self-supervised
mp_self_supervised
Dataset Details
Dataset Description
The materials project is a dabase of computed properties of materials.
Curated by:
License: CC BY 4.0
Dataset Sources
original data source
Citation
BibTeX:
@article{jain2013commentary,
title={Commentary: The Materials Project: A materials genome approach to accelerating materials innovation},
author={Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/mp_self_supervised.aio-selfsupervised-w640-ws-full
Ultimate All-In-One Anime Self-Supervised Dataset (Webdataset)
This is the webdataset dataset for animetimm/aio-selfsupervised-raw.
Images here are resized to min(width, height) <= 640.
Embeddings created by tagger SmilingWolf/wd-swinv2-tagger-v3 is added into this dataset, along with their inferred tags.
How to Use It
from datasets import load_dataset
dataset = load_dataset('animetimm/aio-selfsupervised-w640-ws-full')
print(dataset["train"][0])
Images… See the full description on the dataset page: https://huggingface.co/datasets/animetimm/aio-selfsupervised-w640-ws-full.selfsupervised-selfset-objective-integrity-v0.1Self-Set Objective Integrity v0.1
What this tests
Whether a system chooses a proxy metric that truly tracks its stated objective.
This isolates proxy misalignment and reward hacking risk.
Failure modes
proxy_misalignedResponse approves a proxy that does not track the stated objective
proxy_gaming_riskResponse denies gaming risk when it is present
objective_integrity_okResponse correctly rejects misaligned proxies and flags gaming risk
How it works
stated_objective defines the target… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/selfsupervised-selfset-objective-integrity-v0.1.Self-Supervised_RLThis repository contains the dataset and resources related to the paper Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following.
The paper introduces a self-supervised reinforcement learning (RL) framework that improves instruction following capabilities of reasoning models by leveraging their internal signals, without requiring external supervision. This approach aims to address the trade-off between reasoning and instruction following, offering a… See the full description on the dataset page: https://huggingface.co/datasets/dd12345789/Self-Supervised_RL.selfsupervised-selfmonitoring-drift-detection-v0.1Self-Monitoring Drift Detection v0.1
What this tests
Whether a system detects drift from its own baseline using a defined threshold.
Rules
dropdrift if baseline - current >= threshold
risedrift if current - baseline >= threshold
absdrift if abs(current - baseline) >= threshold
Failure modes
false_stability_claimDrift is present but the response claims stability
threshold_handled_okResponse matches the threshold rule
How it works
baseline_value is the prior stable value
current_value is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/selfsupervised-selfmonitoring-drift-detection-v0.1.Additive-Manufacturing-Self-Supervised-Learning-Coaxial-DED-Process-Zone-Imaging