive Reasoning
-1.1 Syllogistic Arguments
-1.2 Assumptions
-1.3 Abductive Reasoning
-1.4 Modus Ponens
-1.5 Modus Tollens
-1.6 Problem Solving
-1.7 Goal Oriented Thinking
-1.8 Basic Logic
-1.9 Analytical Thinking
-1.10 Philosophical Debate
-1.11 Constructing Arguments
-1.12 Propositional Logic
-1.13 Deduction Rules
-1.14 Mathematical Reasoning
-1.15 Predicate Logic
-1.16 Conclusions
-1.17 The Socratic Method
-1.18 Validity and Soundness
-1.19 Formal Systems
-1.20 Logic Games
-1.21 Decision Making
-1.22 Principled Thinking
-1.23 Inductive Reasoning
-1.24 Predictions
-1.25 Cognitive Theory
-1.26 Inference
-1.27 Quantifying Assumptions
-1.28 Interpreting Evidence
-1.29 Establishing Correlation
-1.30 Rational Inquiry
-1.31 Abductive Logic
-1.32 Exploring Possibilities
-1.33 Distinctions
-1.34 Testing Hypotheses
-1.35 Symmetry
-1.36 Categorical Statements
-1.37 Logical Fallacies
## 2 Inductive Reasoning
2.1 Hypothetical Reasoning
2.2 Analogy
2.3 Probabilistic Reasoning
2.4 Prediction
2.5 Cause and Effect
2.6 Pattern Recognition
2.7 Matching
2.8 Statistical Analysis
2.9 Deductive Reasoning
2.10 Abduction
2.11 Abductive Reasoning
2.12 Systematic Reasoning
2.13 Visual Reasoning
2.14 Analogical Reasoning
2.15 Generalization
2.16 Inductive Logic
2.17 Numerical Analysis
2.18 Heuristic Reasoning
2.19 Experimental Reasoning
2.20 Trend Analysis
2.21 Data Mining
2.22 Decision Trees
2.23 Bayesian Networks
2.24 Predictive Modeling
2.25 Categorical Reasoning
2.26 Test and Measurement
2.27 Simulation and Modeling
2.28 Cognitive Reasoning
2.29 Inferential Reasoning
2.30 Inferential Statistics
2.31 Causal Reasoning
2.32 Pattern Based Reasoning
2.33 Non-Linear Reasoning
2.34 Qualitative Reasoning
2.35 Data Driven Reasoning
2.36 Game Theory
2.37 Mathematical Induction
## 3 Informal Logic
3.1 Fallacies in reasoning
3.2 Argument analysis and evaluation
3.3 Causal reasoning
3.4 Analogical reasoning
3.5 Inductive reasoning
3.6 Deductive reasoning
3.7 Critical thinking skills
3.8 Counterarguments
3.9 Rhetorical devices
3.10 Persuasive techniques
3.11 Logical consistency
3.12 Evidence and reasoning
3.13 Reasoning by analogy
3.14 Logical fallacies in advertising
3.15 Moral reasoning
3.16 Abductive reasoning
3.17 Scientific reasoning
3.18 Ethical reasoning
3.19 Legal reasoning
3.20 Statistical reasoning
3.21 Argument construction
3.22 Logical inference
3.23 Common cognitive biases in reasoning
3.24 Hypothetical reasoning
3.25 Reasoning with probabilities
3.26 Problem-solving techniques
3.27 Decision-making strategies
3.28 Reasoning about cause and effect
3.29 Reasoning with uncertainty
3.30 Argumentation theory
3.31 Reasoning in everyday life
3.32 Reasoning in politics
3.33 Reasoning in ethics
3.34 Reasoning in business
3.35 Reasoning in science
3.36 Reasoning in philosophy
3.37 Reasoning in mathematics
## 4 Cognitive Biases
4.1 Confirmation bias
4.2 Availability heuristic
4.3 Anchoring bias
4.4 Gambler's fallacy
4.5 Hindsight bias
4.6 Framing effect
4.7 Overconfidence bias
4.8 Dunning-Kruger effect
4.9 Self-serving bias
4.10 Status quo bias
4.11 Sunk cost fallacy
4.12 Bandwagon effect
4.13 Illusory correlation
4.14 Halo effect
4.15 Fundamental attribution error
4.16 Negativity bias
4.17 Loss aversion
4.18 Endowment effect
4.19 Choice overload
4.20 Reactance
4.21 Social desirability bias
4.22 In-group bias
4.23 Out-group homogeneity bias
4.24 Implicit bias
4.25 Stereotyping
4.26 Representative heuristic
4.27 False consensus effect
4.28 Priming effect
4.29 Anchoring and adjustment heuristic
4.30 Cognitive dissonance
4.31 Information bias
4.32 Actor-observer bias
4.33 Empathy gap
4.34 Reactivity
4.35 Selective perception
4.36 Projection bias
4.37 Regret aversion
## 5 Logical Fallacies
5.1 Ad Hominem Fallacy
5.2 Straw Man Fallacy
5.3 Appeal to Authority Fallacy
5.4 False Dilemma Fallacy
5.5 Circular Reasoning Fallacy
5.6 Slippery Slope Fallacy
5.7 Appeal to Emotion Fallacy
5.8 Bandwagon Fallacy
5.9 Red Herring Fallacy
5.10 False Cause Fallacy
5.11 Hasty Generalization Fallacy
5.12 Confirmation Bias Fallacy
5.13 Tu Quoque Fallacy
5.14 Begging the Question Fallacy
5.15 Fallacy of Composition
5.16 Fallacy of Division
5.17 Gambler's Fallacy
5.18 Fallacy of Equivocation
5.19 No True Scotsman Fallacy
5.20 Fallacy of Sunk Costs
5.21 Post hoc Ergo Propter hoc Fallacy
5.22 Genetic Fallacy
5.23 Black-and-White Fallacy
5.24 Appeal to Ignorance Fallacy
5.25 Appeal to Tradition Fallacy
5.26 False Analogy Fallacy
5.27 Fallacy of the Middle Ground
5.28 Fallacy of Suppressed Evidence
5.29 Loaded Question Fallacy
5.30 Fallacy of False Equivalence
5.31 Fallacy of the Beard
5.32 Appeal to Fear Fallacy
5.33 Fallacy of the Texas Sharpshooter
5.34 Fallacy of Composition and Division
5.35 Fallacy of Personal Incredulity
5.36 Fallacy of Relative Privation
5.37 Fallacy of Ambiguity
## 6 Probability Theory
6.1 Conditional probability
6.2 Bayes' theorem
6.3 Combinatorics and counting principles
6.4 Random variables
6.5 Probability distributions
6.6 Expected value
6.7 Variance and standard deviation
6.8 Joint probability distributions
6.9 Marginal and conditional distributions
6.10 Independent and dependent events
6.11 Law of large numbers
6.12 Central limit theorem
6.13 Hypothesis testing
6.14 Null and alternative hypotheses
6.15 Type I and Type II errors
6.16 Confidence intervals
6.17 Sampling distributions
6.18 Estimation and point estimation
6.19 Maximum likelihood estimation
6.20 Bayesian inference
6.21 Markov chains
6.22 Random walks
6.23 Stochastic processes
6.24 Queueing theory
6.25 Poisson processes
6.26 Discrete-time and continuous-time models
6.27 Game theory and probability
6.28 Decision theory
6.29 Monte Carlo simulations
6.30 Law of total probability
6.31 Conditional expectation
6.32 Covariance and correlation
6.33 Multivariate probability distributions
6.34 Order statistics
6.35 Moment generating functions
6.36 Survival analysis
6.37 Reliability theory
## 7 Universality
7.1 Turing machines
7.2 Computational universality
7.3 Halting problem
7.4 Universal Turing machine
7.5 Von Neumann architecture
7.6 Formal systems
7.7 Universal logic gates
7.8 Church-Turing thesis
7.9 Universal programming languages
7.10 Genetic universality
7.11 Universal cellular automata
7.12 Universal robots
7.13 Universal data formats
7.14 Universality in artificial intelligence
7.15 Universal computation in physical systems
7.16 Universal computational models
7.17 Universality in quantum computing
7.18 Universal algorithms
7.19 Universal hash functions
7.20 Universality in neural networks
7.21 Universal approximation theorems
7.22 Universality in machine learning models
7.23 Universal grammar in linguistics
7.24 Universal cognitive processes
7.25 Universal reasoning principles
7.26 Universal problem-solving techniques
7.27 Universality in mathematics
7.28 Universal mathematical structures
7.29 Universal properties in category theory
7.30 Universal constructions
7.31 Universal sets
7.32 Universality in formal languages
7.33 Universal automata theory
7.34 Universal logic systems
7.35 Universal semantics
7.36 Universal reasoning in ethics
7.37 Universality in social systems
## 8 Linguistic Logic
8.1 Propositional logic
8.2 Predicate logic
8.3 Formal languages
8.4 Logical connectives
8.5 Truth tables
8.6 Inference rules
8.7 Logical equivalence
8.8 Validity and soundness
8.9 Quantifiers
8.10 First-order logic
8.11 Modal logic
8.12 Fuzzy logic
8.13 Natural language processing
8.14 Sentential logic
8.15 Inductive reasoning
8.16 Deductive reasoning
8.17 Abductive reasoning
8.18 Logical paradoxes
8.19 Set theory
8.20 Type theory
8.21 Propositional calculus
8.22 Linguistic semantics
8.23 Linguistic pragmatics
8.24 Formal systems
8.25 Symbolic logic
8.26 Mathematical logic
8.27 Reasoning fallacies
8.28 Argumentation theory
8.29 Logical puzzles
8.30 Logical operators
8.31 Linguistic ambiguity
8.32 Linguistic meaning
8.33 Linguistic analysis
8.34 Linguistic inference
8.35 Linguistic reasoning tasks
8.36 Linguistic truth values
8.37 Linguistic decision-making
## 9 Moral Reasoning
9.1 Moral dilemmas in healthcare
9.2 Ethical considerations in scientific research
9.3 Moral reasoning in criminal justice
9.4 Ethical implications of artificial intelligence
9.5 Moral decision-making in business ethics
9.6 Ethical issues in genetic engineering
9.7 Moral reasoning in environmental conservation
9.8 Ethical considerations in animal testing
9.9 Moral dilemmas in end-of-life care
9.10 Ethical implications of social media use
9.11 Moral decision-making in global politics
9.12 Ethical issues in human cloning
9.13 Moral reasoning in military ethics
9.14 Ethical considerations in data privacy
9.15 Moral dilemmas in organ transplantation
9.16 Ethical implications of autonomous vehicles
9.17 Moral decision-making in journalism
9.18 Ethical issues in corporate governance
9.19 Moral reasoning in education ethics
9.20 Ethical considerations in cosmetic surgery
9.21 Moral dilemmas in reproductive rights
9.22 Ethical implications of genetic editing
9.23 Moral decision-making in humanitarian aid
9.24 Ethical issues in advertising
9.25 Moral reasoning in social justice
9.26 Ethical considerations in surveillance technologies
9.27 Moral dilemmas in resource allocation
9.28 Ethical implications of human enhancement
9.29 Moral decision-making in professional sports
9.30 Ethical issues in financial markets
9.31 Moral reasoning in immigration ethics
9.32 Ethical considerations in food production
9.33 Moral dilemmas in artificial intelligence and job automation
9.34 Ethical implications of virtual reality technology
9.35 Moral decision-making in international diplomacy
9.36 Ethical issues in nuclear energy
9.37 Moral reasoning in the use of drones
## 10 Philosophical Reasoning
10.1 The nature of knowledge
10.2 Epistemological skepticism
10.3 Theories of truth
10.4 The problem of induction
10.5 The nature of reality
10.6 Metaphysical dualism
10.7 Idealism vs. materialism
10.8 The mind-body problem
10.9 Free will and determinism
10.10 Ethics and moral reasoning
10.11 Ethical relativism
10.12 Utilitarianism
10.13 Deontological ethics
10.14 Virtue ethics
10.15 The problem of evil
10.16 The existence of God
10.17 Arguments for the existence of God
10.18 The problem of divine hiddenness
10.19 The problem of religious diversity
10.20 The nature of consciousness
10.21 Personal identity and the self
10.22 Philosophy of language
10.23 Meaning and reference
10.24 Theories of truth and language
10.25 Language and thought
10.26 Philosophy of mind
10.27 Mental states and qualia
10.28 Artificial intelligence and consciousness
10.29 Philosophy of science
10.30 Scientific realism vs. instrumentalism
10.31 Theories of scientific explanation
10.32 Induction and scientific reasoning
10.33 Philosophy of mathematics
10.34 Platonism vs. nominalism
10.35 The foundations of mathematics
10.36 Philosophy of art and aesthetics
10.37 The nature of beauty and aesthetic experience
## 11 Analogical Reasoning
11.1 Identifying similarities and differences between two objects
11.2 Applying analogical reasoning in problem-solving
11.3 Transfer of knowledge through analogical reasoning
11.4 Analogical reasoning in cognitive development
11.5 Analogical reasoning in artificial intelligence
11.6 Using analogical reasoning to make predictions
11.7 Analogical reasoning in decision-making
11.8 Analogical reasoning in scientific research
11.9 Analogical reasoning in mathematics
11.10 Analogical reasoning in language learning
11.11 Analogical reasoning in concept formation
11.12 Analogical reasoning in pattern recognition
11.13 Analogical reasoning in problem-solving heuristics
11.14 Analogical reasoning in legal reasoning
11.15 Analogical reasoning in moral decision-making
11.16 Analogical reasoning in artistic creativity
11.17 Analogical reasoning in historical analysis
11.18 Analogical reasoning in philosophical arguments
11.19 Analogical reasoning in economic forecasting
11.20 Analogical reasoning in engineering design
11.21 Analogical reasoning in medical diagnosis
11.22 Analogical reasoning in social psychology
11.23 Analogical reasoning in political analysis
11.24 Analogical reasoning in ecological modeling
11.25 Analogical reasoning in educational pedagogy
11.26 Analogical reasoning in architecture and design
11.27 Analogical reasoning in computer programming
11.28 Analogical reasoning in market research
11.29 Analogical reasoning in cognitive biases
11.30 Analogical reasoning in problem reformation
11.31 Analogical reasoning in historical analogies
11.32 Analogical reasoning in evolutionary biology
11.33 Analogical reasoning in logical deduction
11.34 Analogical reasoning in concept mapping
11.35 Analogical reasoning in neural network training
11.36 Analogical reasoning in innovation and invention
11.37 Analogical reasoning in sports strategy
## 12 Set Theory
12.1 Union of sets
12.2 Intersection of sets
12.3 Complement of a set
12.4 Subset relationships
12.5 Power sets
12.6 Disjoint sets
12.7 Cardinality of sets
12.8 Finite and infinite sets
12.9 Empty set
12.10 Universal set
12.11 Set operations
12.12 Set equivalence
12.13 Set difference
12.14 Symmetric difference
12.15 Subset notation
12.16 Set membership notation
12.17 Set equality
12.18 Venn diagrams
12.19 Set partitions
12.20 Cartesian product of sets
12.21 De Morgan's laws
12.22 Distributive laws of sets
12.23 Set identities
12.24 Set operations with intervals
12.25 Interval notation
12.26 Interval arithmetic
12.27 Countable and uncountable sets
12.28 Russell's paradox
12.29 Cantor's diagonal argument
12.30 Set theory axioms
12.31 Zermelo-Fraenkel set theory
12.32 Axiom of choice
12.33 Well-ordering principle
12.34 Russell's paradox
12.35 Infinite sets and their properties
12.36 Finite and infinite unions and intersections
12.37 Applications of set theory in computer science
## 13 Abductive Reasoning
13.1 Hypothesis generation in abductive reasoning
13.2 Evidence evaluation in abductive reasoning
13.3 Inference and deduction in abductive reasoning
13.4 Cognitive biases and abductive reasoning
13.5 Abductive reasoning in scientific research
13.6 Abductive reasoning in detective work
13.7 Abductive reasoning in medical diagnosis
13.8 Abductive reasoning in decision-making
13.9 Abductive reasoning in artificial intelligence
13.10 Abductive reasoning in philosophy
13.11 Abductive reasoning in psychology
13.12 Abductive reasoning in legal reasoning
13.13 Abductive reasoning in problem-solving
13.14 The role of intuition in abductive reasoning
13.15 The relationship between abductive reasoning and induction
13.16 The role of evidence in abductive reasoning
13.17 Abductive reasoning in pattern recognition
13.18 Abductive reasoning in creative thinking
13.19 Abductive reasoning in learning and education
13.20 The limitations of abductive reasoning
13.21 Abductive reasoning and causal inference
13.22 Abductive reasoning in historical analysis
13.23 Abductive reasoning in social sciences
13.24 The role of prior knowledge in abductive reasoning
13.25 Abductive reasoning in business and marketing
13.26 Abductive reasoning in computational linguistics
13.27 Abductive reasoning in engineering design
13.28 Abductive reasoning and Bayesian inference
13.29 The role of uncertainty in abductive reasoning
13.30 Abductive reasoning and problem framing
13.31 Abductive reasoning in natural language understanding
13.32 Abductive reasoning in cognitive psychology
13.33 Abductive reasoning and creativity in art
13.34 Abductive reasoning and decision-making under uncertainty
13.35 Abductive reasoning in ethics and moral reasoning
13.36 Abductive reasoning and argumentation theory
13.37 Abductive reasoning in machine learning and data analysis
## 14 Decision Theory
14.1 Utility theory
14.2 Rational choice theory
14.3 Expected utility theory
14.4 Prospect theory
14.5 Game theory
14.6 Nash equilibrium
14.7 Risk analysis
14.8 Decision trees
14.9 Bayesian decision theory
14.10 Multi-criteria decision analysis
14.11 Behavioral economics
14.12 Information theory
14.13 Decision-making under uncertainty
14.14 Decision-making under risk
14.15 Cost-benefit analysis
14.16 Preference elicitation
14.17 Judgment and decision-making biases
14.18 Social decision-making
14.19 Group decision-making
14.20 Decision support systems
14.21 Robust decision-making
14.22 Uncertainty quantification
14.23 Sensitivity analysis
14.24 Decision-making in complex systems
14.25 Strategic decision-making
14.26 Dynamic decision-making
14.27 Heuristics and biases in decision-making
14.28 Decision-making in healthcare
14.29 Decision-making in finance
14.30 Decision-making in environmental management
14.31 Decision-making in supply chain management
14.32 Decision-making in project management
14.33 Decision-making in artificial intelligence
14.34 Ethical decision-making
14.35 Decision-making in crisis situations
14.36 Decision-making in negotiations
14.37 Decision-making in organizational behavior
## 15 Epistemology
15.1 Foundationalism vs. Coherentism
15.2 Empiricism vs. Rationalism
15.3 Skepticism
15.4 Induction vs. Deduction
15.5 A priori vs. A posteriori knowledge
15.6 Reliability of perception
15.7 The problem of induction
15.8 The nature of truth
15.9 Rationality and irrationality
15.10 Intuition and instinct
15.11 Epistemic justification
15.12 Conceptual schemes and worldview
15.13 Testimony and authority
15.14 Perception vs. interpretation
15.15 Epistemic virtues
15.16 Social construction of knowledge
15.17 Epistemic relativism
15.18 Meta-epistemology
15.19 Internalism vs. Externalism
15.20 Epistemic norms and responsibilities
15.21 Perception and hallucination
15.22 Epistemic luck
15.23 Epistemic closure
15.24 Epistemic contextualism
15.25 Gettier problems
15.26 Reliabilism
15.27 Naturalized epistemology
15.28 Coherence theory of truth
15.29 Foundationalist theories of justification
15.30 Instrumentalism
15.31 Pragmatic theories of truth
15.32 Epistemic justification in science
15.33 Evolutionary epistemology
15.34 Epistemic normativity
15.35 Epistemology of testimony
15.36 Memory and knowledge
15.37 Epistemology and artificial intelligence
## 16 Mind Mapping
16.1 Techniques for creating effective mind maps
16.2 Applying mind mapping to problem-solving
16.3 Using mind maps for brainstorming
16.4 Mind mapping for decision-making
16.5 Mind mapping as a learning tool
16.6 Mind mapping for project management
16.7 Mind mapping for goal setting
16.8 Mind mapping for organizing information
16.9 Mind mapping for note-taking
16.10 Mind mapping for studying
16.11 Mind mapping for creative writing
16.12 Mind mapping for time management
16.13 Mind mapping for team collaboration
16.14 Mind mapping for strategic planning
16.15 Mind mapping for memory improvement
16.16 Mind mapping for visual thinking
16.17 Mind mapping for idea generation
16.18 Mind mapping for effective communication
16.19 Mind mapping for personal development
16.20 Mind mapping for problem analysis
16.21 Mind mapping for critical thinking
16.22 Mind mapping for concept mapping
16.23 Mind mapping for data visualization
16.24 Mind mapping for goal alignment
16.25 Mind mapping for self-reflection
16.26 Mind mapping for information synthesis
16.27 Mind mapping for decision prioritization
16.28 Mind mapping for creativity enhancement
16.29 Mind mapping for task prioritization
16.30 Mind mapping for workflow optimization
16.31 Mind mapping for strategic thinking
16.32 Mind mapping for brainstorming solutions
16.33 Mind mapping for strategic decision-making
16.34 Mind mapping for organizing research
16.35 Mind mapping for collaborative problem-solving
16.36 Mind mapping for mapping knowledge domains
16.37 Mind mapping for generating insights
## 17 Quantitative Reasoning
17.1 Statistical analysis
17.2 Probability theory
17.3 Data interpretation
17.4 Algebraic reasoning
17.5 Arithmetic operations
17.6 Ratios and proportions
17.7 Graphical representation of data
17.8 Data visualization techniques
17.9 Logical reasoning
17.10 Deductive reasoning
17.11 Inductive reasoning
17.12 Geometric reasoning
17.13 Number patterns
17.14 Estimation and approximation
17.15 Data sampling techniques
17.16 Hypothesis testing
17.17 Linear equations
17.18 Quadratic equations
17.19 Exponential growth and decay
17.20 Financial reasoning
17.21 Time and distance problems
17.22 Percentages and fractions
17.23 Permutations and combinations
17.24 Unit conversions
17.25 Measurements and scales
17.26 Logic puzzles
17.27 Game theory
17.28 Decision-making models
17.29 Analytical reasoning
17.30 Statistical inference
17.31 Descriptive statistics
17.32 Operations research
17.33 Optimization problems
17.34 Computational reasoning
17.35 Time series analysis
17.36 Data forecasting
17.37 Critical thinking in quantitative reasoning
## 18 Combinatorics
18.1 Permutations and combinations
18.2 Binomial coefficients
18.3 Pigeonhole principle
18.4 Counting principles
18.5 Combinatorial identities
18.6 Generating functions
18.7 Combinatorial optimization
18.8 Combinatorial proofs
18.9 Combinatorial algorithms
18.10 Graph coloring
18.11 Ramsey theory
18.12 Combinatorial designs
18.13 Latin squares
18.14 Combinatorial game theory
18.15 Partition theory
18.16 Polya's enumeration theorem
18.17 Combinatorial geometry
18.18 Combinatorics in computer science
18.19 Randomized algorithms in combinatorics
18.20 Probabilistic methods in combinatorics
18.21 Combinatorial algorithms for network optimization
18.22 Combinatorial optimization in scheduling problems
18.23 Combinatorial aspects of cryptography
18.24 Combinatorial generation of permutations and subsets
18.25 Combinatorial algorithms for graph theory problems
18.26 Combinatorial optimization in logistics and transportation
18.27 Combinatorial reasoning in coding theory
18.28 Combinatorial methods in data analysis and machine learning
18.29 Combinatorial problems in social network analysis
18.30 Combinatorial enumeration in bioinformatics
18.31 Combinatorial reasoning in operations research
18.32 Combinatorial optimization in supply chain management
18.33 Combinatorial aspects of network design and routing
18.34 Combinatorial reasoning in artificial intelligence
18.35 Combinatorial methods in image processing and computer vision
18.36 Combinatorial reasoning in quantum computing
18.37 Combinatorial aspects of error-correcting codes
## 19 Mathematical Reasoning
19.1 Logical proofs in mathematics
19.2 Inductive reasoning in mathematical patterns
19.3 Deductive reasoning in geometry
19.4 Proving mathematical theorems
19.5 Constructing mathematical counterexamples
19.6 Reasoning with mathematical inequalities
19.7 Applying mathematical logic to problem-solving
19.8 Reasoning with mathematical functions
19.9 Analyzing mathematical series and sequences
19.10 Using mathematical induction to prove statements
19.11 Reasoning with mathematical symbols and notation
19.12 Investigating mathematical paradoxes
19.13 Reasoning with mathematical equations
19.14 Analyzing mathematical graphs and functions
19.15 Applying mathematical reasoning to optimization problems
19.16 Reasoning with mathematical ratios and proportions
19.17 Using logical deduction in number theory
19.18 Reasoning with mathematical vectors and matrices
19.19 Applying mathematical reasoning to combinatorics problems
19.20 Reasoning with mathematical inequalities and absolute values
19.21 Analyzing mathematical algorithms and complexity
19.22 Reasoning with mathematical sets and set operations
19.23 Using inductive reasoning in mathematical modeling
19.24 Reasoning with mathematical limits and convergence
19.25 Applying mathematical reasoning to probability theory
19.26 Reasoning with mathematical graphs and networks
19.27 Using deductive reasoning in mathematical proofs
19.28 Reasoning with mathematical transformations and symmetry
19.29 Applying mathematical reasoning to cryptography
19.30 Reasoning with mathematical series and convergence
19.31 Using mathematical logic in boolean algebra
19.32 Reasoning with mathematical functions and their properties
19.33 Analyzing mathematical patterns in number sequences
19.34 Reasoning with mathematical inequalities and intervals
19.35 Applying mathematical reasoning to optimization in calculus
19.36 Reasoning with mathematical reasoning fallacies
19.37 Using deductive reasoning in mathematical puzzles and riddles
## 20 Critical Thinking
20.1 Logical fallacies
20.2 Inductive reasoning
20.3 Deductive reasoning
20.4 Problem-solving techniques
20.5 Argument analysis
20.6 Decision-making processes
20.7 Cognitive biases
20.8 Evaluating evidence
20.9 Analytical thinking
20.10 Creative thinking
20.11 Causal reasoning
20.12 Syllogistic reasoning
20.13 Counterfactual reasoning
20.14 Abductive reasoning
20.15 Moral reasoning
20.16 Analogical reasoning
20.17 Statistical reasoning
20.18 Decision tree analysis
20.19 Ethical dilemmas
20.20 Argument construction
20.21 Analyzing assumptions
20.22 Evaluating sources of information
20.23 Critical evaluation of claims
20.24 Identifying hidden premises
20.25 Evaluating arguments for validity
20.26 Evaluating arguments for soundness
20.27 Problem-solving heuristics
20.28 Identifying logical inconsistencies
20.29 Evaluating the strength of arguments
20.30 Identifying cognitive biases in others
20.31 Logical reasoning puzzles
20.32 Evaluating the reliability of data
20.33 Identifying common reasoning errors
20.34 Distinguishing correlation from causation
20.35 Identifying straw man arguments
20.36 Identifying circular reasoning
20.37 Evaluating the credibility of experts
## 21 Systems Thinking
21.1 Feedback loops in complex systems
21.2 Causal loop diagrams in systems thinking
21.3 Identifying and understanding system boundaries
21.4 The role of mental models in systems thinking
21.5 Identifying and analyzing system dynamics
21.6 Understanding emergent properties in complex systems
21.7 Identifying and managing system leverage points
21.8 Systems thinking in organizational management
21.9 Systems thinking in environmental sustainability
21.10 Systems thinking in healthcare systems
21.11 Systems thinking in supply chain management
21.12 Systems thinking in economic models
21.13 Systems thinking in social networks and relationships
21.14 Holistic approach to problem-solving using systems thinking
21.15 Systems thinking in urban planning and development
21.16 Systems thinking in educational systems
21.17 Systems thinking in project management
21.18 Systems thinking in risk management
21.19 Systems thinking in policy development and analysis
21.20 Systems thinking in technological innovation
21.21 Systems thinking in climate change mitigation and adaptation
21.22 Systems thinking in complex data analysis
21.23 Systems thinking in conflict resolution and peacebuilding
21.24 Systems thinking in organizational change management
21.25 Systems thinking in financial markets and investments
21.26 Systems thinking in product design and development
21.27 Systems thinking in transportation and logistics
21.28 Systems thinking in public health strategies
21.29 Systems thinking in agriculture and food production
21.30 Systems thinking in energy systems and sustainability
21.31 Systems thinking in quality management
21.32 Systems thinking in information technology systems
21.33 Systems thinking in disaster management and response
21.34 Systems thinking in government and public administration
21.35 Systems thinking in social justice and equity
21.36 Systems thinking in artificial intelligence and machine learning
21.37 Systems thinking in personal development and self-improvement
## 22 Arguments
22.1 Logical fallacies
22.2 Deductive reasoning
22.3 Inductive reasoning
22.4 Abductive reasoning
22.5 Cognitive biases in arguments
22.6 Counterarguments
22.7 Persuasive techniques
22.8 Rhetorical devices
22.9 Propositional logic
22.10 Syllogisms
22.11 Validity and soundness of arguments
22.12 Causal reasoning
22.13 Analogical reasoning
22.14 Ethical reasoning
22.15 Critical thinking
22.16 Informal fallacies
22.17 Argument structure
22.18 Argument analysis
22.19 Toulmin model of argumentation
22.20 Dialectical reasoning
22.21 Reasoning by analogy
22.22 Fallacies of relevance
22.23 Fallacies of presumption
22.24 Fallacies of ambiguity
22.25 Reasoning and decision-making
22.26 Bayesian reasoning
22.27 Reasoning under uncertainty
22.28 Reasoning in mathematics
22.29 Argumentation theory
22.30 Rationality and irrationality in arguments
22.31 Reasoning and problem-solving
22.32 Argument mapping
22.33 Rhetoric and persuasion
22.34 Emotional appeals in arguments
22.35 Cognitive dissonance and argumentation
22.36 Logical consistency in arguments
22.37 Argumentation ethics
## 23 Reasoning from Consequences
23.1 Evaluating the potential outcomes of an action
23.2 Predicting the consequences of a decision
23.3 Analyzing cause-and-effect relationships
23.4 Identifying unintended consequences
23.5 Weighing the benefits and drawbacks of different choices
23.6 Assessing the long-term implications of a course of action
23.7 Considering the ripple effects of a decision
23.8 Recognizing the impact of one's behavior on others
23.9 Anticipating the results of a specific strategy
23.10 Projecting the future based on current actions
23.11 Examining the logical implications of a hypothesis
23.12 Understanding the relationship between actions and outcomes
23.13 Reflecting on past experiences to inform future decision-making
23.14 Considering the ethical implications of a decision
23.15 Assessing the risk and reward of a particular course of action
23.16 Distinguishing between immediate and delayed consequences
23.17 Examining the unintended benefits of an action
23.18 Recognizing the trade-offs involved in decision-making
23.19 Identifying potential obstacles or roadblocks in achieving desired outcomes
23.20 Weighing the potential impact on different stakeholders
23.21 Evaluating the likelihood of different outcomes
23.22 Analyzing the causal chain of events
23.23 Considering the impact of external factors on outcomes
23.24 Assessing the reliability of predictive models
23.25 Recognizing the difference between correlation and causation
23.26 Anticipating the reactions of others to a particular action
23.27 Examining the relationship between intentions and consequences
23.28 Evaluating the effectiveness of different strategies in achieving desired outcomes
23.29 Considering the unintended consequences of policy decisions
23.30 Reflecting on the lessons learned from previous failures or successes
23.31 Identifying potential risks and mitigating strategies
23.32 Analyzing the impact of technological advancements on future consequences
23.33 Evaluating the impact of economic factors on decision outcomes
23.34 Considering the impact of cultural norms on decision consequences
23.35 Assessing the long-term sustainability of a chosen course of action
23.36 Recognizing the role of feedback loops in determining outcomes
23.37 Evaluating the scalability of a decision in different contexts
## 24 Argumentative Strategies
24.1 Logical fallacies in argumentation
24.2 The role of evidence in constructing arguments
24.3 Counterargument and rebuttal techniques
24.4 The use of emotion in persuasive reasoning
24.5 Ethical considerations in argumentation
24.6 The role of language and rhetoric in shaping arguments
24.7 Cognitive biases and their impact on reasoning
24.8 Strategies for constructing a strong thesis statement
24.9 The importance of clarity and coherence in arguments
24.10 Evaluating the credibility of sources in argumentation
24.11 The distinction between deductive and inductive reasoning
24.12 Identifying and analyzing assumptions in arguments
24.13 The role of analogy in persuasive reasoning
24.14 Analyzing and critiquing arguments in written texts
24.15 The use of logical reasoning in legal arguments
24.16 The influence of cultural and societal factors on argumentation
24.17 Understanding and addressing logical inconsistencies in arguments
24.18 Constructing a persuasive argument in a debate setting
24.19 The impact of personal bias on argumentation
24.20 Analyzing the structure and organization of arguments
24.21 The use of statistics and data in persuasive reasoning
24.22 The role of logical operators (AND, OR, NOT) in constructing arguments
24.23 Identifying and responding to straw man arguments
24.24 Ethos, logos, and pathos in persuasive communication
24.25 The psychology of persuasion and argumentation
24.26 Evaluating the strengths and weaknesses of different argumentative strategies
24.27 The role of storytelling in persuasive reasoning
24.28 Assessing the relevance and validity of evidence in arguments
24.29 The impact of framing and language choice on argumentation
24.30 Recognizing and countering ad hominem attacks in arguments
24.31 Understanding the concept of burden of proof in argumentation
24.32 The role of critical thinking in constructing effective arguments
24.33 Analyzing conflicting viewpoints in argumentation
24.34 The impact of social media on argumentative discourse
24.35 The role of logic puzzles in honing reasoning skills
24.36 Identifying and addressing logical fallacies in oral arguments
24.37 The importance of empathy and understanding in constructive argumentation.
## 25 Prediction
25.1 Statistical modeling for predictions
25.2 Time series forecasting
25.3 Machine learning algorithms for prediction
25.4 Predictive analytics in business
25.5 Predictive modeling techniques
25.6 Predictive maintenance in manufacturing
25.7 Predictive modeling for healthcare outcomes
25.8 Predictive policing and crime prevention
25.9 Predictive modeling for stock market trends
25.10 Predictive modeling in weather forecasting
25.11 Predictive analytics for customer behavior
25.12 Predictive modeling for credit risk assessment
25.13 Predictive modeling in sports analytics
25.14 Predictive modeling for transportation planning
25.15 Predictive modeling for disease outbreak prediction
25.16 Predictive modeling for energy consumption
25.17 Predictive modeling for supply chain optimization
25.18 Predictive analytics for marketing campaigns
25.19 Predictive modeling for fraud detection
25.20 Predictive modeling for insurance claims
25.21 Predictive modeling for demand forecasting
25.22 Predictive modeling for election outcomes
25.23 Predictive analytics in personalized medicine
25.24 Predictive modeling for natural disasters
25.25 Predictive modeling for customer churn prediction
25.26 Predictive analytics for website user behavior
25.27 Predictive modeling for student performance
25.28 Predictive modeling for recommendation systems
25.29 Predictive analytics for social media trends
25.30 Predictive modeling for traffic congestion
25.31 Predictive analytics for asset management
25.32 Predictive modeling for customer lifetime value
25.33 Predictive analytics for sentiment analysis
25.34 Predictive modeling for urban planning
25.35 Predictive analytics for machine failure prediction
25.36 Predictive modeling for crop yield prediction
25.37 Predictive analytics for healthcare resource allocation
## 26 Reversibility
26.1 Cause and effect relationships
26.2 Logical reasoning
26.3 Cognitive flexibility
26.4 Problem-solving strategies
26.5 Decision-making processes
26.6 Analytical thinking
26.7 Memory recall and retrieval
26.8 Pattern recognition
26.9 Sequential reasoning
26.10 Hypothetical scenarios
26.11 Inference and deduction
26.12 Inductive reasoning
26.13 Deductive reasoning
26.14 Algorithmic thinking
26.15 Computational complexity
26.16 Counterfactual reasoning
26.17 Abductive reasoning
26.18 Heuristics and biases
26.19 Critical thinking skills
26.20 Systems thinking
26.21 Error analysis and correction
26.22 Experimental design and control
26.23 Probability and uncertainty
26.24 Spatial reasoning
26.25 Analogical reasoning
26.26 Transitive reasoning
26.27 Metacognition
26.28 Mental models
26.29 Logic puzzles and games
26.30 Decision trees
26.31 Bayes' theorem
26.32 Game theory
26.33 Problem decomposition
26.34 Causal reasoning
26.35 Ethical reasoning
26.36 Conceptual reasoning
26.37 Reasoning under constraints
## 27 Causality
27.1 Cause and effect relationships
27.2 Temporal causality
27.3 Counterfactual reasoning
27.4 Deterministic causality
27.5 Probabilistic causality
27.6 Causal inference
27.7 Causal reasoning in psychology
27.8 Causal reasoning in philosophy
27.9 Causal reasoning in economics
27.10 Causal reasoning in artificial intelligence
27.11 Causal models
27.12 Causal diagrams
27.13 Causal networks
27.14 Causal explanations
27.15 Causal mechanisms
27.16 Causal loops
27.17 Causal attribution
27.18 Causal analysis
27.19 Causal reasoning in social sciences
27.20 Causal reasoning in medicine
27.21 Causal reasoning in law
27.22 Causal reasoning in history
27.23 Causal reasoning in biology
27.24 Causal reasoning in physics
27.25 Causal reasoning in engineering
27.26 Causal reasoning in decision-making
27.27 Causal reasoning in education
27.28 Causal reasoning in environmental studies
27.29 Causal reasoning in public policy
27.30 Causal reasoning in statistics
27.31 Causal reasoning in marketing
27.32 Causal reasoning in game theory
27.33 Causal reasoning in ethics
27.34 Causal reasoning in anthropology
27.35 Causal reasoning in sociology
27.36 Causal reasoning in linguistics
27.37 Causal reasoning in neuroscience
## 28 Reasoned Judgement
28.1 Logical reasoning
28.2 Deductive reasoning
28.3 Inductive reasoning
28.4 Abductive reasoning
28.5 Critical thinking
28.6 Decision-making processes
28.7 Cognitive biases in reasoning
28.8 Argument evaluation
28.9 Evaluating evidence
28.10 Fallacies in reasoning
28.11 Analyzing patterns and trends
28.12 Counterfactual reasoning
28.13 Problem-solving strategies
28.14 Rationality and reasoning
28.15 Ethical reasoning
28.16 Moral decision-making
28.17 Bayesian reasoning
28.18 Decision theory
28.19 Heuristics and biases
28.20 Cognitive development and reasoning
28.21 Analogical reasoning
28.22 Reasoning under uncertainty
28.23 Causal reasoning
28.24 Syllogistic reasoning
28.25 Reasoning in mathematics
28.26 Legal reasoning
28.27 Scientific reasoning
28.28 Reasoning in artificial intelligence
28.29 Linguistic reasoning
28.30 Reasoning in philosophy
28.31 Reasoning in psychology
28.32 Cultural influences on reasoning
28.33 Reasoning in economics
28.34 Historical reasoning
28.35 Political reasoning
28.36 Social reasoning
28.37 Reasoning in education
## 29 Heuristics
29.1 Anchoring and adjustment heuristic
29.2 Availability heuristic
29.3 Representativeness heuristic
29.4 Confirmation bias
29.5 Overconfidence bias
29.6 Gambler's fallacy
29.7 Sunk cost fallacy
29.8 Framing effect
29.9 Base rate fallacy
29.10 Hindsight bias
29.11 Cognitive biases in decision making
29.12 Decision-making under uncertainty
29.13 Prospect theory
29.14 Loss aversion
29.15 Intuition in decision making
29.16 The role of emotions in decision making
29.17 Biases in risk assessment
29.18 Bounded rationality
29.19 System 1 and System 2 thinking
29.20 The impact of heuristics on judgment and decision making
29.21 Cognitive biases in problem-solving
29.22 Anchoring bias in negotiation
29.23 The role of heuristics in learning
29.24 Algorithmic decision-making
29.25 Cognitive shortcuts in information processing
29.26 Counterfactual thinking
29.27 Bias blind spot
29.28 The role of social influence in heuristic reasoning
29.29 The relationship between heuristics and biases
29.30 The adaptive value of heuristics
29.31 The impact of expertise on heuristic reasoning
29.32 The role of culture in heuristic reasoning
29.33 Rationality vs. heuristics in decision making
29.34 Decision-making in complex environments
29.35 Heuristics in artificial intelligence
29.36 Heuristics in economic models
29.37 The role of heuristics in creativity and innovation
## 30 Probabilistic Reasoning
30.1 Bayesian networks
30.2 Markov chains
30.3 Hidden Markov models
30.4 Conditional probability
30.5 Joint probability
30.6 Marginal probability
30.7 Prior probability
30.8 Posterior probability
30.9 Maximum likelihood estimation
30.10 Expectation-maximization algorithm
30.11 Decision theory
30.12 Bayesian inference
30.13 Naive Bayes classifier
30.14 Probabilistic graphical models
30.15 Monte Carlo methods
30.16 Sampling techniques
30.17 Belief propagation
30.18 Variable elimination
30.19 Independence assumptions
30.20 Causal reasoning
30.21 Probabilistic reasoning in artificial intelligence
30.22 Uncertainty modeling
30.23 Probabilistic reasoning in robotics
30.24 Probabilistic reasoning in finance
30.25 Probabilistic reasoning in healthcare
30.26 Probabilistic reasoning in natural language processing
30.27 Probabilistic reasoning in computer vision
30.28 Probabilistic reasoning in recommendation systems
30.29 Probabilistic reasoning in anomaly detection
30.30 Probabilistic reasoning in risk assessment
30.31 Probabilistic reasoning in decision-making
30.32 Probabilistic reasoning in game theory
30.33 Probabilistic reasoning in pattern recognition
30.34 Probabilistic reasoning in fault diagnosis
30.35 Probabilistic reasoning in bioinformatics
30.36 Probabilistic reasoning in data analysis
30.37 Probabilistic reasoning in optimization
## 31 Pragmatism
31.1 Cost-benefit analysis
31.2 Decision-making under uncertainty
31.3 Risk assessment and mitigation
31.4 Game theory
31.5 Cognitive biases and heuristics
31.6 Rationality in decision-making
31.7 Logical reasoning
31.8 Ethical reasoning
31.9 Deductive reasoning
31.10 Inductive reasoning
31.11 Abductive reasoning
31.12 Argumentation and critical thinking
31.13 Problem-solving strategies
31.14 Decision-making models
31.15 Bayesian reasoning
31.16 Cognitive psychology and reasoning
31.17 Neurological basis of reasoning
31.18 Analytical thinking
31.19 Creative problem-solving
31.20 Cognitive load and reasoning efficiency
31.21 Syllogistic reasoning
31.22 Fallacies in reasoning
31.23 Non-monotonic reasoning
31.24 Dialectical reasoning
31.25 Scientific reasoning
31.26 Statistical reasoning
31.27 Deductive logic
31.28 Inductive logic
31.29 Fuzzy logic
31.30 Probabilistic reasoning
31.31 Analogical reasoning
31.32 Practical reasoning
31.33 Normative reasoning
31.34 Emotion and reasoning
31.35 Argument evaluation and reconstruction
31.36 Decision-making in complex systems
31.37 Legal reasoning and interpretation
## 32 Induction
32.1 Predictive modeling
32.2 Data analysis
32.3 Statistical inference
32.4 Generalization
32.5 Causal reasoning
32.6 Pattern recognition
32.7 Machine learning algorithms
32.8 Data mining
32.9 Bayesian inference
32.10 Decision tree algorithms
32.11 Hypothesis testing
32.12 Regression analysis
32.13 Neural networks
32.14 Feature selection
32.15 Clustering algorithms
32.16 Model evaluation
32.17 Overfitting and underfitting
32.18 Model selection
32.19 Time series forecasting
32.20 Confidence intervals
32.21 Ensemble methods
32.22 Cross-validation
32.23 Exploratory data analysis
32.24 Bias-variance trade-off
32.25 Dimensionality reduction
32.26 Association rule mining
32.27 Model interpretation
32.28 Unsupervised learning
32.29 Probabilistic graphical models
32.30 Support vector machines
32.31 Naive Bayes classifier
32.32 Reinforcement learning
32.33 Transfer learning
32.34 Active learning
32.35 Deep learning
32.36 Natural language processing
32.37 Optimization algorithms
## 33 Model-Based Reasoning
33.1 Model-based reasoning in decision-making processes
33.2 The role of models in scientific reasoning
33.3 Model-based reasoning in artificial intelligence
33.4 Applying model-based reasoning to predictive analytics
33.5 Model-based reasoning in cognitive psychology
33.6 Model-based reasoning in problem-solving
33.7 The limitations of model-based reasoning
33.8 Model-based reasoning in engineering design
33.9 Model-based reasoning in computer simulation
33.10 Model-based reasoning in economic forecasting
33.11 Model-based reasoning in medical diagnosis
33.12 The use of models in climate change prediction and mitigation
33.13 Model-based reasoning in risk assessment
33.14 Model-based reasoning in game theory
33.15 Model-based reasoning in fault detection and diagnosis
33.16 The impact of uncertainty on model-based reasoning
33.17 Model-based reasoning in robotics
33.18 Model-based reasoning in natural language processing
33.19 Model-based reasoning in financial modeling
33.20 The use of models in policy analysis and decision-making
33.21 Model-based reasoning in evolutionary biology
33.22 Model-based reasoning in control systems
33.23 Model-based reasoning in supply chain optimization
33.24 Model-based reasoning in transportation planning
33.25 The role of models in social network analysis
33.26 Model-based reasoning in image recognition
33.27 Model-based reasoning in machine learning
33.28 Model-based reasoning in mathematical proof
33.29 Model-based reasoning in ecological modeling
33.30 Model-based reasoning in virtual reality environments
33.31 Model-based reasoning in chemical reaction modeling
33.32 Model-based reasoning in architectural design
33.33 Model-based reasoning in data fusion
33.34 Model-based reasoning in anomaly detection
33.35 The use of models in forecasting stock market trends
33.36 Model-based reasoning in energy management systems
33.37 Model-based reasoning in natural language generation
## 34 Directed Reasoning
34.1 Logical reasoning
34.2 Deductive reasoning
34.3 Inductive reasoning
34.4 Abductive reasoning
34.5 Critical thinking
34.6 Problem-solving
34.7 Decision-making
34.8 Argument analysis
34.9 Analogical reasoning
34.10 Causal reasoning
34.11 Counterfactual reasoning
34.12 Hypothetical reasoning
34.13 Bayesian reasoning
34.14 Syllogistic reasoning
34.15 Dialectical reasoning
34.16 Transitive reasoning
34.17 Spatial reasoning
34.18 Temporal reasoning
34.19 Fuzzy reasoning
34.20 Heuristic reasoning
34.21 Probabilistic reasoning
34.22 Reasoning under uncertainty
34.23 Reasoning under incomplete information
34.24 Reasoning with constraints
34.25 Reasoning with emotions
34.26 Ethical reasoning
34.27 Moral reasoning
34.28 Reasoning in mathematics
34.29 Reasoning in science
34.30 Reasoning in philosophy
34.31 Reasoning in law
34.32 Reasoning in economics
34.33 Reasoning in artificial intelligence
34.34 Reasoning in computer programming
34.35 Reasoning in linguistics
34.36 Reasoning in psychology
34.37 Reasoning in education
## 35 Integrative Reasoning
35.1 Logical reasoning
35.2 Analytical reasoning
35.3 Deductive reasoning
35.4 Inductive reasoning
35.5 Abductive reasoning
35.6 Critical thinking
35.7 Problem-solving
35.8 Decision-making
35.9 Cognitive flexibility
35.10 Pattern recognition
35.11 Data analysis
35.12 Statistical reasoning
35.13 Comparative analysis
35.14 Conceptual reasoning
35.15 Systems thinking
35.16 Cause and effect reasoning
35.17 Analogical reasoning
35.18 Argumentation
35.19 Counterfactual reasoning
35.20 Hypothetical reasoning
35.21 Creative reasoning
35.22 Emotional intelligence in reasoning
35.23 Ethical reasoning
35.24 Scientific reasoning
35.25 Cognitive biases in reasoning
35.26 Cognitive load in reasoning
35.27 Metacognition in reasoning
35.28 Heuristics and biases
35.29 Cognitive development and reasoning
35.30 Decision-making under uncertainty
35.31 Cognitive mapping
35.32 Cognitive dissonance and reasoning
35.33 Belief revision
35.34 Bayesian reasoning
35.35 Fuzzy logic reasoning
35.36 Game theory reasoning
35.37 Risk assessment and reasoning
## 36 Analytical Reasoning
36.1 Logical deduction
36.2 Pattern recognition
36.3 Data interpretation
36.4 Critical thinking
36.5 Problem-solving strategies
36.6 Inference and conclusion drawing
36.7 Analyzing arguments
36.8 Decision-making processes
36.9 Analyzing cause and effect
36.10 Inductive reasoning
36.11 Deductive reasoning
36.12 Statistical reasoning
36.13 Cognitive biases
36.14 Analyzing assumptions
36.15 Analogical reasoning
36.16 Analyzing syllogisms
36.17 Analyzing logical fallacies
36.18 Analyzing graphs and charts
36.19 Analyzing puzzles
36.20 Analyzing paradoxes
36.21 Analyzing correlations
36.22 Analyzing contradictions
36.23 Analyzing probabilities
36.24 Analyzing premises and evidence
36.25 Analyzing hypothetical scenarios
36.26 Analyzing analogies
36.27 Analyzing data sets
36.28 Analyzing scientific experiments
36.29 Analyzing quantitative information
36.30 Analyzing qualitative information
36.31 Analyzing trends and patterns
36.32 Analyzing decision trees
36.33 Analyzing financial data
36.34 Analyzing ethical dilemmas
36.35 Analyzing historical events
36.36 Analyzing legal arguments
36.37 Analyzing logical frameworks
## 37 Rule-Based Reasoning
37.1 If-else statements in rule-based reasoning
37.2 Rule-based decision-making
37.3 Rule-based expert systems
37.4 Forward chaining in rule-based reasoning
37.5 Backward chaining in rule-based reasoning
37.6 Rule-based inference engines
37.7 Rule-based reasoning in artificial intelligence
37.8 Rule-based systems in healthcare
37.9 Rule-based reasoning in finance
37.10 Rule-based reasoning in legal applications
37.11 Rule-based reasoning in robotics
37.12 Rule-based reasoning in natural language processing
37.13 Rule-based reasoning in computer vision
37.14 Rule-based reasoning in game playing
37.15 Rule-based reasoning in recommender systems
37.16 Rule-based reasoning in logistics and supply chain management
37.17 Rule-based reasoning in customer relationship management
37.18 Rule-based reasoning in data mining
37.19 Rule-based reasoning in fraud detection
37.20 Rule-based reasoning in quality control
37.21 Rule-based reasoning in fault diagnosis
37.22 Rule-based reasoning in smart homes
37.23 Rule-based reasoning in intelligent transportation systems
37.24 Rule-based reasoning in industrial automation
37.25 Rule-based reasoning in energy management
37.26 Rule-based reasoning in risk assessment
37.27 Rule-based reasoning in pattern recognition
37.28 Rule-based reasoning in anomaly detection
37.29 Rule-based reasoning in security systems
37.30 Rule-based reasoning in environmental monitoring
37.31 Rule-based reasoning in agricultural applications
37.32 Rule-based reasoning in inventory management
37.33 Rule-based reasoning in sentiment analysis
37.34 Rule-based reasoning in speech recognition
37.35 Rule-based reasoning in virtual assistants
37.36 Rule-based reasoning in personalization
37.37 Rule-based reasoning in education and e-learning
## 38 Creative Reasoning
38.1 Analogical reasoning
38.2 Problem-solving strategies
38.3 Divergent thinking
38.4 Convergent thinking
38.5 Lateral thinking
38.6 Reasoning by analogy
38.7 Deductive reasoning
38.8 Inductive reasoning
38.9 Abductive reasoning
38.10 Pattern recognition
38.11 Decision-making heuristics
38.12 Counterfactual reasoning
38.13 Metacognition
38.14 Cognitive flexibility
38.15 Visual reasoning
38.16 Mathematical reasoning
38.17 Logical reasoning
38.18 Reasoning under uncertainty
38.19 Reasoning under constraints
38.20 Conceptual reasoning
38.21 Critical thinking
38.22 Reasoning about causality
38.23 Reasoning about ethics
38.24 Analytical reasoning
38.25 Intuitive reasoning
38.26 Reasoning about emotions
38.27 Reasoning about time
38.28 Reasoning about spatial relationships
38.29 Hypothetical reasoning
38.30 Reasoning about probabilities
38.31 Reasoning about paradoxes
38.32 Reasoning about ambiguity
38.33 Reasoning about complex systems
38.34 Reasoning about human behavior
38.35 Analogical problem-solving
38.36 Reasoning about creativity itself
38.37 Reasoning about art and aesthetics
## 39 Narrative Reasoning
39.1 Character motivation analysis
39.2 Plot analysis
39.3 Story structure analysis
39.4 Theme identification
39.5 Symbolism interpretation
39.6 Conflict resolution analysis
39.7 Foreshadowing identification
39.8 Point of view analysis
39.9 Setting analysis
39.10 Character development analysis
39.11 Plot twist analysis
39.12 Subtext interpretation
39.13 Moral dilemma analysis
39.14 Narrative perspective analysis
39.15 Emotional arc analysis
39.16 Narrative pacing analysis
39.17 Relationship dynamics analysis
39.18 World-building analysis
39.19 Narrative voice analysis
39.20 Narrative tension analysis
39.21 Intertextuality analysis
39.22 Narrative framing analysis
39.23 Allegory interpretation
39.24 Metaphor analysis
39.25 Irony identification
39.26 Archetypal analysis
39.27 Narrative coherence analysis
39.28 Narrative ambiguity analysis
39.29 Cause and effect analysis
39.30 Narrative symbolism analysis
39.31 Backstory analysis
39.32 Character arcs analysis
39.33 Genre analysis
39.34 Narrative point of no return analysis
39.35 Narrative resolution analysis
39.36 Narrative parallelism analysis
39.37 Narrative engagement analysis
## 40 Reasoning by Analogy
40.1 Comparing shapes using analogy
40.2 Analogical reasoning in mathematics
40.3 Analogies in language and linguistics
40.4 Analogical reasoning in problem-solving
40.5 Analogies in scientific reasoning
40.6 Analogical reasoning in artificial intelligence
40.7 Analogies in literature and storytelling
40.8 Analogical reasoning in decision making
40.9 Analogies in historical analysis
40.10 Analogical reasoning in philosophical arguments
40.11 Analogies in biological systems
40.12 Analogical reasoning in physics
40.13 Analogies in learning and education
40.14 Analogical reasoning in legal arguments
40.15 Analogies in cognitive psychology
40.16 Analogical reasoning in computer programming
40.17 Analogies in cultural analysis
40.18 Analogical reasoning in economics
40.19 Analogies in social sciences
40.20 Analogical reasoning in ethical debates
40.21 Analogies in medical diagnosis
40.22 Analogical reasoning in engineering design
40.23 Analogies in political analysis
40.24 Analogical reasoning in pattern recognition
40.25 Analogies in historical analogies
40.26 Analogical reasoning in problem-solving heuristics
40.27 Analogies in metaphorical thinking
40.28 Analogical reasoning in evolutionary biology
40.29 Analogies in moral reasoning
40.30 Analogical reasoning in logical puzzles
40.31 Analogies in artistic creation
40.32 Analogical reasoning in machine learning
40.33 Analogies in environmental analysis
40.34 Analogical reasoning in market research
40.35 Analogies in cognitive development
40.36 Analogical reasoning in teamwork and collaboration
40.37 Analogies in cultural metaphors
## 41 Abductive Reasoning
41.1 Non-declarative Memory Representations
41.2 Qualitative Reasoning
41.3 Qualitative Modeling
41.4 Abductive Networks
41.5 Statistical Relational Learning
41.6 Information Fusion
41.7 Qualitative Probability
41.8 Causal Reasoning
41.9 Qualitative Simulation
41.10 Knowledge Representation
41.11 Machine Learning
41.12 Shared Abductive Reasoning
41.13 Bayesian Reasoning
41.14 Causal Graphs
41.15 Probabilistic Argumentation
41.16 Abductive Inference
41.17 Logic-Based Reasoning
41.18 Justification-Based Explanation
41.19 Epistemic Planning
41.20 Automated Reasoning
41.21 Non-Monotonic Reasoning
41.22 Prototypes
41.23 Abductive Learning
41.24 Inductive Reasoning
41.25 Abductive Argumentation
41.26 Abductive Clustering
41.27 Abduction in Cognitive Psychology
41.28 Reasoning with Rules
41.29 Qualitative Spatial Reasoning
41.30 Abductive Explanation
41.31 Reasoning with Uncertainty
41.32 Abductive Perception
41.33 Inductive Inference
41.34 Structural Abduction
41.35 Application of Abduction
41.36 Diagnostic Reasoning
41.37 Abductive Planning
## 42 Incidental Reasoning
42.1 Environmental Consequences
42.2 Unexpected Challenges
42.3 Cognitive Biases
42.4 Structured Decisions
42.5 Judgmental Heuristics
42.6 Relationship Analysis
42.7 Consequence Evaluation
42.8 Comparative Analysis
42.9 Strategic Thinking
42.10 Novel Perspectives
42.11 Predictive Modeling
42.12 Logical Fallacies
42.13 Contextual Understanding
42.14 Creative Problem-Solving
42.15 Problem Framing
42.16 Prospective Reasoning
42.17 Self-Reflective Reasoning
42.18 Recognizing Patterns
42.19 Evidence-Based Theories
42.20 Explanatory Reasoning
42.21 Empirical Phenomena
42.22 Deductive Conclusions
42.23 Decision Trees
42.24 Systemic Conclusions
42.25 Critical Reasoning
42.26 Probabilistic Reasoning
42.27 Relational Correlations
42.28 Empirically Validated Assumptions
42.29 Data-Driven Processes
42.30 Analogical Reasoning
42.31 Non-Linear Approaches
42.32 Narrative Reasoning
42.33 Quantitative Modeling
42.34 Integrative Reasoning
42.35 Unanticipated Consequences
42.36 Applying Networks of Knowledge
42.37 Experimental Hypotheses |
alxcarln/codons | ---
task_categories:
- translation
size_categories:
- 100K<n<1M
---
# Fungal coding sequence dataset
Dataset of codon usage for fungal organisms created from the Ensembl Genomes clustered to 50% sequence identity at the protein level and split into 80%/10%/10% train/validation/test splits for use in training a neural network to design native-looking nucleotide sequences for fungal organisms
## Dataset processing
This document describes the preparation of the fungal codons
dataset.
### Obtaining the raw data
The raw data, CDS sequences for fungal organisms,
was obtained from [Ensembl Genomes](https://ensemblgenomes.org/) via the following URL
https://ftp.ensemblgenomes.ebi.ac.uk/pub/fungi/release-57/fasta/
All files were considered, and those matching the pattern
"*.cds.all.fa.gz" were downloaded with wget using the
following command
```shell
wget -r -np -nH -A "*.cds.all.fa.gz" \
ftp://ftp.ensemblgenomes.ebi.ac.uk/pub/fungi/release-57/fasta/
```
This results in a dataset of 775,642 nucleotide sequences from 1,506 individual
species represented in [Ensembl Genomes](https://ensemblgenomes.org/).
### Calling ORFs from the nucleotide sequences
For this step, we keep sequences that start with ATG and are an even
multiple of 3 with no ambiguous nucleotides. Also we remove sequences
that would result in a protein longer than 512 residues.
### Clustering at the protein level
Clustering was performed with MMseqs2 using commands like the following.
```shell
mmseqs createdb protein.fa proteinDB
mmseqs cluster -c 0.80 --min-seq-id 0.5 proteinDB clustDB tmp
mmseqs createsubdb clustDB proteinDB repDB
mmseqs convert2fasta repDB rep.fa
```
This produces 259,737 clusters at 50% identity (80% coverage for both sequences)
### Train/test splits
The dataset was split into 80% training examples (around 200k), 10% validation examples (around 20k), and 10% testing (around 20k) examples |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-52000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1078320
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
thu-coai/Safety-Prompts | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
pretty_name: Safety-Prompts
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
GitHub Repository: https://github.com/thu-coai/Safety-Prompts
Paper: https://arxiv.org/abs/2304.10436
|
dilanbakr/tquad | ---
dataset_info:
features:
- name: contexts
dtype: string
- name: questions
dtype: string
- name: answers
struct:
- name: answer_start
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 10360301
num_examples: 8308
- name: validation
num_bytes: 3803178
num_examples: 2676
download_size: 1618809
dataset_size: 14163479
---
# Dataset Card for "tquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
darknoon/noto-emoji-vector-512-svg | ---
dataset_info:
features:
- name: image
dtype: image
- name: codepoints
sequence: int64
- name: name
dtype: string
- name: text
dtype: string
- name: svg_path
dtype: string
- name: svg_text
dtype: string
splits:
- name: train
num_bytes: 90176885.81
num_examples: 2329
download_size: 74032133
dataset_size: 90176885.81
---
# Dataset Card for "noto-emoji-vector-512-svg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openpecha/stt-prodigy-finalised | ---
license: mit
---
**374,548 pairs** and a total of **307.34 hours** of Tibetan Speech-To-Text dataset created using the Prodigy annotation tool.
</br>All the transcripts have been reviewed by two people in addition to the original transcriber.
| dept | desc | hours |
|-------|-------------------|--------|
|STT_AB | Audio book | 0.03 |
|STT_CS | Children Speech | 60.44 |
|STT_NS | Natural Speech | 81.13 |
|STT_TT | Tibetan Teachings | 165.75 | |
CyberHarem/hiiragi_nemu_puellamagimadokamagicasidestorymagiarecord | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Hiiragi Nemu
This is the dataset of Hiiragi Nemu, containing 81 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 81 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 188 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 81 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 81 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 81 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 81 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 81 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 188 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 188 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 188 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
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