datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
admin_privateCar_Race_AI_V0 #And the Devil said, "Let there be Air"
Open-Source and free to use dataset. Only ask is to cite if you are using code or dataset for research, publication etc.
This contains 30,000,000 training timesteps using Tensorflow 2.xx and PPO algorithm to train a single agent car racing utilizing CarRacing-v3 Gymnasium environment which is a maintained fork of OpenAI’s Gym library.
This agent behaviour is non-optimal as it… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Car_Race_AI_V0.Taxi-V4Asgard, Midgard, Vanaheimr, Jötunheimr, Alfheim, Svartalfheim, Niflheim, Muspelheim, Helheim
#RootsOfTheCosmos 9 Realms
The Taxi-v4 agent leartns to pick up and drop-off a passenger in their homes efficiently in a toy-text environment after 40million timesteps
Asgard: Realm of the Æsir gods e.g. Odin(The Mad One, AllFather), Loki and Thor.
Midgard: Realm of humans, located in the center of the cosmos and surrounded by a vast ocean patrolled by the serpent Jörmungandr.
Vanaheimr: Realm of the… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Taxi-V4.fer2013_train_publicTest_privateTest
Dataset Card for "fer2013_train_publicTest_privateTest"
More Information needed
Lunar_Lander-V3_Discretev2And the Devil said, I am Air, Space, Defence and Intelligence
And God said, I am Light, Water, Creation and Intelligence
-From Chaos Form. From Slience Storm. From Blood God's and Devil's reborn #YmirAwakening
-Only Fire and Ice = Locked in dark or Ethernal game
#Open-Source and free to use dataset. Only ask is to cite if you are using code or dataset for research, publication etc.
Agent lands successfully, but figures out a reward hack by bouncing for additional points.. #interesting. Average… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Lunar_Lander-V3_Discretev2.Car_Race_AI_V1 #And the Devil said, "Let there be Air"
#TheEnd #Open-Source and free to use dataset. Only ask is to cite if you are using code or dataset for research, publication etc.
This contains 30,000,000 training time-steps using Ubuntu Linux, Tensorflow 2.xx and PPO algorithm to train a single agent car racing utilizing CarRacing-v3 Gymnasium environment
which is a maintained fork of OpenAI’s Gym library.
Trained 30000000 but… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Car_Race_AI_V1.My-Saved-Private-DownloadsLunar_Lander-V3_Discrete
license: mit
size_categories:
10M<n<100M
And the Devil said, I am Air, Space, Defence and Intelligence
And God said, I am Light, Water, Creation and Intelligence
-From Chaos Form. From Slience Storm. From Blood God's and Devil's reborn #YmirAwakening
-Only Fire and Ice = Locked in dark or Ethernal game
#Open-Source and free to use dataset. Only ask is to cite if you are using code or dataset for research, publication etc.
---The agent performs successful landing with an average score of… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Lunar_Lander-V3_Discrete.Network_Defense_Symmetric_Competitive102,400,000 timesteps, Multi-Agent Reinforcement Learning
Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Timesteps
-The Red Agent’s goal is to discover vulnerabilities, elevate privileges, compromise assets, and maintain persistence. Its action space can be modeled after phases of the
MITRE ATT&CK framework.
-The Blue Agent’s goal is to maintain system availability, reduce the attack surface, detect malicious behavior… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Network_Defense_Symmetric_Competitive.SnakeAI_TF_PPO_V0Action mask has been implemented, the model has been updated to support 'Training Resumption' after system disruption. Utilizing the same training parameters as the "Full Reinforcement learning Agent", this agent prioritizes survival over rewards. It's playtime for 100 games is 6hrs, compared to 2hrs for the FRLA.
This demonstrates the agent is adapting for survival, but not to the desired goal of higher scores/reward. #10000000 training timesteps.
Training Hyperparameters is the same as the… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/SnakeAI_TF_PPO_V0.phone-archive-2026-08-04-privateSnakeAI_TF_PPO_V1The Hebrew word נָחָשׁ (Nāḥāš) is used in the Hebrew Bible to identify the serpent that appears in Genesis 3:1, in the Garden of Eden.
This contains #7000000 training parameters/timestep for Snake_AI game using TensorFlow 2.XX.
Best score and performance comes from data #4800000 dataset for ActorCritic, with an average score of 72 with no action mask/upfront rules. Full reinforcement learning with score/reward as a priority
Agent score can be improved with the combination of more training and… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/SnakeAI_TF_PPO_V1.Usdb_private
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
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]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/zxc4wewewe/Usdb_private.user-analytics-privateguided_genshin_impact_private_server_recordings_01
原神 raw recordings
This dataset contains raw game recordings managed by Game Data Platform. Access requests require manual approval.
Game: 原神 (Genshin Impact)
Collection: guided (精数据)
Subset: private_server (私服)
Recordings: 139
Planned bytes: 1113999401063
Layout: recordings//
Parquet files are intentionally excluded.
GenCAD-Code-Private-v2ContinuousMountainCar_Dynamic_Best_Tracking#4.5million timesteps
Included Dynamic Best Weight Tracking: Which Saves the best 15k deployment weights if performance improves to a seperate file
this allow the trained agent run infererence from the "improved performance file only" but the trained_agent viewpoint is
narrow. while the agent completes the goal, it is not super efficient due to edge usecases. #Good- #VeryGood
-Lokis Laugh is rising, like smoke above the flame, a trick without a master, a god without a name.
-Lokis Laugh is… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/ContinuousMountainCar_Dynamic_Best_Tracking.lop_mon_hinh_private_test_v1africa-synth-markets-private-health-insurance-all
Private Health Insurance Markets | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health datasets help… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-markets-private-health-insurance-all.GenCAD-Code-Private-v2libero-plus4-layout800-privatea-private-datasetprivate-finepersonas-with-all-questions
Dataset Card for private-finepersonas-with-all-questions
This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.
Using this dataset with Argilla
To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:
import argilla as rg
ds =… See the full description on the dataset page: https://huggingface.co/datasets/jfcalvo/private-finepersonas-with-all-questions.ko-en-vdr-private
Ko-En Visual Document Retrieval (VDR) Training Data
Multimodal retrieval training set used to fine-tune Qwen/Qwen3-VL-Embedding-2B
on mixed Korean and English visual-document retrieval: the query is text,
the document is a page image (PDF screenshot / slide / report / chart /
invoice / etc.), and each row ships 1 positive + 7 mined hard negatives.
Structure
This repo is a single dataset with two configs:
config
rows
description
corpus
207,522
deduplicated image… See the full description on the dataset page: https://huggingface.co/datasets/yjoonjang/ko-en-vdr-private.stable-diffusion-flagging-privateKvasir-VQA-x1-privatelop_mon_hinh_private_test_style2private_meta_sampledmil-privateKvasir-VQA-private