Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
This work focuses on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real world tasks.
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
This paper presents a framework called search, label, and propagate (SLP) for bootstrapping intents from existing chat logs using weak supervision.
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
This work develops a rule-based NLP algorithm to automatically generate labels for the training data, and then use the pre-trained word embeddings as deep representation features for training machine learning models.
Introducing BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision.
This paper describes Snorkel, a system that enables users to help shape, create, and manage training data for Software 2.0 stacks.
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
