datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
yiming19/construction_place | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 268981588.0
num_examples: 32
download_size: 16173440
dataset_size: 268981588.0
---
# Dataset Card for "construction_place"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-132000 | ---
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: 665544
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
spac3dr3am/test-one | ---
license: afl-3.0
---
|
30830d/road-test | ---
license: mit
---
|
CVasNLPExperiments/docvqa_test_Salesforce_blip2-flan-t5-xxl_ns_5188 | ---
dataset_info:
features:
- name: question
dtype: string
- name: id
dtype: int64
- name: answers
sequence: string
- name: generated_answer
dtype: string
splits:
- name: train
num_bytes: 436040
num_examples: 5188
download_size: 231919
dataset_size: 436040
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Qdrant/arxiv-abstracts-instructorxl-embeddings | ---
language:
- en
pretty_name: InstructorXL embeddings of the Arxiv.org abstracts
task_categories:
- sentence-similarity
- feature-extraction
size_categories:
- 1M<n<10M
---
# arxiv-abstracts-instructorxl-embeddings
This dataset contains 768-dimensional embeddings generated from the [arxiv](https://arxiv.org/)
paper abstracts using [InstructorXL](https://huggingface.co/hkunlp/instructor-xl) model. Each
vector has an abstract used to create it, along with the DOI (Digital Object Identifier). The
dataset was created using precomputed embeddings exposed by the [Alexandria Index](https://alex.macrocosm.so/download).
## Generation process
The embeddings have been generated using the following instruction:
```text
Represent the Research Paper abstract for retrieval; Input:
```
The following code snippet shows how to generate embeddings using the InstructorXL model:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-xl')
sentence = "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train."
instruction = "Represent the Research Paper abstract for retrieval; Input:"
embeddings = model.encode([[instruction, sentence]])
``` |
ibivibiv/alpaca_tiny2 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 460831820
num_examples: 290901
download_size: 266582149
dataset_size: 460831820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
huggingface/autotrain-data-lw1q-vmpq-ylvw2 | Invalid username or password. |
hayleyg/yelp | ---
dataset_info:
features:
- name: label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 332000
num_examples: 2000
- name: test
num_bytes: 83000
num_examples: 500
download_size: 174280
dataset_size: 415000
---
# Dataset Card for "yelp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
weijie210/UFB_prefs_iter_0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: critique
dtype: string
- name: post_score
dtype: float64
- name: pre_score
dtype: float64
- name: score_diff
dtype: float64
- name: subsitute
dtype: bool
splits:
- name: train_prefs
num_bytes: 98739560
num_examples: 24069
- name: test_prefs
num_bytes: 3197544
num_examples: 781
download_size: 52757242
dataset_size: 101937104
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
---
|
tyzhu/ds_combined_200_try_lora_merge | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 20884.95238095238
num_examples: 200
- name: validation
num_bytes: 20884.95238095238
num_examples: 200
download_size: 26996
dataset_size: 41769.90476190476
---
# Dataset Card for "ds_combined_200_try_lora_merge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quocanh34/itay-hires-lora-dataset-v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 10693631.0
num_examples: 24
download_size: 10690080
dataset_size: 10693631.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Aakash941/THAR-Dataset | ---
license: cc-by-4.0
task_categories:
- text-classification
language:
- hi
- en
pretty_name: Targeted Hate Speech Against Religion
size_categories:
- 10K<n<100K
---
The dataset consists 11,549 YouTube comments in Hindi-English code-mixed language for targeted hate speech detection against religion. Binary and multi-class tagging of YouTube comments is used.
The classification of YouTube comments addresses two subtasks: Subtask-1 (Binary classification): comments are labeled as antireligion or non-antireligion. Subtask-2 (Multi-class classification): comments are labeled on the major targeted religions such as Islam, Hinduism, and Christianity, with a ‘none’ class also provided.
For more information, refer this paper: Sharma, D., Singh, A., & Singh, V. K. (2024). THAR-Targeted Hate Speech Against Religion: A high-quality Hindi-English code-mixed Dataset with the Application of Deep Learning Models for Automatic Detection. ACM Transactions on Asian and Low-Resource Language Information Processing.
https://doi.org/10.1145/3653017 |
Ozinex/Edneu | ---
license: openrail
---
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-6c534f-38130145047 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: minhtoan/t5-finetune-cnndaily-news
metrics: ['rouge', 'accuracy', 'bleu']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: minhtoan/t5-finetune-cnndaily-news
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@https://huggingface.co/Sini](https://huggingface.co/https://huggingface.co/Sini) for evaluating this model. |
open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp | ---
pretty_name: Evaluation run of Abhinav7/NeuralPipe-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Abhinav7/NeuralPipe-7B-slerp](https://huggingface.co/Abhinav7/NeuralPipe-7B-slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-14T11:22:42.602148](https://huggingface.co/datasets/open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp/blob/main/results_2024-01-14T11-22-42.602148.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\": 0.6447169263171724,\n\
\ \"acc_stderr\": 0.03211493893533018,\n \"acc_norm\": 0.6450175117328331,\n\
\ \"acc_norm_stderr\": 0.03277128130072703,\n \"mc1\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.5982418830210784,\n\
\ \"mc2_stderr\": 0.01515275893598861\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6467576791808873,\n \"acc_stderr\": 0.013967822714840055,\n\
\ \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693252\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6692889862577176,\n\
\ \"acc_stderr\": 0.004695076629884537,\n \"acc_norm\": 0.8611830312686716,\n\
\ \"acc_norm_stderr\": 0.003450488042965012\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\": 0.7039473684210527,\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\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n\
\ \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \
\ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n\
\ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n\
\ \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n\
\ \"acc_norm_stderr\": 0.04810840148082635\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\": 0.5787234042553191,\n\
\ \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n\
\ \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n\
\ \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"\
acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\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\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.02412112541694119,\n \
\ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.02412112541694119\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n\
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\
acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\
acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\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\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.02280138253459754,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.02280138253459754\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\": 0.8326947637292464,\n\
\ \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n\
\ \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.02386800326250011,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.02386800326250011\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.358659217877095,\n\
\ \"acc_stderr\": 0.016040454426164474,\n \"acc_norm\": 0.358659217877095,\n\
\ \"acc_norm_stderr\": 0.016040454426164474\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\
\ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47196870925684486,\n\
\ \"acc_stderr\": 0.012750151802922438,\n \"acc_norm\": 0.47196870925684486,\n\
\ \"acc_norm_stderr\": 0.012750151802922438\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\
\ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399673,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399673\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\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\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.5982418830210784,\n\
\ \"mc2_stderr\": 0.01515275893598861\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.797947908445146,\n \"acc_stderr\": 0.011285013754047436\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6929492039423806,\n \
\ \"acc_stderr\": 0.01270568572313171\n }\n}\n```"
repo_url: https://huggingface.co/Abhinav7/NeuralPipe-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|arc:challenge|25_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|gsm8k|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hellaswag|10_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-14T11-22-42.602148.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-14T11-22-42.602148.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- '**/details_harness|winogrande|5_2024-01-14T11-22-42.602148.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-14T11-22-42.602148.parquet'
- config_name: results
data_files:
- split: 2024_01_14T11_22_42.602148
path:
- results_2024-01-14T11-22-42.602148.parquet
- split: latest
path:
- results_2024-01-14T11-22-42.602148.parquet
---
# Dataset Card for Evaluation run of Abhinav7/NeuralPipe-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Abhinav7/NeuralPipe-7B-slerp](https://huggingface.co/Abhinav7/NeuralPipe-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-14T11:22:42.602148](https://huggingface.co/datasets/open-llm-leaderboard/details_Abhinav7__NeuralPipe-7B-slerp/blob/main/results_2024-01-14T11-22-42.602148.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": 0.6447169263171724,
"acc_stderr": 0.03211493893533018,
"acc_norm": 0.6450175117328331,
"acc_norm_stderr": 0.03277128130072703,
"mc1": 0.43084455324357407,
"mc1_stderr": 0.017335272475332366,
"mc2": 0.5982418830210784,
"mc2_stderr": 0.01515275893598861
},
"harness|arc:challenge|25": {
"acc": 0.6467576791808873,
"acc_stderr": 0.013967822714840055,
"acc_norm": 0.674061433447099,
"acc_norm_stderr": 0.013697432466693252
},
"harness|hellaswag|10": {
"acc": 0.6692889862577176,
"acc_stderr": 0.004695076629884537,
"acc_norm": 0.8611830312686716,
"acc_norm_stderr": 0.003450488042965012
},
"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": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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[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
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[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]
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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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ckoozzzu/test_14_2014 | ---
task_categories:
- text-classification
language:
- en
- de
tags:
- chemistry
pretty_name: Dick
--- |
playgroundai/MJHQ-30K | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': animals
'1': art
'2': fashion
'3': food
'4': indoor
'5': landscape
'6': logo
'7': people
'8': plants
'9': vehicles
splits:
- name: test
num_bytes: 9764107710
num_examples: 30000
download_size: 8838465412
dataset_size: 9764107710
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
task_categories:
- text-to-image
language:
- en
size_categories:
- 10K<n<100K
tags:
- text-to-image
- playground
---
# MJHQ-30K Benchmark
| Model | Overall FID |
| ------------------------------------- | ----- |
| SDXL-1-0-refiner | 9.55 |
| [playground-v2-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic) | **7.07** |
We introduce a new benchmark, **MJHQ-30K**, for automatic evaluation of a model’s aesthetic quality. The benchmark computes FID on a high-quality dataset to gauge aesthetic quality.
We curate the high-quality dataset from Midjourney with 10 common categories, each category with 3K samples. Following common practice, we use aesthetic score and CLIP score to ensure high image quality and high image-text alignment. Furthermore, we take extra care to make the data diverse within each category.
For Playground v2, we report both the overall FID and per-category FID. (All FID metrics are computed at resolution 1024x1024.) From the results, our model outperforms SDXL-1-0-refiner in overall FID and all the categories FID, especially in people and fashion categories. This is inline with the results of the user study, which indicates the correlation between human preferences and the FID score of the MJHQ30K benchmark.
We release this benchmark to the public and encourage the community to adopt it for benchmarking their models’ aesthetic quality.
Please see our [blog](https://blog.playgroundai.com/playground-v2/) for more details.
### Dataset Download
First, download `mjhq30k_imgs.zip`
```python
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id="playgroundai/MJHQ-30K",
filename="mjhq30k_imgs.zip",
local_dir="path/to/folder",
repo_type="dataset"
)
```
Unzip `mjhq30k_imgs.zip` into its per-category folder structure.
```
root
├── animals
├── art
├── fashion
├── food
├── indoor
├── landscape
├── logo
├── people
├── plants
└── vehicles
```
`meta_data.json` contains metadata including its category and the promp for all the image.
Here is one example. Note that the key is the name of the image file.
```json
"126c23ae9a879fdc05f355f9a72b418d589f3926": {
"category": "plants",
"prompt": "beautiful British garden5, clear sky, unreal engine, detailed, tropical plants ,strong ambient lighting, volumetric lighting, forest plants and leaves, strong light, a lot of leaves in font, 3D effect, 8k render, bright color rendering "
}
```
### Measure FID
To benchmark your model's performance, you need to first generate images using the same prompts in `meta_data.json`.
We calculate our FID using [clean-fid](https://github.com/GaParmar/clean-fid). You can measure the FID between the generated images and the reference images using
```python
from cleanfid import fid
score = fid.compute_fid(ref_dir, gen_dir)
```
### Contributor
Dataset curated by: [Playground](https://playground.com/) Research Team
|
EleutherAI/quirky_subtraction_increment0_alice_easy | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 3149505.1940104165
num_examples: 47750
- name: validation
num_bytes: 64327.0875
num_examples: 975
- name: test
num_bytes: 64819.5115
num_examples: 983
download_size: 842501
dataset_size: 3278651.7930104164
---
# Dataset Card for "quirky_subtraction_increment0_alice_easy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NathanGavenski/LunarLander-v2 | ---
license: mit
tags:
- Imitation Learning
- Expert Trajectory
pretty_name: LunarLander-v2 Expert Dataset
size_categories:
- 10M<n<100M
---
# LunarLander-v2 - Imitation Learning Datasets
This is a dataset created by [Imitation Learning Datasets](https://github.com/NathanGavenski/IL-Datasets) project.
It was created by using Stable Baselines weights from a PPO policy from [HuggingFace](https://huggingface.co/sb3/ppo-LunarLander-v2).
## Description
The dataset consists of 1,000 episodes with an average episodic reward of 500.
Each entry consists of:
```
obs (list): observation with length 8.
action (int): action (0, 1, 2 and 3).
reward (float): reward point for that timestep.
episode_returns (bool): if that state was the initial timestep for an episode.
```
## Usage
Feel free to download and use the `teacher.jsonl` dataset as you please.
If you are interested in using our PyTorch Dataset implementation, feel free to check the [IL Datasets](https://github.com/NathanGavenski/IL-Datasets/blob/main/src/imitation_datasets/dataset/dataset.py) project.
There, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace.
The Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary).
## Citation
Coming soon. |
qgallouedec/prj_gia_dataset_metaworld_handle_pull_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the handle-pull-v2 environment, sample for the policy handle-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_pull_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_pull_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
Nayanjit/OpenHathi-7B-finetune-summarization | ---
license: llama2
---
|
breno30/AlcyrCruz | ---
license: openrail
---
|
AntonioForte/UWDS | ---
task_categories:
- text-classification
language:
- en
license: apache-2.0
--- |
levalencia/emailstocustomer | ---
license: mit
---
|
CyberHarem/hiyori_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hiyori/槌永ヒヨリ/日和 (Blue Archive)
This is the dataset of hiyori/槌永ヒヨリ/日和 (Blue Archive), containing 198 images and their tags.
The core tags of this character are `long_hair, hair_over_one_eye, halo, side_ponytail, hairclip, hair_ornament, breasts, aqua_hair, hat, large_breasts, black_headwear, cabbie_hat, green_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 198 | 254.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyori_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 198 | 220.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyori_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 495 | 458.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyori_bluearchive/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/hiyori_bluearchive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, upper_body, long_sleeves, open_mouth, simple_background, white_background, white_scarf |
| 1 | 7 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, simple_background, solo, white_background, white_skirt, belt, blush, thigh_strap, white_scarf, cowboy_shot, open_mouth, grey_eyes |
| 2 | 7 |  |  |  |  |  | 1girl, black_footwear, full_body, long_sleeves, looking_at_viewer, shoes, solo, standing, white_skirt, thigh_strap, white_scarf, belt, black_socks, closed_mouth, cape, simple_background, white_background |
| 3 | 7 |  |  |  |  |  | 1girl, blush, long_sleeves, navel, simple_background, solo, stomach, white_skirt, lifted_by_self, looking_at_viewer, white_background, open_mouth, shirt_lift, very_long_hair, black_shirt, cowboy_shot, midriff |
| 4 | 5 |  |  |  |  |  | 1girl, black_shirt, open_mouth, short_sleeves, solo, blush, brown_apron, burger, holding_food, collared_shirt, simple_background, chibi, closed_eyes, looking_at_viewer, white_background |
| 5 | 8 |  |  |  |  |  | cleavage, navel, 1girl, collarbone, open_mouth, stomach, blush, eyewear_on_head, holding, looking_at_viewer, sunglasses, beach, outdoors, white_bikini, bare_shoulders, day, ocean, alternate_costume, bead_bracelet, blue_sky, food, front-tie_top, sitting, solo_focus, very_long_hair |
| 6 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, black_leotard, cleavage, fake_animal_ears, playboy_bunny, rabbit_ears, solo, strapless_leotard, alternate_costume, bare_shoulders, blush, open_mouth, pantyhose, collarbone, gloves, green_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | upper_body | long_sleeves | open_mouth | simple_background | white_background | white_scarf | white_skirt | belt | blush | thigh_strap | cowboy_shot | grey_eyes | black_footwear | full_body | shoes | standing | black_socks | closed_mouth | cape | navel | stomach | lifted_by_self | shirt_lift | very_long_hair | black_shirt | midriff | short_sleeves | brown_apron | burger | holding_food | collared_shirt | chibi | closed_eyes | cleavage | collarbone | eyewear_on_head | holding | sunglasses | beach | outdoors | white_bikini | bare_shoulders | day | ocean | alternate_costume | bead_bracelet | blue_sky | food | front-tie_top | sitting | solo_focus | black_leotard | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | pantyhose | gloves | green_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------|:---------------|:-------------|:--------------------|:-------------------|:--------------|:--------------|:-------|:--------|:--------------|:--------------|:------------|:-----------------|:------------|:--------|:-----------|:--------------|:---------------|:-------|:--------|:----------|:-----------------|:-------------|:-----------------|:--------------|:----------|:----------------|:--------------|:---------|:---------------|:-----------------|:--------|:--------------|:-----------|:-------------|:------------------|:----------|:-------------|:--------|:-----------|:---------------|:-----------------|:------|:--------|:--------------------|:----------------|:-----------|:-------|:----------------|:----------|:-------------|:----------------|:-------------------|:----------------|:--------------|:--------------------|:------------|:---------|:-------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | | X | X | X | X | | X | | X | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | | | X | X | X | | | | X | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | X | | | | X | | | | | | X | | | | | | | | | | | X | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | | | X | | | | | | | X | X | X | X | X | X | X | X |
|
SEACrowd/kopi_cc | ---
tags:
- self-supervised-pretraining
language:
- ind
---
# kopi_cc
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra "filtering" using deduplication technique
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Benoit},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{'a}rez and Laurent Romary and Benoit Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics.},
language = {en}
}
```
## License
CC0
## Homepage
[https://huggingface.co/datasets/munggok/KoPI-CC](https://huggingface.co/datasets/munggok/KoPI-CC)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
shi3z/ja_conv_wikipedia_llama2pro8b_3k | ---
license: llama2
task_categories:
- conversational
language:
- ja
size_categories:
- 1K<n<10K
---
This dataset is based on the Japanese version of Wikipedia dataset and converted into a multi-turn conversation format using llama2Pro8B. After generating 10,000 conversations and screening, only about 3,000 were usable, so I will publish them in this state first.
Since it is a llama2 license, it can be used commercially for services.
Some strange dialogue may be included as it has not been screened by humans.
We generated 10,000 conversations over 24 hours on an A100 80GBx7 machine and automatically screened them.
# Model
https://huggingface.co/spaces/TencentARC/LLaMA-Pro-8B-Instruct-Chat
# Dataset
https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720
# Compute by
Tsuginosuke AI SuperComputer
FreeAI Ltd.
https://free-ai.ltd |
mmajange/UKCH-iXBRL | ---
license: afl-3.0
---
|
gguichard/myriade_ontologie_w_synth_example_multi_v4 | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 36730918
num_examples: 68666
download_size: 6882026
dataset_size: 36730918
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "myriade_ontologie_w_synth_example_multi_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/tweet_eval_affix | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: words_with_affixes
sequence: string
splits:
- name: test
num_bytes: 137916
num_examples: 1060
download_size: 95675
dataset_size: 137916
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "tweet_eval_affix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maveriq/bisect | ---
task_categories:
- summarization
language:
- en
pretty_name: BiSECT
size_categories:
- 100K<n<1M
---
# Dataset Card for BiSECT
I am not the author of this dataset. I have only uploaded the data on HF for ease of availability. For all details on dataset curation and paper, please see relevant sections below.
## 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):** English
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/mounicam/BiSECT
- **Paper :** https://aclanthology.org/2021.emnlp-main.500/
## 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:**
[@inproceedings{bisect2021,
title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2021}
}]
## 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] |
mstz/haberman | ---
language:
- en
tags:
- haberman
- tabular_classification
- binary_classification
- multiclass_classification
pretty_name: Haberman
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- survival
license: cc
---
# Haberman
The [Haberman dataset](https://archive.ics.uci.edu/ml/datasets/Haberman) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Has the patient survived surgery?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------|
| sruvival | Binary classification | Has the patient survived surgery? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/haberman", "survival")["train"]
``` |
irds/medline_2017_trec-pm-2017 | ---
pretty_name: '`medline/2017/trec-pm-2017`'
viewer: false
source_datasets: ['irds/medline_2017']
task_categories:
- text-retrieval
---
# Dataset Card for `medline/2017/trec-pm-2017`
The `medline/2017/trec-pm-2017` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/medline#medline/2017/trec-pm-2017).
# Data
This dataset provides:
- `queries` (i.e., topics); count=30
- `qrels`: (relevance assessments); count=22,642
- For `docs`, use [`irds/medline_2017`](https://huggingface.co/datasets/irds/medline_2017)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/medline_2017_trec-pm-2017', 'queries')
for record in queries:
record # {'query_id': ..., 'disease': ..., 'gene': ..., 'demographic': ..., 'other': ...}
qrels = load_dataset('irds/medline_2017_trec-pm-2017', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Roberts2017TrecPm,
title={Overview of the TREC 2017 Precision Medicine Track},
author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant},
booktitle={TREC},
year={2017}
}
```
|
nayohan/translation_en_ko_datasets | ---
dataset_info:
features:
- name: domain
dtype: string
- name: subdomain
dtype: string
- name: style
dtype: string
- name: target
dtype: string
- name: source
dtype: string
- name: target_text
dtype: string
- name: source_text
dtype: string
splits:
- name: train
num_bytes: 1073364105.2644497
num_examples: 4637591
download_size: 584993856
dataset_size: 1073364105.2644497
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tyzhu/squad_qa_wrong_rare_v5_full_recite_ans_sent_no_permute_rerun | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 7960034.039930323
num_examples: 4778
- name: validation
num_bytes: 409972
num_examples: 300
download_size: 1464912
dataset_size: 8370006.039930323
---
# Dataset Card for "squad_qa_wrong_rare_v5_full_recite_ans_sent_no_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jgoodie/mediumroast-press-releases | ---
dataset_info:
features:
- name: Id
dtype: string
- name: Title
dtype: string
- name: Published
dtype: string
- name: Link
dtype: string
- name: Text
dtype: string
- name: Abouts
struct:
- name: About TransVoyant
dtype: string
- name: About Merck Global Health Innovation Fund
dtype: string
- name: About P74 Ventures
dtype: string
- name: About Historic Hotels of America
dtype: string
- name: About First Internet Bancorp
dtype: string
- name: About Mary Kay
dtype: string
- name: About United Nations Development Programme (UNDP)
dtype: string
- name: About China International Center for Economic and Technical Exchanges
(CICETE)
dtype: string
- name: About China Women’s Development Foundation (CWDF)
dtype: string
- name: About Ivanti
dtype: string
- name: About Brandon Hall Group
dtype: string
- name: About UBS
dtype: string
- name: About CDP
dtype: string
- name: About The SEAL Awards
dtype: string
- name: About CyrusOne
dtype: string
- name: About Vizient
dtype: string
- name: About KARL STORZ
dtype: string
- name: About Rupert Resources
dtype: string
- name: About Grain Sustainability
dtype: string
- name: About Garmin International, Inc.
dtype: string
- name: About CARFAX Canada
dtype: string
- name: About Edgecore
dtype: string
- name: About Cyware
dtype: string
- name: About CSW
dtype: string
- name: About Euromonitor International
dtype: string
- name: About FICO
dtype: string
- name: About Veritone
dtype: string
- name: About Seagate Technology
dtype: string
- name: About MedVector
dtype: string
- name: About Gain®
dtype: string
- name: About Procter & Gamble
dtype: string
- name: About SITE Centers Corp.
dtype: string
- name: About Ford Motor Company
dtype: string
- name: About CDK Global, Inc.
dtype: string
- name: About William Blair Investment Banking
dtype: string
- name: About William Blair
dtype: string
- name: About Postmedia Network Inc.
dtype: string
- name: About Sports Venture Holdings and BET99
dtype: string
- name: About Great Western Bank
dtype: string
- name: About Ambient Photonics
dtype: string
- name: About Origis Energy
dtype: string
- name: About Mitsubishi Power Americas, Inc.
dtype: string
- name: About Insider Connected
dtype: string
- name: About Stern Pinball, Inc.
dtype: string
- name: 'About Garmin:'
dtype: string
- name: 'About Navy Federal Credit Union:'
dtype: string
- name: About Carbon Robotics
dtype: string
- name: About Purpose Investments Inc.
dtype: string
- name: About Purpose Financial
dtype: string
- name: About Second Harvest
dtype: string
- name: About Kerrigan Advisors
dtype: string
- name: About InstaSafe
dtype: string
- name: About ZNet Technologies
dtype: string
- name: About RPtech
dtype: string
- name: About Xylem
dtype: string
- name: About Bobbie
dtype: string
- name: About Uber Eats
dtype: string
- name: About ConocoPhillips
dtype: string
- name: About Genius Sports
dtype: string
- name: About Walmart
dtype: string
- name: About
dtype: string
- name: About Historic Hotels Worldwide
dtype: string
- name: About Symetra
dtype: string
- name: About MCR
dtype: string
- name: About Bynder
dtype: string
- name: About Thomas H. Lee Partners, L.P.
dtype: string
- name: About Nickel 28
dtype: string
- name: About Critical Metals Corp.
dtype: string
- name: About European Lithium Ltd
dtype: string
- name: About Historic Hotels Worldwide®
dtype: string
- name: About KBRA
dtype: string
- name: About Knowles
dtype: string
- name: About Trillbit
dtype: string
- name: About UKG
dtype: string
- name: About Unravel Data
dtype: string
- name: About OmniVision
dtype: string
- name: About Seagate
dtype: string
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- name: About MATRIX Inc.
dtype: string
- name: About MATRIX GENESIS LABS (MGL)
dtype: string
- name: About MetaReal Co., Ltd.
dtype: string
- name: About OWC
dtype: string
- name: About Elior Group
dtype: string
- name: About FarEye
dtype: string
- name: About Dole plc
dtype: string
- name: 'About Forbright Bank:'
dtype: string
- name: About Trez Capital
dtype: string
- name: About Sharp/NEC
dtype: string
splits:
- name: train
num_bytes: 5620857.333333333
num_examples: 578
- name: test
num_bytes: 709900.6666666666
num_examples: 73
- name: valid
num_bytes: 700176.0
num_examples: 72
download_size: 5767270
dataset_size: 7030934.0
---
# Dataset Card for "mediumroast-press-releases"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
luizlzg/prefeitura_dataset_100topics_v1 | ---
task_categories:
- text-generation
language:
- pt
configs:
- config_name: default
data_files:
- split: train
path: dataset_instrutivo_100topics_treino*
- split: test
path: dataset_instrutivo_100topics_teste*
- split: validation
path: dataset_instrutivo_100topics_validation*
--- |
Multimodal-Fatima/VQAv2_test_no_image_split_7 | ---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_L_14_wo_openai
sequence: string
- name: clip_tags_ViT_L_14_with_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_with_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_with_openai
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_module
sequence: string
- name: captions_module_filter
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
splits:
- name: test
num_bytes: 2142349083
num_examples: 44779
download_size: 542841593
dataset_size: 2142349083
---
# Dataset Card for "VQAv2_test_no_image_split_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yuyijiong/multi-doc-qa-zh | ---
license: unknown
task_categories:
- text-generation
- question-answering
language:
- zh
---
多文档qa数据集，谷歌翻译成中文，用于微调长度更大的模型。\
任务：给定多个参考文档和一个问题，只有一个文档包含有用信息，模型需要根据参考文档回答问题，并指出哪个文档包含有用信息。\
对于每个question，会提供几十或上百个文档片段，只有一个文档包含有用信息，gold_document_id表示含有有用信息的文档序号，注意文档是从1开始编号。\
源数据来自 togethercomputer/Long-Data-Collections\ |
Anonimosos/bianca | ---
license: openrail
---
|
CNX-PathLLM/MultiConversation | ---
dataset_info:
features:
- name: image
dtype: image
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 1844814185.004
num_examples: 29636
download_size: 2210940073
dataset_size: 1844814185.004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
atom92/medical_healthwa_3.0 | ---
dataset_info:
features:
- name: text
struct:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2710809
num_examples: 7360
download_size: 586464
dataset_size: 2710809
---
# Dataset Card for "medical_healthwa_3.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/cecilia_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of cecilia (Fire Emblem)
This is the dataset of cecilia (Fire Emblem), containing 183 images and their tags.
The core tags of this character are `green_hair, long_hair, green_eyes, breasts, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 183 | 184.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 183 | 119.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 387 | 227.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 183 | 168.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 387 | 291.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_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/cecilia_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 | 16 |  |  |  |  |  | bare_shoulders, 1girl, cleavage, elbow_gloves, flower, smile, wedding_dress, white_dress, bridal_veil, solo, white_gloves, bride, bangs, blush, simple_background, bouquet, looking_at_viewer, tiara, strapless_dress, holding, official_alternate_costume, open_mouth, white_background, detached_collar, full_body, shiny_hair |
| 1 | 34 |  |  |  |  |  | 1girl, solo, cape, smile, elbow_gloves, dress, boots, simple_background, breastplate, white_gloves, full_body, white_background |
| 2 | 7 |  |  |  |  |  | 1girl, female_pubic_hair, nipples, solo, blush, nude, pussy, censored, colored_pubic_hair, medium_breasts |
| 3 | 31 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, nipples, blush, sex, penis, nude, open_mouth, mosaic_censoring, sweat, vaginal, cum, elbow_gloves |
| 4 | 7 |  |  |  |  |  | blush, hetero, nipples, 1girl, multiple_penises, solo_focus, vaginal, cum_in_pussy, cum_on_breasts, elbow_gloves, facial, gangbang, mosaic_censoring, rape, torn_clothes, 3boys, bukkake, cum_on_hair, handjob, nude, open_mouth, spread_legs, testicles, thighhighs, tongue_out, white_gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | bare_shoulders | 1girl | cleavage | elbow_gloves | flower | smile | wedding_dress | white_dress | bridal_veil | solo | white_gloves | bride | bangs | blush | simple_background | bouquet | looking_at_viewer | tiara | strapless_dress | holding | official_alternate_costume | open_mouth | white_background | detached_collar | full_body | shiny_hair | cape | dress | boots | breastplate | female_pubic_hair | nipples | nude | pussy | censored | colored_pubic_hair | medium_breasts | 1boy | hetero | solo_focus | sex | penis | mosaic_censoring | sweat | vaginal | cum | multiple_penises | cum_in_pussy | cum_on_breasts | facial | gangbang | rape | torn_clothes | 3boys | bukkake | cum_on_hair | handjob | spread_legs | testicles | thighhighs | tongue_out |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------|:--------|:-----------|:---------------|:---------|:--------|:----------------|:--------------|:--------------|:-------|:---------------|:--------|:--------|:--------|:--------------------|:----------|:--------------------|:--------|:------------------|:----------|:-----------------------------|:-------------|:-------------------|:------------------|:------------|:-------------|:-------|:--------|:--------|:--------------|:--------------------|:----------|:-------|:--------|:-----------|:---------------------|:-----------------|:-------|:---------|:-------------|:------|:--------|:-------------------|:--------|:----------|:------|:-------------------|:---------------|:-----------------|:---------|:-----------|:-------|:---------------|:--------|:----------|:--------------|:----------|:--------------|:------------|:-------------|:-------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 34 |  |  |  |  |  | | X | | X | | X | | | | X | X | | | | X | | | | | | | | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | | X | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 31 |  |  |  |  |  | | 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 | X | X | X |
|
Kannada-LLM-Labs/Laion-Coco-Kn | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: eng_caption
dtype: string
- name: score
dtype: float64
- name: kn_caption
dtype: string
splits:
- name: test
num_bytes: 5223531
num_examples: 14906
- name: train
num_bytes: 258046154
num_examples: 733604
download_size: 156666204
dataset_size: 263269685
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
license: mit
task_categories:
- visual-question-answering
language:
- kn
- en
size_categories:
- 100K<n<1M
---
[laion-coco](https://huggingface.co/datasets/laion/laion-coco) dataset with captions translated to Kannada language. The dataset contains 733604 training and
14906 test samples. Images can be downloaded directly from Coco page.
### Data Sample:
```python
{'id': 'dde3bdc5-36b7-4340-b2ae-d9564c0d213a',
'url': 'https://i.pinimg.com/236x/ca/84/a1/ca84a1d6f83c88c94452a94e320f024c--lens.jpg',
'eng_caption': 'Black and white photograph of woman in hat leaning against tree.',
'score': 5.8029,
'kn_caption': 'ಮರದ ವಿರುದ್ಧ ಒರಗಿರುವ ಟೋಪಿ ಹೊಂದಿರುವ ಮಹಿಳೆಯ ಕಪ್ಪು ಮತ್ತು ಬಿಳಿ ಛಾಯಾಚಿತ್ರ.'}
```
### Use with Datasets:
```python
from datasets import load_dataset
ds = load_dataset("Kannada-LLM-Labs/Laion-Coco-Kn")
``` |
krishan-CSE/Unified_Dataset | ---
license: apache-2.0
---
|
jxu124/OpenX-Embodiment | ---
license: cc-by-4.0
task_categories:
- robotics
- reinforcement-learning
language:
- en
tags:
- Robotics
pretty_name: Open X-Embodiment Dataset
size_categories:
- 1M<n<10M
---
# Open X-Embodiment Dataset (unofficial)
This is an unofficial Dataset Repo. This Repo is set up to make **Open X-Embodiment Dataset (55 in 1)** more accessible for people who love huggingface🤗.
**Open X-Embodiment Dataset** is the largest open-source real robot dataset to date. It contains 1M+ real robot trajectories spanning 22 robot embodiments, from single robot arms to bi-manual robots and quadrupeds.
More information is located on RT-X website (https://robotics-transformer-x.github.io/) .
### Usage Example
```python
import datasets
ds = datasets.load_dataset("jxu124/OpenX-Embodiment", "fractal20220817_data", streaming=True, split='train') # IterDataset
```
Optional subdatasets:
```
fractal20220817_data
kuka
bridge
taco_play
jaco_play
berkeley_cable_routing
roboturk
nyu_door_opening_surprising_effectiveness
viola
berkeley_autolab_ur5
toto
language_table
columbia_cairlab_pusht_real
stanford_kuka_multimodal_dataset_converted_externally_to_rlds
nyu_rot_dataset_converted_externally_to_rlds
stanford_hydra_dataset_converted_externally_to_rlds
austin_buds_dataset_converted_externally_to_rlds
nyu_franka_play_dataset_converted_externally_to_rlds
maniskill_dataset_converted_externally_to_rlds
furniture_bench_dataset_converted_externally_to_rlds
cmu_franka_exploration_dataset_converted_externally_to_rlds
ucsd_kitchen_dataset_converted_externally_to_rlds
ucsd_pick_and_place_dataset_converted_externally_to_rlds
austin_sailor_dataset_converted_externally_to_rlds
austin_sirius_dataset_converted_externally_to_rlds
bc_z
usc_cloth_sim_converted_externally_to_rlds
utokyo_pr2_opening_fridge_converted_externally_to_rlds
utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds
utokyo_saytap_converted_externally_to_rlds
utokyo_xarm_pick_and_place_converted_externally_to_rlds
utokyo_xarm_bimanual_converted_externally_to_rlds
robo_net
berkeley_mvp_converted_externally_to_rlds
berkeley_rpt_converted_externally_to_rlds
kaist_nonprehensile_converted_externally_to_rlds
stanford_mask_vit_converted_externally_to_rlds
tokyo_u_lsmo_converted_externally_to_rlds
dlr_sara_pour_converted_externally_to_rlds
dlr_sara_grid_clamp_converted_externally_to_rlds
dlr_edan_shared_control_converted_externally_to_rlds
asu_table_top_converted_externally_to_rlds
stanford_robocook_converted_externally_to_rlds
eth_agent_affordances
imperialcollege_sawyer_wrist_cam
iamlab_cmu_pickup_insert_converted_externally_to_rlds
uiuc_d3field
utaustin_mutex
berkeley_fanuc_manipulation
cmu_playing_with_food
cmu_play_fusion
cmu_stretch
berkeley_gnm_recon
berkeley_gnm_cory_hall
berkeley_gnm_sac_son
```
Optional subdatasets (Full Name):
```
RT-1 Robot Action
QT-Opt
Berkeley Bridge
Freiburg Franka Play
USC Jaco Play
Berkeley Cable Routing
Roboturk
NYU VINN
Austin VIOLA
Berkeley Autolab UR5
TOTO Benchmark
Language Table
Columbia PushT Dataset
Stanford Kuka Multimodal
NYU ROT
Stanford HYDRA
Austin BUDS
NYU Franka Play
Maniskill
Furniture Bench
CMU Franka Exploration
UCSD Kitchen
UCSD Pick Place
Austin Sailor
Austin Sirius
BC-Z
USC Cloth Sim
Tokyo PR2 Fridge Opening
Tokyo PR2 Tabletop Manipulation
Saytap
UTokyo xArm PickPlace
UTokyo xArm Bimanual
Robonet
Berkeley MVP Data
Berkeley RPT Data
KAIST Nonprehensile Objects
QUT Dynamic Grasping
Stanford MaskVIT Data
LSMO Dataset
DLR Sara Pour Dataset
DLR Sara Grid Clamp Dataset
DLR Wheelchair Shared Control
ASU TableTop Manipulation
Stanford Robocook
ETH Agent Affordances
Imperial Wrist Cam
CMU Franka Pick-Insert Data
QUT Dexterous Manpulation
MPI Muscular Proprioception
UIUC D3Field
Austin Mutex
Berkeley Fanuc Manipulation
CMU Food Manipulation
CMU Play Fusion
CMU Stretch
RECON
CoryHall
SACSoN
RoboVQA
ALOHA
```
## Copyright Notice
- This is an unofficial Dataset Repo.
- Copyright 2023 DeepMind Technologies Limited
- All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may
not use this file except in compliance with the Apache 2.0 license. You may obtain a
copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0
- All other materials are licensed under the Creative Commons Attribution 4.0
International License (CC-BY). You may obtain a copy of the CC-BY license at:
https://creativecommons.org/licenses/by/4.0/legalcode
- Unless required by applicable law or agreed to in writing, all software and materials
distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS"
BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied. See the licenses for the specific language governing permissions and
limitations under those licenses. |
Multimodal-Fatima/VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_bs_32
num_bytes: 29928
num_examples: 200
download_size: 14434
dataset_size: 29928
---
# Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irc_disentangle | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: irc-disentanglement
pretty_name: IRC Disentanglement
tags:
- conversation-disentanglement
dataset_info:
- config_name: ubuntu
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
- name: date
dtype: string
- name: connections
sequence: int32
splits:
- name: train
num_bytes: 56012854
num_examples: 220616
- name: validation
num_bytes: 3081479
num_examples: 12510
- name: test
num_bytes: 3919900
num_examples: 15010
download_size: 118470210
dataset_size: 63014233
- config_name: channel_two
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
- name: connections
sequence: int32
splits:
- name: dev
num_bytes: 197505
num_examples: 1001
- name: pilot
num_bytes: 92663
num_examples: 501
- name: test
num_bytes: 186823
num_examples: 1001
- name: pilot_dev
num_bytes: 290175
num_examples: 1501
- name: all_
num_bytes: 496524
num_examples: 2602
download_size: 118470210
dataset_size: 1263690
---
# Dataset Card for IRC Disentanglement
## 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)
- [Acknowledgments](#acknowledgments)
## Dataset Description
- **Homepage:** https://jkk.name/irc-disentanglement/
- **Repository:** https://github.com/jkkummerfeld/irc-disentanglement/tree/master/data
- **Paper:** https://aclanthology.org/P19-1374/
- **Leaderboard:** NA
- **Point of Contact:** jkummerf@umich.edu
### Dataset Summary
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. This new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. The dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context.
Note, the Github repository for the dataset also contains several useful tools for:
- Conversion (e.g. extracting conversations from graphs)
- Evaluation
- Preprocessing
- Word embeddings trained on the full Ubuntu logs in 2018
### Supported Tasks and Leaderboards
Conversational Disentanglement
### Languages
English (en)
## Dataset Structure
### Data Instances
For Ubuntu:
data["train"][1050]
```
{
'ascii': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'connections': [1048, 1054, 1055, 1072, 1073],
'date': '2004-12-25',
'id': 1050,
'raw': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'tokenized': "<s> ( also , i 'm guessing that this is n't a good place to report minor but annoying bugs ... what is ?) </s>"
}
```
For Channel_two:
data["train"][50]
```
{
'ascii': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'connections': [49, 53],
'id': 50,
'raw': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'tokenized': "<s> <user> : i do n't know off hand sorry </s>"
}
```
### Data Fields
'id' : The id of the message, this is the value that would be in the 'connections' of associated messages.
'raw' : The original message from the IRC log, as downloaded.
'ascii' : The raw message converted to ascii (unconvertable characters are replaced with a special word).
'tokenized' : The same message with automatic tokenisation and replacement of rare words with placeholder symbols.
'connections' : The indices of linked messages.
(only ubuntu) 'date' : The date the messages are from. The labelling for each date only start after the first 1000 messages of that date.
### Data Splits
The dataset has 4 parts:
| Part | Number of Annotated Messages |
| ------------- | ------------------------------------------- |
| Train | 67,463 |
| Dev | 2,500 |
| Test | 5,000 |
| Channel 2 | 2,600 |
## Dataset Creation
### Curation Rationale
IRC is a synchronous chat setting with a long history of use.
Several channels log all messages and make them publicly available.
The Ubuntu channel is particularly heavily used and has been the subject of several academic studies.
Data was selected from the channel in order to capture the diversity of situations in the channel (e.g. when there are many users or very few users).
For full details, see the [annotation information page](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/data/READ.history.md).
### Source Data
#### Initial Data Collection and Normalization
Data was collected from the Ubuntu IRC channel logs, which are publicly available at [https://irclogs.ubuntu.com/](https://irclogs.ubuntu.com/).
The raw files are included, as well as two other versions:
- ASCII, converted using the script [make_txt.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/make-txt.py)
- Tok, tokenised text with rare words replaced by UNK using the script [dstc8-tokenise.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/dstc8-tokenise.py)
The raw channel two data is from prior work [(Elsner and Charniak, 2008)](https://www.aclweb.org/anthology/P08-1095.pdf)].
#### Who are the source language producers?
The text is from a large group of internet users asking questions and providing answers related to Ubuntu.
### Annotations
#### Annotation process
The data is expert annotated with:
- Training, one annotation per line in general, a small portion is double-annotated and adjudicated
- Dev, Channel 2, double annotated and adjudicated
- Test, triple annotated and adjudicated
| Part | Annotators | Adjudication? |
| ------------- | --------------- | ------------------------------------- |
| Train | 1 or 2 per file | For files with 2 annotators (only 10) |
| Dev | 2 | Yes |
| Test | 3 | Yes |
| Channel 2 | 2 | Yes |
#### Who are the annotators?
Students and a postdoc at the University of Michigan.
Everyone involved went through a training process with feedback to learn the annotation guidelines.
### Personal and Sensitive Information
No content is removed or obfuscated.
There is probably personal information in the dataset from users.
## Considerations for Using the Data
### Social Impact of Dataset
The raw data is already available online and the annotations do not significantly provide additional information that could have a direct social impact.
### Discussion of Biases
The data is mainly from a single technical domain (Ubuntu tech support) that probably has a demographic skew of some sort.
Given that users are only identified by their self-selected usernames, it is difficult to know more about the authors.
### Other Known Limitations
Being focused on a single language and a single channel means that the data is likely capturing a particular set of conventions in communication.
Those conventions may not apply to other channels, or beyond IRC.
## Additional Information
### Dataset Curators
Jonathan K. Kummerfeld
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{kummerfeld-etal-2019-large,
title = "A Large-Scale Corpus for Conversation Disentanglement",
author = "Kummerfeld, Jonathan K. and
Gouravajhala, Sai R. and
Peper, Joseph J. and
Athreya, Vignesh and
Gunasekara, Chulaka and
Ganhotra, Jatin and
Patel, Siva Sankalp and
Polymenakos, Lazaros C and
Lasecki, Walter",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1374",
doi = "10.18653/v1/P19-1374",
pages = "3846--3856",
arxiv = "https://arxiv.org/abs/1810.11118",
software = "https://jkk.name/irc-disentanglement",
data = "https://jkk.name/irc-disentanglement",
abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.",
}
```
### Contributions
Thanks to [@dhruvjoshi1998](https://github.com/dhruvjoshi1998) for adding this dataset.
Thanks to [@jkkummerfeld](https://github.com/jkkummerfeld) for improvements to the documentation.
### Acknowledgments
This material is based in part upon work supported by IBM under contract 4915012629. Any opinions, findings, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of IBM. |
RohanHBTU/autotrain-data-t5-autotrain | ---
task_categories:
- translation
---
# AutoTrain Dataset for project: t5-autotrain
## Dataset Description
This dataset has been automatically processed by AutoTrain for project t5-autotrain.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": "SHOULD I WEAR A COAT TODAY ?",
"source": "Kya mujhe aj coat pehena chahiye ?",
"feat_en_parse": "[IN:GET_WEATHER SHOULD I WEAR A [SL:WEATHER_ATTRIBUTE COAT ] [SL:DATE_TIME TODAY ] ? ]",
"feat_cs_parse": "[IN:GET_WEATHER Kya mujhe [SL:DATE_TIME aj ] [SL:WEATHER_ATTRIBUTE coat ] pehena chahiye ? ]",
"feat_domain": "weather"
},
{
"target": "Label my timer as Gym Timer",
"source": "Mere timer ko Gym Timer ka label dijiye",
"feat_en_parse": "[IN:UNSUPPORTED_TIMER Label my timer as Gym Timer ]",
"feat_cs_parse": "[IN:UNSUPPORTED_TIMER Mere timer ko Gym Timer ka label dijiye ]",
"feat_domain": "timer"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='string', id=None)",
"source": "Value(dtype='string', id=None)",
"feat_en_parse": "Value(dtype='string', id=None)",
"feat_cs_parse": "Value(dtype='string', id=None)",
"feat_domain": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2394 |
| valid | 599 |
|
FelixdoingAI/IP2P-hiddenwm-200 | ---
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
- name: adversarial_image
dtype: image
splits:
- name: train
num_bytes: 104484241.0
num_examples: 200
download_size: 104481659
dataset_size: 104484241.0
---
# Dataset Card for "IP2P-hiddenwm-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/duke_of_york_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of duke_of_york/デューク・オブ・ヨーク/约克公爵 (Azur Lane)
This is the dataset of duke_of_york/デューク・オブ・ヨーク/约克公爵 (Azur Lane), containing 145 images and their tags.
The core tags of this character are `long_hair, breasts, pointy_ears, pink_hair, large_breasts, blue_eyes, bangs, earrings, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 145 | 220.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duke_of_york_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 145 | 116.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duke_of_york_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 350 | 247.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duke_of_york_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 145 | 191.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duke_of_york_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 350 | 366.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duke_of_york_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/duke_of_york_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 41 |  |  |  |  |  | cleavage, black_bra, epaulettes, 1girl, black_gloves, black_skirt, red_jacket, solo, looking_at_viewer, pleated_skirt, black_cape, jewelry, smile, black_pantyhose, long_sleeves, miniskirt, simple_background, holding_sword, red_cape, hair_between_eyes |
| 1 | 24 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, elbow_gloves, smile, cleavage_cutout, race_queen, black_thighhighs, covered_navel, garter_straps, jewelry, black_gloves, blush, skindentation, parted_lips, black_leotard, thighs |
| 2 | 9 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, cleavage, looking_at_viewer, solo, detached_sleeves, cross, strapless_dress, nail_polish, sitting, fishnet_thighhighs, hair_between_eyes, pink_eyes, red_hair, red_nails, smile, choker, closed_mouth, collarbone, holding_cup, jewelry, red_eyes, wine_glass |
| 3 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, blush, cleavage, collarbone, navel, simple_background, bare_shoulders, elf, jewelry, white_background, arm_under_breasts, breast_hold, choker, closed_mouth, cowboy_shot, finger_to_mouth, shiny_skin, white_bikini |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | cleavage | black_bra | epaulettes | 1girl | black_gloves | black_skirt | red_jacket | solo | looking_at_viewer | pleated_skirt | black_cape | jewelry | smile | black_pantyhose | long_sleeves | miniskirt | simple_background | holding_sword | red_cape | hair_between_eyes | bare_shoulders | elbow_gloves | cleavage_cutout | race_queen | black_thighhighs | covered_navel | garter_straps | blush | skindentation | parted_lips | black_leotard | thighs | black_dress | detached_sleeves | cross | strapless_dress | nail_polish | sitting | fishnet_thighhighs | pink_eyes | red_hair | red_nails | choker | closed_mouth | collarbone | holding_cup | red_eyes | wine_glass | navel | elf | white_background | arm_under_breasts | breast_hold | cowboy_shot | finger_to_mouth | shiny_skin | white_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:------------|:-------------|:--------|:---------------|:--------------|:-------------|:-------|:--------------------|:----------------|:-------------|:----------|:--------|:------------------|:---------------|:------------|:--------------------|:----------------|:-----------|:--------------------|:-----------------|:---------------|:------------------|:-------------|:-------------------|:----------------|:----------------|:--------|:----------------|:--------------|:----------------|:---------|:--------------|:-------------------|:--------|:------------------|:--------------|:----------|:---------------------|:------------|:-----------|:------------|:---------|:---------------|:-------------|:--------------|:-----------|:-------------|:--------|:------|:-------------------|:--------------------|:--------------|:--------------|:------------------|:-------------|:---------------|
| 0 | 41 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 24 |  |  |  |  |  | | | | X | X | | | X | X | | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | X | | | | X | X | | | X | X | | | | | | | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | | | | X | X | | | X | X | | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | X | X | X | | | | X | X | X | X | X | X | X | X | X |
|
Glac1er/glac1erdst | ---
license: unknown
---
|
sanjin7/copy_dataset_competitors | ---
dataset_info:
features:
- name: shop_id
dtype: int64
- name: ad_text
dtype: string
splits:
- name: train
num_bytes: 691250
num_examples: 2884
download_size: 421475
dataset_size: 691250
---
# Dataset Card for "copy_dataset_competitors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DZN222/rafael123 | ---
license: openrail
---
|
wenhanhan/FEVER_train | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 102726431
num_examples: 145449
download_size: 36028026
dataset_size: 102726431
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "FEVER_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_stsb_flat_adj_for_adv | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 2907
num_examples: 15
- name: test
num_bytes: 1194
num_examples: 9
- name: train
num_bytes: 3648
num_examples: 19
download_size: 14251
dataset_size: 7749
---
# Dataset Card for "MULTI_VALUE_stsb_flat_adj_for_adv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/eval_tag_nq_dev_v11_first | ---
dataset_info:
features:
- name: question
dtype: string
- name: title
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
- name: id
dtype: string
- name: titles
dtype: string
splits:
- name: train
num_bytes: 3340
num_examples: 10
- name: validation
num_bytes: 2403269
num_examples: 6515
download_size: 1389023
dataset_size: 2406609
---
# Dataset Card for "eval_tag_nq_dev_v11_first"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AlekseyKorshuk/wizardlm-alpaca-evol-instruct-chatml | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: do_train
dtype: bool
- name: role
dtype: string
splits:
- name: train
num_bytes: 96942441
num_examples: 54974
download_size: 46458065
dataset_size: 96942441
---
# Dataset Card for "wizardlm-alpaca-evol-instruct-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bluuwhale/custom-comui | ---
license: unknown
---
|
KvrParaskevi/hotel_data | ---
license: mit
---
|
CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ryuuzaki_kaoru/龍崎薫 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of ryuuzaki_kaoru/龍崎薫 (THE iDOLM@STER: Cinderella Girls), containing 357 images and their tags.
The core tags of this character are `short_hair, hair_ornament, hairclip, brown_hair, orange_hair, yellow_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 357 | 349.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 357 | 228.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 801 | 468.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 357 | 319.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 801 | 628.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryuuzaki_kaoru_idolmastercinderellagirls/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/ryuuzaki_kaoru_idolmastercinderellagirls',
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 | 6 |  |  |  |  |  | blush, looking_at_viewer, open_mouth, shorts, simple_background, teeth, white_background, 1girl, :d, solo, camisole, sunflower |
| 1 | 6 |  |  |  |  |  | 1girl, hat, looking_at_viewer, open_mouth, orange_shorts, smile, solo, white_gloves, navel, short_sleeves, simple_background, blush, boots, white_background, white_footwear, argyle, orange_headwear |
| 2 | 10 |  |  |  |  |  | 1girl, solo, card_(medium), character_name, sun_symbol, open_mouth, :d, jewelry, star_(symbol), skirt |
| 3 | 8 |  |  |  |  |  | 1girl, open_mouth, randoseru, solo, blush, shorts, looking_at_viewer, :d |
| 4 | 7 |  |  |  |  |  | green_jacket, 1girl, blue_shorts, blush, hooded_jacket, open_mouth, white_background, drawstring, hood_down, long_sleeves, open_jacket, simple_background, upper_teeth_only, :d, forehead, full_body, looking_at_viewer, short_shorts, star_(symbol), thick_eyebrows, bangs, collarbone, denim_shorts, necklace, red_shirt, shoes, solo_focus, white_socks |
| 5 | 7 |  |  |  |  |  | 1girl, blush, open_mouth, sleeveless_dress, solo, thick_eyebrows, upper_teeth_only, yellow_flower, :d, bare_shoulders, blue_sky, day, flower_field, outdoors, parted_bangs, bare_arms, cloud, forehead, ^_^, bow, collarbone, round_teeth, standing, sunflower_hair_ornament, white_dress, brown_eyes, brown_headwear, facing_viewer, looking_at_viewer, straw_hat, sundress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | looking_at_viewer | open_mouth | shorts | simple_background | teeth | white_background | 1girl | :d | solo | camisole | sunflower | hat | orange_shorts | smile | white_gloves | navel | short_sleeves | boots | white_footwear | argyle | orange_headwear | card_(medium) | character_name | sun_symbol | jewelry | star_(symbol) | skirt | randoseru | green_jacket | blue_shorts | hooded_jacket | drawstring | hood_down | long_sleeves | open_jacket | upper_teeth_only | forehead | full_body | short_shorts | thick_eyebrows | bangs | collarbone | denim_shorts | necklace | red_shirt | shoes | solo_focus | white_socks | sleeveless_dress | yellow_flower | bare_shoulders | blue_sky | day | flower_field | outdoors | parted_bangs | bare_arms | cloud | ^_^ | bow | round_teeth | standing | sunflower_hair_ornament | white_dress | brown_eyes | brown_headwear | facing_viewer | straw_hat | sundress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:---------|:--------------------|:--------|:-------------------|:--------|:-----|:-------|:-----------|:------------|:------|:----------------|:--------|:---------------|:--------|:----------------|:--------|:-----------------|:---------|:------------------|:----------------|:-----------------|:-------------|:----------|:----------------|:--------|:------------|:---------------|:--------------|:----------------|:-------------|:------------|:---------------|:--------------|:-------------------|:-----------|:------------|:---------------|:-----------------|:--------|:-------------|:---------------|:-----------|:------------|:--------|:-------------|:--------------|:-------------------|:----------------|:-----------------|:-----------|:------|:---------------|:-----------|:---------------|:------------|:--------|:------|:------|:--------------|:-----------|:--------------------------|:--------------|:-------------|:-----------------|:----------------|:------------|:-----------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | X | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | | | X | | | | | X | X | X | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | X | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
TokenBender/e5_FT_sentence_retrieval_task_Hindi | ---
license: apache-2.0
---
|
CyberHarem/kanzaki_ranko_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kanzaki_ranko/神崎蘭子 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of kanzaki_ranko/神崎蘭子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `grey_hair, red_eyes, long_hair, twintails, drill_hair, twin_drills, ribbon, breasts, 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 | 500 | 653.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanzaki_ranko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 420.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanzaki_ranko_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1265 | 897.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanzaki_ranko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 595.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanzaki_ranko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1265 | 1.14 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kanzaki_ranko_idolmastercinderellagirls/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/kanzaki_ranko_idolmastercinderellagirls',
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 | 7 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, looking_at_viewer, navel, solo, white_dress, white_thighhighs, bangs, cleavage, blush, bow, medium_breasts, sitting, smile, hair_between_eyes, hairband, jewelry, lace-trimmed_legwear, pink_flower, sleeveless_dress, white_rose |
| 1 | 6 |  |  |  |  |  | 1girl, black_dress, frills, gothic_lolita, hair_bow, long_sleeves, looking_at_viewer, solo, black_bow, choker, :d, bangs, open_mouth, upper_body, black_ribbon, collarbone, simple_background, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, gothic_lolita, solo, black_pantyhose, smile, looking_at_viewer, parasol, black_dress, frills |
| 3 | 8 |  |  |  |  |  | 1girl, gothic_lolita, smile, solo, dress, parasol, choker, open_mouth |
| 4 | 5 |  |  |  |  |  | 1girl, book, pantyhose, solo, gothic_lolita, looking_at_viewer, open_mouth, smile, blush, dress |
| 5 | 9 |  |  |  |  |  | 1girl, solo, hair_flower, smile, wings, looking_at_viewer, bare_shoulders, blush, detached_sleeves |
| 6 | 9 |  |  |  |  |  | 1girl, dress, flower, open_mouth, solo, smile, thighhighs, hair_ornament, hat, petals, bare_shoulders, detached_sleeves |
| 7 | 5 |  |  |  |  |  | 1girl, blush, open_mouth, smile, solo, dress, looking_at_viewer, mini_crown |
| 8 | 12 |  |  |  |  |  | 1girl, solo, horns, gloves, mini_crown, thighhighs, wings, medium_breasts, bandages, open_mouth, cleavage, :d |
| 9 | 5 |  |  |  |  |  | 1girl, fishnet_gloves, gothic_lolita, hair_down, long_sleeves, solo, black_dress, looking_at_viewer, butterfly_on_hand, :d, earrings, mini_hat, open_mouth, white_background |
| 10 | 5 |  |  |  |  |  | 1girl, smile, solo, striped_thighhighs, white_gloves, capelet, dress, looking_at_viewer, simple_background, bow, frills, open_mouth, white_background |
| 11 | 16 |  |  |  |  |  | 1girl, solo, elbow_gloves, medium_breasts, blush, cleavage, looking_at_viewer, black_bikini, smile, detached_collar, navel, black_thighhighs, frills, lolita_hairband |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | hair_flower | looking_at_viewer | navel | solo | white_dress | white_thighhighs | bangs | cleavage | blush | bow | medium_breasts | sitting | smile | hair_between_eyes | hairband | jewelry | lace-trimmed_legwear | pink_flower | sleeveless_dress | white_rose | black_dress | frills | gothic_lolita | hair_bow | long_sleeves | black_bow | choker | :d | open_mouth | upper_body | black_ribbon | collarbone | simple_background | white_background | black_pantyhose | parasol | dress | book | pantyhose | wings | detached_sleeves | flower | thighhighs | hair_ornament | hat | petals | mini_crown | horns | gloves | bandages | fishnet_gloves | hair_down | butterfly_on_hand | earrings | mini_hat | striped_thighhighs | white_gloves | capelet | elbow_gloves | black_bikini | detached_collar | black_thighhighs | lolita_hairband |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:--------------|:--------------------|:--------|:-------|:--------------|:-------------------|:--------|:-----------|:--------|:------|:-----------------|:----------|:--------|:--------------------|:-----------|:----------|:-----------------------|:--------------|:-------------------|:-------------|:--------------|:---------|:----------------|:-----------|:---------------|:------------|:---------|:-----|:-------------|:-------------|:---------------|:-------------|:--------------------|:-------------------|:------------------|:----------|:--------|:-------|:------------|:--------|:-------------------|:---------|:-------------|:----------------|:------|:---------|:-------------|:--------|:---------|:-----------|:-----------------|:------------|:--------------------|:-----------|:-----------|:---------------------|:---------------|:----------|:---------------|:---------------|:------------------|:-------------------|:------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | X | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | X | | X | | | | | | | | | X | | | | | | | | X | X | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | X | | | | | X | | | | | | | | | X | | | | | | | | | | X | | | | X | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | | X | | | | | X | | | | X | | | | | | | | | | X | | | | | | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | X | X | X | | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | X | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | | X | | | | | X | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | |
| 8 | 12 |  |  |  |  |  | X | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | X | | | X | | | | X | X | X | X | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | | | | | | | | X | | X | | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | | | X | | X | | | | | | X | | | X | | | | | | | | | X | | | | | | | X | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | | |
| 11 | 16 |  |  |  |  |  | X | | | X | X | X | | | | X | X | | X | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X |
|
dmrau/cqadupstack-physics | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: queries
num_bytes: 73255
num_examples: 1039
- name: corpus
num_bytes: 29949928
num_examples: 38316
download_size: 17827262
dataset_size: 30023183
---
# Dataset Card for "cqadupstack-physics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wrrdhfj/BanGDream | ---
license: unknown
---
|
gaizerick/vayne | ---
license: openrail
---
|
ayuhamaro/ws-pos-model-tune | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ws-pos-model-tune
pretty_name: WS POS Model Tune
train-eval-index:
- config: default
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ws_tags
sequence:
class_label:
names:
'0': B,
'1': I
- name: pos_tags
sequence:
class_label:
names:
'0': A,
'1': Caa,
'2': Cab,
'3': Cba,
'4': Cbb,
'5': D,
'6': Da,
'7': Dfa,
'8': Dfb,
'9': Di,
'10': Dk,
'11': DM,
'12': I,
'13': Na,
'14': Nb,
'15': Nc,
'16': Ncd,
'17': Nd,
'18': Nep,
'19': Neqa,
'20': Neqb,
'21': Nes,
'22': Neu,
'23': Nf,
'24': Ng,
'25': Nh,
'26': Nv,
'27': P,
'28': T,
'29': VA,
'30': VAC,
'31': VB,
'32': VC,
'33': VCL,
'34': VD,
'35': VF,
'36': VE,
'37': VG,
'38': VH,
'39': VHC,
'40': VI,
'41': VJ,
'42': VK,
'43': VL,
'44': V_2,
'45': DE,
'46': SHI,
'47': FW,
'48': COLONCATEGORY,
'49': COMMACATEGORY,
'50': DASHCATEGORY,
'51': DOTCATEGORY,
'52': ETCCATEGORY,
'53': EXCLAMATIONCATEGORY,
'54': PARENTHESISCATEGORY,
'55': PAUSECATEGORY,
'56': PERIODCATEGORY,
'57': QUESTIONCATEGORY,
'58': SEMICOLONCATEGORY,
'59': SPCHANGECATEGORY
splits:
- name: train
num_bytes: 1024
num_examples: 1
download_size: 1024
dataset_size: 1024
---
# Dataset Card for "WS POS Model Tune"
## 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:** None
- **Repository:** https://huggingface.co/datasets/ayuhamaro/nlp-model-tune
- **Paper:** [More Information Needed]
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 |
hashir123/huma | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_BFauber__lora_opt6.7b_10e5 | ---
pretty_name: Evaluation run of BFauber/lora_opt6.7b_10e5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BFauber/lora_opt6.7b_10e5](https://huggingface.co/BFauber/lora_opt6.7b_10e5)\
\ 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_BFauber__lora_opt6.7b_10e5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-10T02:11:19.300528](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_opt6.7b_10e5/blob/main/results_2024-02-10T02-11-19.300528.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\": 0.2579471750430987,\n\
\ \"acc_stderr\": 0.030703734066923796,\n \"acc_norm\": 0.25888864670457046,\n\
\ \"acc_norm_stderr\": 0.03148926211495383,\n \"mc1\": 0.2386780905752754,\n\
\ \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.37605500350105314,\n\
\ \"mc2_stderr\": 0.014217330165792038\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.34215017064846415,\n \"acc_stderr\": 0.013864152159177275,\n\
\ \"acc_norm\": 0.3703071672354949,\n \"acc_norm_stderr\": 0.01411129875167495\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4869547898824935,\n\
\ \"acc_stderr\": 0.004988082825213278,\n \"acc_norm\": 0.6565425214100776,\n\
\ \"acc_norm_stderr\": 0.004738920624724476\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.036906779861372814,\n\
\ \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.036906779861372814\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\
\ \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.02461829819586651,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.02461829819586651\n },\n\
\ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n\
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24855491329479767,\n\
\ \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.24855491329479767,\n\
\ \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349424,\n\
\ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349424\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.040493392977481404,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.040493392977481404\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n\
\ \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\
acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\
\ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\
\ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25483870967741934,\n\
\ \"acc_stderr\": 0.024790118459332215,\n \"acc_norm\": 0.25483870967741934,\n\
\ \"acc_norm_stderr\": 0.024790118459332215\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n\
\ \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\"\
: 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.28484848484848485,\n \"acc_stderr\": 0.035243908445117836,\n\
\ \"acc_norm\": 0.28484848484848485,\n \"acc_norm_stderr\": 0.035243908445117836\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365897,\n \"\
acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365897\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.23316062176165803,\n \"acc_stderr\": 0.030516111371476008,\n\
\ \"acc_norm\": 0.23316062176165803,\n \"acc_norm_stderr\": 0.030516111371476008\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.02075242372212801,\n \
\ \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.02075242372212801\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\
: 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22018348623853212,\n\
\ \"acc_stderr\": 0.017765978652327565,\n \"acc_norm\": 0.22018348623853212,\n\
\ \"acc_norm_stderr\": 0.017765978652327565\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.027920963147993656,\n\
\ \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.027920963147993656\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25980392156862747,\n \"acc_stderr\": 0.030778554678693264,\n \"\
acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.030778554678693264\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658335,\n \
\ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658335\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.19282511210762332,\n\
\ \"acc_stderr\": 0.02647824096048936,\n \"acc_norm\": 0.19282511210762332,\n\
\ \"acc_norm_stderr\": 0.02647824096048936\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.21374045801526717,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.21374045801526717,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2644628099173554,\n \"acc_stderr\": 0.04026187527591205,\n \"\
acc_norm\": 0.2644628099173554,\n \"acc_norm_stderr\": 0.04026187527591205\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3006134969325153,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.3006134969325153,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\
\ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\
\ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.039166677628225836,\n\
\ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.039166677628225836\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2564102564102564,\n\
\ \"acc_stderr\": 0.02860595370200425,\n \"acc_norm\": 0.2564102564102564,\n\
\ \"acc_norm_stderr\": 0.02860595370200425\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2720306513409962,\n\
\ \"acc_stderr\": 0.015913367447500514,\n \"acc_norm\": 0.2720306513409962,\n\
\ \"acc_norm_stderr\": 0.015913367447500514\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2976878612716763,\n \"acc_stderr\": 0.024617055388677003,\n\
\ \"acc_norm\": 0.2976878612716763,\n \"acc_norm_stderr\": 0.024617055388677003\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2733118971061093,\n\
\ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.2733118971061093,\n\
\ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.024922001168886324,\n\
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.024922001168886324\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2695035460992908,\n \"acc_stderr\": 0.026469036818590638,\n \
\ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.026469036818590638\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2685788787483703,\n\
\ \"acc_stderr\": 0.01132005662912173,\n \"acc_norm\": 0.2685788787483703,\n\
\ \"acc_norm_stderr\": 0.01132005662912173\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.16544117647058823,\n \"acc_stderr\": 0.022571771025494767,\n\
\ \"acc_norm\": 0.16544117647058823,\n \"acc_norm_stderr\": 0.022571771025494767\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.018120224251484587,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.018120224251484587\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\
\ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\
\ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.027212835884073153,\n\
\ \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.027212835884073153\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24875621890547264,\n\
\ \"acc_stderr\": 0.030567675938916707,\n \"acc_norm\": 0.24875621890547264,\n\
\ \"acc_norm_stderr\": 0.030567675938916707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.20481927710843373,\n\
\ \"acc_stderr\": 0.03141784291663925,\n \"acc_norm\": 0.20481927710843373,\n\
\ \"acc_norm_stderr\": 0.03141784291663925\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.03508771929824565,\n\
\ \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.03508771929824565\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2386780905752754,\n\
\ \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.37605500350105314,\n\
\ \"mc2_stderr\": 0.014217330165792038\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.013366596951934375\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.0016927007401501843\n }\n}\n```"
repo_url: https://huggingface.co/BFauber/lora_opt6.7b_10e5
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|arc:challenge|25_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|gsm8k|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hellaswag|10_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T02-11-19.300528.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T02-11-19.300528.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- '**/details_harness|winogrande|5_2024-02-10T02-11-19.300528.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-10T02-11-19.300528.parquet'
- config_name: results
data_files:
- split: 2024_02_10T02_11_19.300528
path:
- results_2024-02-10T02-11-19.300528.parquet
- split: latest
path:
- results_2024-02-10T02-11-19.300528.parquet
---
# Dataset Card for Evaluation run of BFauber/lora_opt6.7b_10e5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BFauber/lora_opt6.7b_10e5](https://huggingface.co/BFauber/lora_opt6.7b_10e5) 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_BFauber__lora_opt6.7b_10e5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-10T02:11:19.300528](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_opt6.7b_10e5/blob/main/results_2024-02-10T02-11-19.300528.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": 0.2579471750430987,
"acc_stderr": 0.030703734066923796,
"acc_norm": 0.25888864670457046,
"acc_norm_stderr": 0.03148926211495383,
"mc1": 0.2386780905752754,
"mc1_stderr": 0.014922629695456418,
"mc2": 0.37605500350105314,
"mc2_stderr": 0.014217330165792038
},
"harness|arc:challenge|25": {
"acc": 0.34215017064846415,
"acc_stderr": 0.013864152159177275,
"acc_norm": 0.3703071672354949,
"acc_norm_stderr": 0.01411129875167495
},
"harness|hellaswag|10": {
"acc": 0.4869547898824935,
"acc_stderr": 0.004988082825213278,
"acc_norm": 0.6565425214100776,
"acc_norm_stderr": 0.004738920624724476
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.036906779861372814,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.036906779861372814
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2,
"acc_stderr": 0.02461829819586651,
"acc_norm": 0.2,
"acc_norm_stderr": 0.02461829819586651
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.03295304696818318,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.03295304696818318
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237655,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237655
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.20425531914893616,
"acc_stderr": 0.026355158413349424,
"acc_norm": 0.20425531914893616,
"acc_norm_stderr": 0.026355158413349424
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.040493392977481404,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.040493392977481404
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2896551724137931,
"acc_stderr": 0.03780019230438014,
"acc_norm": 0.2896551724137931,
"acc_norm_stderr": 0.03780019230438014
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.26455026455026454,
"acc_stderr": 0.022717467897708617,
"acc_norm": 0.26455026455026454,
"acc_norm_stderr": 0.022717467897708617
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15079365079365079,
"acc_stderr": 0.03200686497287392,
"acc_norm": 0.15079365079365079,
"acc_norm_stderr": 0.03200686497287392
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.25483870967741934,
"acc_stderr": 0.024790118459332215,
"acc_norm": 0.25483870967741934,
"acc_norm_stderr": 0.024790118459332215
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2955665024630542,
"acc_stderr": 0.032104944337514575,
"acc_norm": 0.2955665024630542,
"acc_norm_stderr": 0.032104944337514575
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.28484848484848485,
"acc_stderr": 0.035243908445117836,
"acc_norm": 0.28484848484848485,
"acc_norm_stderr": 0.035243908445117836
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.25252525252525254,
"acc_stderr": 0.030954055470365897,
"acc_norm": 0.25252525252525254,
"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.23316062176165803,
"acc_stderr": 0.030516111371476008,
"acc_norm": 0.23316062176165803,
"acc_norm_stderr": 0.030516111371476008
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2128205128205128,
"acc_stderr": 0.02075242372212801,
"acc_norm": 0.2128205128205128,
"acc_norm_stderr": 0.02075242372212801
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26296296296296295,
"acc_stderr": 0.02684205787383371,
"acc_norm": 0.26296296296296295,
"acc_norm_stderr": 0.02684205787383371
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21008403361344538,
"acc_stderr": 0.026461398717471874,
"acc_norm": 0.21008403361344538,
"acc_norm_stderr": 0.026461398717471874
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.271523178807947,
"acc_stderr": 0.03631329803969653,
"acc_norm": 0.271523178807947,
"acc_norm_stderr": 0.03631329803969653
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.22018348623853212,
"acc_stderr": 0.017765978652327565,
"acc_norm": 0.22018348623853212,
"acc_norm_stderr": 0.017765978652327565
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.21296296296296297,
"acc_stderr": 0.027920963147993656,
"acc_norm": 0.21296296296296297,
"acc_norm_stderr": 0.027920963147993656
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25980392156862747,
"acc_stderr": 0.030778554678693264,
"acc_norm": 0.25980392156862747,
"acc_norm_stderr": 0.030778554678693264
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.26582278481012656,
"acc_stderr": 0.028756799629658335,
"acc_norm": 0.26582278481012656,
"acc_norm_stderr": 0.028756799629658335
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.19282511210762332,
"acc_stderr": 0.02647824096048936,
"acc_norm": 0.19282511210762332,
"acc_norm_stderr": 0.02647824096048936
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.21374045801526717,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.21374045801526717,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2644628099173554,
"acc_stderr": 0.04026187527591205,
"acc_norm": 0.2644628099173554,
"acc_norm_stderr": 0.04026187527591205
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.3006134969325153,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.3006134969325153,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.24107142857142858,
"acc_stderr": 0.04059867246952687,
"acc_norm": 0.24107142857142858,
"acc_norm_stderr": 0.04059867246952687
},
"harness|hendrycksTest-management|5": {
"acc": 0.1941747572815534,
"acc_stderr": 0.039166677628225836,
"acc_norm": 0.1941747572815534,
"acc_norm_stderr": 0.039166677628225836
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2564102564102564,
"acc_stderr": 0.02860595370200425,
"acc_norm": 0.2564102564102564,
"acc_norm_stderr": 0.02860595370200425
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2720306513409962,
"acc_stderr": 0.015913367447500514,
"acc_norm": 0.2720306513409962,
"acc_norm_stderr": 0.015913367447500514
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2976878612716763,
"acc_stderr": 0.024617055388677003,
"acc_norm": 0.2976878612716763,
"acc_norm_stderr": 0.024617055388677003
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24692737430167597,
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"acc_norm": 0.24692737430167597,
"acc_norm_stderr": 0.014422292204808835
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.25163398692810457,
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"acc_norm": 0.25163398692810457,
"acc_norm_stderr": 0.024848018263875195
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2733118971061093,
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"acc_norm": 0.2733118971061093,
"acc_norm_stderr": 0.02531176597542612
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2777777777777778,
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"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.024922001168886324
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.2695035460992908,
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"acc_norm": 0.2695035460992908,
"acc_norm_stderr": 0.026469036818590638
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2685788787483703,
"acc_stderr": 0.01132005662912173,
"acc_norm": 0.2685788787483703,
"acc_norm_stderr": 0.01132005662912173
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.16544117647058823,
"acc_stderr": 0.022571771025494767,
"acc_norm": 0.16544117647058823,
"acc_norm_stderr": 0.022571771025494767
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.018120224251484587,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.018120224251484587
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.20909090909090908,
"acc_stderr": 0.038950910157241364,
"acc_norm": 0.20909090909090908,
"acc_norm_stderr": 0.038950910157241364
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.23673469387755103,
"acc_stderr": 0.027212835884073153,
"acc_norm": 0.23673469387755103,
"acc_norm_stderr": 0.027212835884073153
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.24875621890547264,
"acc_stderr": 0.030567675938916707,
"acc_norm": 0.24875621890547264,
"acc_norm_stderr": 0.030567675938916707
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.20481927710843373,
"acc_stderr": 0.03141784291663925,
"acc_norm": 0.20481927710843373,
"acc_norm_stderr": 0.03141784291663925
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.03508771929824565,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.03508771929824565
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2386780905752754,
"mc1_stderr": 0.014922629695456418,
"mc2": 0.37605500350105314,
"mc2_stderr": 0.014217330165792038
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.013366596951934375
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401501843
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- 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:**
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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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## Dataset Card Contact
[More Information Needed] |
josephloh/donut-receipts75 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 12388370.0
num_examples: 52
- name: validation
num_bytes: 2401425.0
num_examples: 11
- name: test
num_bytes: 2915735.0
num_examples: 12
download_size: 17696976
dataset_size: 17705530.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
dhuynh95/Magicoder-Evol-Instruct-110K-Filtered_0.35 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 10675290
num_examples: 5000
download_size: 5542965
dataset_size: 10675290
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_186 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 646529360.0
num_examples: 125980
download_size: 655230708
dataset_size: 646529360.0
---
# Dataset Card for "chunk_186"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xaviviro/common_voice_16_1_ca_up_8 | ---
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
splits:
- name: train
num_bytes: 747445420.7396456
num_examples: 23151
- name: test
num_bytes: 18942707.0
num_examples: 525
download_size: 699210386
dataset_size: 766388127.7396456
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Thi4gomn/Voz_do_rascal3 | ---
license: openrail
---
|
Tushar3277/kyutech | ---
language:
- en
tags:
- medical
size_categories:
- n<1K
---
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as slowdown, slow_thoughts, impairment_hands, moodchange. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as rigidity, slowdown, slow_thoughts, impairment_hands, moodchange. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as . They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing deep sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing awake sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as tremors, anxiety, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, slow_thoughts, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing rem sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. They are experiencing light sleep. [/INST] To improve sleep quality, consider adjusting the medication timing to the afternoon, when it's associated with more restorative sleep types. Incorporating relaxation techniques before bedtime, establishing a regular sleep schedule, and ensuring a comfortable sleep environment can also help enhance sleep quality. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 71.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 73.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 79.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 94.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 78.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 81.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . Their stress score is 43.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 37.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as . Their stress score is 55.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 91.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 88.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as rigidity, slowdown, slow_thoughts, impairment_hands, moodchange. Their stress score is 89.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . Their stress score is 93.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 95.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 67.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 65.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 13.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 9.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . Their stress score is 77.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 38, sex female, with Hoehn and Yahr scale 3 takes their medication in the evening and reports symptoms such as . Their stress score is 73.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 20.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 21.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 79.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 77.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 70.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 48.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 79.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 73.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 77.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 79.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 25.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 18.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 0.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the morning and reports symptoms such as . Their stress score is 21.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 94.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the afternoon and reports symptoms such as . Their stress score is 93.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 88.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 43, sex female, with Hoehn and Yahr scale 2 takes their medication in the evening and reports symptoms such as . Their stress score is 75.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 78.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 31.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange. Their stress score is 72.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 29.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 61.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 60.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 67.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 62.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 76.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 22.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 70.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 66.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 61.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 17.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 24.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 38.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 82.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 37.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 37.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 2.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 22.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 10.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 17.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 88.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 2.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 70.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 84, sex male, with Hoehn and Yahr scale 4 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, slow_thoughts, moodchange, muscle_spasm. Their stress score is 15.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 71.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 19.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the morning and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 16.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s>
<s>[INST] A Parkinson's patient, age 58, sex male, with Hoehn and Yahr scale 3 takes their medication in the afternoon and reports symptoms such as pain, tremors, anxiety, rigidity, slowdown, slow_thoughts, impairment_hands, moodchange, muscle_spasm. Their stress score is 23.0. [/INST] To manage stress levels, consider adjusting medication timing away from the evening, where it's linked to higher stress scores. Engaging in regular physical activity, practicing mindfulness or relaxation techniques, and seeking social support can also contribute to stress reduction. </s> |
gguichard/myriade_ontologie | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 13863915
num_examples: 43590
download_size: 0
dataset_size: 13863915
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "myriade_ontologie"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigscience-data/roots_ca_wikimedia | ---
language: ca
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ca_wikimedia
# wikimedia_filtered
- Dataset uid: `wikimedia_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0005 % of total
- 0.0835 % of id
- 0.0126 % of ca
- 0.0054 % of pt
- 0.0005 % of indic-hi
### BigScience processing steps
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_1.0_seed_1 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
- name: input
dtype: string
- name: output
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- name: preference
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- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
- name: do_sample
dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
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num_examples: 18928
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num_examples: 18928
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num_examples: 18928
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num_bytes: 44546432
num_examples: 18928
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num_bytes: 44546730
num_examples: 18928
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num_examples: 18928
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num_examples: 18928
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num_examples: 18928
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num_bytes: 44547839
num_examples: 18928
- name: epoch_28
num_bytes: 44547965
num_examples: 18928
- name: epoch_29
num_bytes: 44548068
num_examples: 18928
download_size: 701209194
dataset_size: 1335662632
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
WuWenc/tiny_coco | ---
license: apache-2.0
---
---
dataset_info:
- config_name: train
features:
- name: filename
dtype: string
- name: height
dtype: int64
- name: width
dtype: int64
- name: ann
struct:
- name: bboxes
sequence:
sequence: float64
- name: bboxes_ignore
sequence:
sequence: int64
- name: label_ignore
sequence: int64
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 211748
num_examples: 500
download_size: 89624346
dataset_size: 211748
- config_name: val
features:
- name: filename
dtype: string
- name: height
dtype: int64
- name: width
dtype: int64
- name: ann
struct:
- name: bboxes
sequence:
sequence: float64
- name: bboxes_ignore
sequence:
sequence: int64
- name: label_ignore
sequence: int64
- name: labels
sequence: int64
splits:
- name: val
num_bytes: 209868
num_examples: 500
download_size: 82654443
dataset_size: 209868
---
|
Rasi1610/Deathce502_series2_3 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 173126795.0
num_examples: 317
- name: val
num_bytes: 42926147.0
num_examples: 80
download_size: 215969447
dataset_size: 216052942.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
|
valurank/Topic_Classification | ---
license: other
license_name: valurank
license_link: LICENSE
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- multi-class-classification
size_categories:
- 10K<n<100K
---
# Dataset Card for News_Topic_Classification
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
22462 News Articles classified into 120 different topics
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely article_text and topic.
The article_text column consists of the news article and the topic column consists of the topic each article belongs to
## Source Data
The dataset is scrapped from Otherweb database, some news sources, manually annotated by NLP Engineers at Otherweb and GPT-4 |
Qdrant/NOAA-Buoy | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: NOAA Buoy meterological data
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- feature-extraction
- tabular-classification
- time-series-forecasting
---
# NOAA Buoy meterological data
NOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets
1. From 1980 through 2022 (denoted with "years" in file names)
2. From Jan 2023 through end of Sept 2023 (denoted with "2023" in file names)
The original intended use is for anomaly detection in tabular data.
## Dataset Details
### Dataset Description
This dataset contains weather buoy data to be used in a tabular embedding scenarios.
Buoy 42002 was chosen because it had many years of historical data and was still actively collecting information
Here is the buoy's page and its historical data page:
- https://www.ndbc.noaa.gov/station_page.php?station=42002
- https://www.ndbc.noaa.gov/station_history.php?station=42002
Only standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.
### Data Fields
{'TSTMP': 'timestamp',
'#YY': '#yr',
' MM': 'mo',
'DD': 'dy',
'hh': 'hr',
'mm': 'mn',
'WDIR': 'degT',
'WSPD': 'm/s',
' GST': 'm/s',
' WVHT': 'm',
'DPD': 'sec',
'APD': 'sec',
'MWD ': 'degT',
'PRES': 'hPa',
' ATMP': 'degC',
' WTMP': 'degC'
}
## Dataset Creation
### Curation Rationale
The original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data
but kept separate to allow for train and inference.
### Source Data
#### Initial Data Collection and Normalization
Data Downloaded on Oct 12 2023
All code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code.
1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column.
2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00.
3. All missing data was set to a blank value rather than an actual number
4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD)
5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values
6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR
7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units.
These steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980
Since the original purpose of this data was anomaly detection. The two data sets above received further processing.
1. All data values were converted to Z-scores (file named zscore_2023)
1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years )
## Uses
### Direct Use
Primary use is working with tabular data and embeddings, particularly for anomaly detection
|
Anusha64/netflix-media | ---
license: mit
---
|
RikoteMaster/isear_for_llama2 | ---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
- name: text
dtype: string
splits:
- name: train
num_bytes: 4360637
num_examples: 8823
- name: test
num_bytes: 854222
num_examples: 1879
download_size: 2057989
dataset_size: 5214859
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "isear_for_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_GPT2_Baseline | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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dtype: string
splits:
- name: train
num_bytes: 115650065.625
num_examples: 37500
- name: test
num_bytes: 38550020.0
num_examples: 12500
download_size: 211782412
dataset_size: 154200085.625
---
# Dataset Card for "Spirit_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nayohan/feedback-collection-ko | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: orig_criteria
dtype: string
- name: orig_feedback
dtype: string
- name: orig_instruction
dtype: string
- name: orig_reference_answer
dtype: string
- name: orig_response
dtype: string
- name: orig_score
dtype: string
- name: orig_score1_description
dtype: string
- name: orig_score2_description
dtype: string
- name: orig_score3_description
dtype: string
- name: orig_score4_description
dtype: string
- name: orig_score5_description
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 766960620
num_examples: 99952
download_size: 342907606
dataset_size: 766960620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sdiazlor/evol-instruct-4-elimination-3.5-10samples | ---
dataset_info:
features:
- name: input
dtype: string
- name: generation_model
sequence: string
- name: generation_prompt
list:
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_generation_responses
sequence: string
- name: instructions
sequence: string
splits:
- name: train
num_bytes: 39052
num_examples: 9
download_size: 45048
dataset_size: 39052
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CronosGhost/code-reranking-NaturalLangQueries | ---
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: train
num_bytes: 63043927
num_examples: 9900
download_size: 26474163
dataset_size: 63043927
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Djulo/Wider_FaceSegLite | ---
license: apache-2.0
---
|
joey234/mmlu-high_school_us_history-neg-answer | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_answer
dtype: string
splits:
- name: test
num_bytes: 309230
num_examples: 204
download_size: 163790
dataset_size: 309230
---
# Dataset Card for "mmlu-high_school_us_history-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-one-sec-cv12/chunk_16 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1234601228
num_examples: 242459
download_size: 1255696002
dataset_size: 1234601228
---
# Dataset Card for "chunk_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ssunny112233/Sssssssssssssddddddddddddddddddddddddddddddd | ---
license: apache-2.0
---
|
zolutiontech/datasetConcordiumID | ---
license: apache-2.0
---
|
SEACrowd/identic | ---
tags:
- machine-translation
- pos-tagging
language:
- ind
- eng
---
# identic
IDENTIC is an Indonesian-English parallel corpus for research purposes.
The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide
researchers a proper Indonesian-English textual data set and also to promote research in this language pair.
The corpus contains texts coming from different sources with different genres.
Additionally, the corpus contains tagged texts that follows MorphInd tagset (Larasati et. al., 2011).
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{larasati-2012-identic,
title = "{IDENTIC} Corpus: Morphologically Enriched {I}ndonesian-{E}nglish Parallel Corpus",
author = "Larasati, Septina Dian",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/644_Paper.pdf",
pages = "902--906",
abstract = "This paper describes the creation process of an Indonesian-English parallel corpus (IDENTIC).
The corpus contains 45,000 sentences collected from different sources in different genres.
Several manual text preprocessing tasks, such as alignment and spelling correction, are applied to the corpus
to assure its quality. We also apply language specific text processing such as tokenization on both sides and
clitic normalization on the Indonesian side. The corpus is available in two different formats: plain',
stored in text format and morphologically enriched', stored in CoNLL format. Some parts of the corpus are
publicly available at the IDENTIC homepage.",
}
```
## License
CC BY-NC-SA 3.0
## Homepage
[https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0005-BF85-F](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0005-BF85-F)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
maywell/ko-calibration | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 55843115
num_examples: 38772
download_size: 31384444
dataset_size: 55843115
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 한국어 모델 캘리브레이션용 데이터셋
허깅페이스에 올라와 있는 다양한 한국어 데이터셋이 사용되었습니다. |
TopicNet/Lenta | ---
language:
- ru
multilinguality:
- monolingual
license: other
license_name: topicnet
license_link: >-
https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
- multi-label-classification
tags:
- topic-modeling
- topic-modelling
- text-clustering
- multimodal-data
- multimodal-learning
- modalities
- document-representation
---
# Lenta
Some measurable characteristics of the dataset:
* D — number of documents
* <modality name> W — modality dictionary size (number of unique tokens)
* <modality name> len D — average document length in modality tokens (number of tokens)
* <modality name> len D uniq — average document length in unique modality tokens (number of unique tokens)
| | D | @topmine W | @topmine len D | @topmine len D uniq | @time_n W | @time_n len D | @time_n len D uniq | @lemmatized_title W | @lemmatized_title len D | @lemmatized_title len D uniq | @lemmatized W | @lemmatized len D | @lemmatized len D uniq | @theme W | @theme len D | @theme len D uniq |
|:------|------------:|--------------------:|------------------------:|-----------------------------:|-------------------:|-----------------------:|----------------------------:|-----------------------------:|---------------------------------:|--------------------------------------:|-----------------------:|---------------------------:|--------------------------------:|------------------:|----------------------:|---------------------------:|
| value | 263557 | 2.32892e+07 | 88.365 | 83.8258 | 263557 | 1 | 1 | 2.05546e+06 | 7.79894 | 7.72848 | 2.90254e+07 | 110.13 | 84.5878 | 383816 | 1.45629 | 1.45629 |
Information about document lengths in modality tokens:
| | len_total@topmine | len_total@time_n | len_total@lemmatized_title | len_total@lemmatized | len_total@theme | len_uniq@topmine | len_uniq@time_n | len_uniq@lemmatized_title | len_uniq@lemmatized | len_uniq@theme |
|:-----|--------------------:|-------------------:|-----------------------------:|-----------------------:|------------------:|-------------------:|------------------:|----------------------------:|----------------------:|-----------------:|
| mean | 88.365 | 1 | 7.79894 | 110.13 | 1.45629 | 83.8258 | 1 | 7.72848 | 84.5878 | 1.45629 |
| std | 50.2072 | 0 | 1.86916 | 39.7804 | 0.722741 | 47.5763 | 0 | 1.81461 | 26.7959 | 0.722741 |
| min | 1 | 1 | 1 | 7 | 1 | 1 | 1 | 1 | 7 | 1 |
| 25% | 54 | 1 | 6 | 83 | 1 | 51 | 1 | 6 | 66 | 1 |
| 50% | 77 | 1 | 8 | 104 | 1 | 73 | 1 | 8 | 81 | 1 |
| 75% | 110 | 1 | 9 | 131 | 2 | 104 | 1 | 9 | 99 | 2 |
| max | 791 | 1 | 17 | 1000 | 3 | 647 | 1 | 16 | 542 | 3 |
|
presencesw/LLM_UIT_DATA | ---
dataset_info:
features:
- name: pairID
dtype: string
- name: evidence
dtype: string
- name: gold_label
dtype: string
- name: link
dtype: string
- name: context
dtype: string
- name: sentenceID
dtype: string
- name: claim
dtype: string
- name: annotator_labels
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 14846988.729050146
num_examples: 10459
- name: test
num_bytes: 14848408.270949854
num_examples: 10460
download_size: 10513358
dataset_size: 29695397.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
AlekseyKorshuk/DotCHA-100k-2D | ---
dataset_info:
features:
- name: '0'
dtype: string
- name: '1'
dtype: string
- name: letter
sequence: int64
- name: buckets
sequence:
sequence:
sequence: float64
splits:
- name: train
num_bytes: 4351176493
num_examples: 100000
download_size: 2830430898
dataset_size: 4351176493
---
# Dataset Card for "DotCHA-100k-2D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
micsell/hebrew_kan_sentence120000 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: language
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1830479133.0
num_examples: 10000
download_size: 1829673014
dataset_size: 1830479133.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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