cta
Datasets
All datasets matching “cta”claude-protein-binder-design-dgui-corpus
Claude protein binder design — data release v1.0
1,440 de novo miniprotein binders (50 to 120 residues) against 16 targets, designed by two Claude models operating as autonomous protein-design agents (Mythos Preview, 900 designs; Opus 4.8, 540 designs) and characterized at two contract research organizations, Adaptyv Bio (cell-free expression; SPR/BLI kinetics with the design immobilized) and Twist Bioscience (Fc-fusion expression; capture SPR with a six-point antigen… See the full description on the dataset page: https://huggingface.co/datasets/ctaxnagomi/claude-protein-binder-design-dgui-corpus.record-screw-urThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "cta_ur_follower",
"total_episodes": 10,
"total_frames": 7519,
"total_tasks": 1,
"total_videos": 10,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/alex-cta/record-screw-ur.cta-bench
CTA-Bench v0.3
CTA-Bench evaluates statement-layer semantic faithfulness in Lean-facing algorithmic correctness obligations.
Summary
CTA-Bench v0.3 contains 84 algorithmic correctness-obligation instances across 12 classical algorithm families, 294 critical semantic units, reference obligations, code-context artifacts, generated Lean-facing obligation packets, strict and expanded result views, correction overlays, and human strict-overlap agreement reports.… See the full description on the dataset page: https://huggingface.co/datasets/fraware/cta-bench.DGUI_HYPERMEM-JEV
DGUI_HYPERMEM-JEV
The training "brain" for DGUI-HyperMem (DeckerGUI HyperMemory) — the self-hosted
memory MCP server. Every JEV reasoning decision the service makes is appended here as a
typed instruction row, so the corpus grows with real usage and can be used to fine-tune or
few-shot the JEV layer later.
Usage
from datasets import load_dataset
ds = load_dataset("ctaxnagomi/DGUI_HYPERMEM-JEV", split="train")
for row in ds.stream():
print(row["use_case"]… See the full description on the dataset page: https://huggingface.co/datasets/ctaxnagomi/DGUI_HYPERMEM-JEV.d2l4asr-wiki-jaCT-ADE-PT
CT-ADE-PT Dataset
Overview
The CT-ADE-PT is a multilabel classification dataset designed for predicting adverse drug events (ADEs) at the Preferred Term (PT) level of the MedDRA ontology using clinical trial data.
Key Features
Instances: 15'640
Unique Drugs: 2'497
Annotations: Preferred Term (PT) level of MedDRA
Dataset Splits
Train: 12'736 instances
Validation: 1'509 instances
Test: 1'395 instances
Citation… See the full description on the dataset page: https://huggingface.co/datasets/anthonyyazdaniml/CT-ADE-PT.