Subprober is a powerful and efficient subdomain scanning tool written in Python. With the ability to handle large lists of subdomains. The tool offers concurrent scanning, allowing users to define their preferred concurrency level for faster results. Whether you are on Linux, Windows, or macos

[NAACL 2021] Factual Probing Is [MASK]: Learning vs. Learning to Recall https://arxiv.org/abs/2104.05240

Network probing tool crafted for datacenters (but not only)

Framework for probing tasks

The official code and dataset for EMNLP 2022 paper "COPEN: Probing Conceptual Knowledge in Pre-trained Language Models".

This repo contains the official implementation of the paper "Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency"

[NAACL 2022 Findings]Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning

Official repository for the paper: "Probing LLMs for Joint Encoding of Linguistic Categories." Findings of EMNLP 2023.

Codebase for probing VFMs and Feature Upsamplers using Intractive Segmentation.

Memory efficient convolution networks

Probing for Predicate Argument Structures in Pretrained Language Models (ACL 2022).

Code and data for the EMNLP 2020 paper "Intrinsic Probing through Dimension Selection".

This repo contains a challenging dataset for sentiment analysis, as well as a python script to calculate per class results presented in at BlackboxNLP 2019

Code and data for the ACL 2022 paper "Probing as Quantifying Inductive Bias".

Repository with code for interpretability analyses of a recurrent neural network predicting German plural nouns.

Probe and cluster antibody paratopes and clonotypes

A framework for ASR probing using MDL approach

Automatic music tagging using foundation models

Latent-Explorer is the Python implementation of the framework proposed in the paper "Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph".

Probing Bert for part of speech (POS) and named entity recognition (NER) to alleviate the opacity of learned representations.