A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具,将PDF转换成Markdown和JSON格式。
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Updated
Jun 30, 2025 - Python
A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具,将PDF转换成Markdown和JSON格式。
Graphormer is a general-purpose deep learning backbone for molecular modeling.
A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.
The resources of LucaOne, including: the model code, training scripts, embedding inference code, and trained checkpoints.
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
Code and datasets for paper "K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization" in WSDM-2024
A language agent gym with challenging scientific tasks
[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models
Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023
A general AI agent framework that can be adapted to various tasks and environments.
Official code repo for the paper "LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset"
[ICLR'25] ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
A Text-guided Protein Design Framework, Nat Mach Intell 2025 (https://www.nature.com/articles/s42256-025-01011-z)
[ICML 2025] 🧬 ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
The official code for "TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation"
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
[ICLR 2022] The implementation for the paper "Equivariant Graph Mechanics Networks with Constraints".
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