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Text preprocessing, representation and visualization from zero to hero.
The Supabase for RAG - R2R lets you build, scale, and manage user-facing Retrieval-Augmented Generation applications
Repository for experiments regarding the assessment of the suitability of LLMs for Knowledge Graph Completion task, using Mixtral-8x7b, GPT-3.5-turbo, and GPT-4o.
DSPy: The framework for programming—not prompting—foundation models
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
A WebUI for Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
List of references and online resources related to data science, machine learning and deep learning.
A project that uses Langchain to construct SPARQL queries against SPARQL endpoints
mOWL: Machine Learning library with Ontologies
☘️ A visualization grammar based on G2 for streamlit.
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
Instruct LLMs for flat and nested NER. Fine-tuning Llama and Mistral models for instruction named entity recognition. (Instruction NER)
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation, NAACL 2024
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning, CIKM 2022
Library for representing and working with ontologies in Python
python based framework for imperative representation of knowledge
A query language for exploring knowledge graphs.
Source code for XSearchKG, a full-stack web application for explainable keyword search over knowledge graphs
Deterministic LLMs Outputs for AI Applications and AI Agents
This is my personal knowledge-base. Here you'll find code-snippets, technical documentation, and command reference for various tools, and technologies.
PlaNet: Predicting population response to drugs via clinical knowledge graph
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases (https://stark.stanford.edu/)
Collection of SPARQL queries and their visualization
A system for quickly generating training data with weak supervision
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.