MhmdSaiid
“If a machine is expected to be infallible, it cannot also be intelligent.” ― Alan Turing
Nice,France
MhmdSaiid's Stars
stanfordnlp/dspy
DSPy: The framework for programming—not prompting—foundation models
mlabonne/llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
huggingface/text-generation-inference
Large Language Model Text Generation Inference
langchain-ai/langchain
🦜🔗 Build context-aware reasoning applications
Hannibal046/Awesome-LLM
Awesome-LLM: a curated list of Large Language Model
openai/openai-python
The official Python library for the OpenAI API
karpathy/ng-video-lecture
Giskard-AI/giskard
🐢 Open-Source Evaluation & Testing for ML models & LLMs
hadyelsahar/RE-NLG-Dataset
T-Rex : A Large Scale Alignment of Natural Language with Knowledge Base Triples
Raldir/FEVEROUS
Repository for Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), accepted to NeurIPS 2021 Dataset and Benchmarks and used for the FEVER Workshop Shared Task at EMNLP2021.
facebookresearch/LAMA
LAnguage Model Analysis
facebookresearch/fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
CSAILVision/NetDissect
Network Dissection http://netdissect.csail.mit.edu for quantifying interpretability of deep CNNs.
Jimin9401/avocado
AVocaDo : Strategy for Adapting Vocabulary to Downstream Domain
Cartus/Automated-Fact-Checking-Resources
Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).
HLR/DomiKnowS
nyu-dl/NLP_DL_Lecture_Note
srush/streambook
Live Python Notebooks with any Editor
sylvainhalle/textidote
Spelling, grammar and style checking on LaTeX documents
ppapotti/expclaim
ExpClaim fact checks claims passed as triples with interpretable explanations of its assessement
GokuMohandas/mlops-course
Learn how to design, develop, deploy and iterate on production-grade ML applications.
GokuMohandas/Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
facebookarchive/bAbI-tasks
Task generation for testing text understanding and reasoning
google-research/text-to-text-transfer-transformer
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
pytorch/captum
Model interpretability and understanding for PyTorch
jalammar/ecco
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
Aluriak/clyngor
Handy python wrapper around Potassco's Clingo ASP solver.
PAIR-code/lit
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
d2l-ai/d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
jessevig/bertviz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)