PISA
layoutlmv3-sroie-token-classificationGuanRu09246006_-_mm_pisa_ini_r32_a32-ggufGuanRu09246006_-_mm_pisa_ini_r16_a16-ggufmeta-llama-Llama-3.1-8B-Instruct-pisanitizer-squad_v2-llm-judge-42-20260108-1706meta-llama-Llama-3.1-8B-Instruct-pisanitizer-squad_v2-llm-judge-42t5-base-customised-1k-tokens-pisa-state-only-finetunedmeta-llama-Llama-3.1-8B-Instruct-pisanitizer-squad_v2-sanitization-42-202601082138t5-small-pisa-state-only-finetuned
Datasets
All datasets matching “PISA”pisa-experiments
Pisa Experiments
This repository contains the PisaBench, training data, model checkpoints, introduced in PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop.
PisaBench
Real World Videos
We curate a dataset comprising 361 videos demonstrating the dropping task.Each video begins with an object suspended by an invisible wire in the first frame. We cut the video clips to begin as soon as the… See the full description on the dataset page: https://huggingface.co/datasets/nyu-visionx/pisa-experiments.PiSAs
PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems
Paper: arXiv:2607.05318
PiSAs (Privacy in Shared Agentic systems) is a benchmark for contextual privacy in multi-agent LLM systems. Each scenario puts an
executor agent in an organisation, gives it a decision to make, and spreads the evidence it needs
across colleagues — mixed in with private facts that are not legitimate inputs to that decision.
A system is scored both on getting the decision right and on… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/PiSAs.ciff-hub
CIFF Hub
Common Index File Format CIFF is an inverted index exchange format as defined as part of the Open-Source IR Replicability Challenge (OSIRRC) initiative.
The Ciff Hub hosts many indexes and queries for a variety of collections and models.
MS Marco
The MS Marco passage ranking dataset consists of 8.8M passages.
ESPLADE
Lassance, Carlos, and Stéphane Clinchant. "An efficiency study for splade models." Proceedings of the 45th International ACM SIGIR… See the full description on the dataset page: https://huggingface.co/datasets/pisa-engine/ciff-hub.msmarco-passage-v2-dedup.pisa
msmarco-passage-v2-dedup.pisa
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier_alpha as pta
artifact = pta.Artifact.from_hf('macavaney/msmarco-passage-v2-dedup.pisa')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"package_hint": "pyterrier_pisa"
}
msmarco-passage-v2.splade-lg.pisa
msmarco-passage-v2.splade-lg.pisa
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('pyterrier/msmarco-passage-v2.splade-lg.pisa')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"type": "sparse_index",
"format":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/msmarco-passage-v2.splade-lg.pisa.cord19.pisa
cord19.pisa
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier_alpha as pta
artifact = pta.Artifact.from_hf('macavaney/cord19.pisa')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"type": "sparse_index",
"format": "pisa",
"package_hint": "pyterrier-pisa",
"stemmer":… See the full description on the dataset page: https://huggingface.co/datasets/macavaney/cord19.pisa.