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Hi there, I'm Onur Sahil πŸ‘‹πŸΌπŸ‘¨πŸ»β€πŸ’»

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onursahil

I am a Data Scientist/Research Engineer in Natural Language Processing πŸ“Š, Pythonic programming 🐍, and open source :octocat:. I love to learn and contribute.

  • I’m currently working on Natural Language Processing & Understanding

  • I’m recently doing research on Large & Small Language Models

  • My recent project is on training models and building RAG applications with LLMs

  • I solve algorithm problems on Leetcode and Hackerrank

  • I’m looking to collaborate on exciting Data Science projects

  • Reach me: [email protected] or https://www.linkedin.com/in/onursahil

onursahil

My Github Stats

Onur Sahil Cerit's Projects

awesome-conformal-prediction icon awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

bert icon bert

TensorFlow code and pre-trained models for BERT

generative-ai-for-beginners icon generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI πŸ”— https://microsoft.github.io/generative-ai-for-beginners/

handson-ml icon handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

machine-learning-interview icon machine-learning-interview

Machine Learning Interviews from FAAG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

mpqg icon mpqg

Code corresponding to our paper "Leveraging Context Information for Natural Question Generation"

nqg_ass2s icon nqg_ass2s

Implementation of <Improving Neural Question Generation Using Answer Separation> by Yanghoon Kim et al., AAAI 2019

petitions_archive icon petitions_archive

μ²­μ™€λŒ€ ꡭ민청원 데이터 μ•„μΉ΄μ΄λΈŒ

qg icon qg

Implementation of the paper "Learning to Generate Questions by Learning What not to Generate"

question_generation icon question_generation

It is a question-generator model. It takes text and an answer as input and outputs a question.

rlseq2seq icon rlseq2seq

Deep Reinforcement Learning For Sequence to Sequence Models

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