Md Zia Ullah (zuacubd)

zuacubd

Geek Repo

Company:Edinburgh Napier University

Location:Edinburgh, UK

Home Page:https://zuacubd.github.io

Twitter:@zuacsea

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Md Zia Ullah's repositories

Conversational_IR

Conversational information retrieval system: querying an IR system in multiple turns while the IR system uses the context to retrieve and rank the documents considering previous turns.

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cs224u

Code for Stanford CS224u

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DeepQPP

A deep learning model to predict the performance of your input query

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Group-QPP

The github repository of paper "Groupwise Query Performance Prediction with BERT" in ECIR2022.

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Search-intent-mining

Search intent mining through subtopics flat or hierarchical

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Depression

A model to detect depressed users using social mdeia

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FGPI

Fill in the people interaction gap

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beir

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

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CheatSheets

Just a place to store cheatsheets

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course-python-os

In the course, I taught the operating system functionality and its implementation using python programming

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DSSA

Document Sequence with Subtopic Attention

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erisk-depression-enu

Dataset for Erisk Depression

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Intro2AI-2022-CV-NLP-DL

Introduction to AI class (M2 level)

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LeCaRD

A Chinese legal case retrieval dataset.

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LLM-workshop-2024

A 4-hour coding workshop to understand how LLMs are implemented and used

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machine_learning_refined

Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

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MSMARCO-Passage-Ranking

MS MARCO(Microsoft Machine Reading Comprehension) is a large scale dataset focused on machine reading comprehension, question answering, and passage ranking. A variant of this task will be the part of TREC and AFIRM 2019. For Updates about TREC 2019 please follow This Repository Passage Reranking task Task Given a query q and a the 1000 most relevant passages P = p1, p2, p3,... p1000, as retrieved by BM25 a succeful system is expected to rerank the most relevant passage as high as possible. For this task not all 1000 relevant items have a human labeled relevant passage. Evaluation will be done using MRR

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notebooks-transformer

Jupyter notebooks for the Natural Language Processing with Transformers book

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QPP-bipartite-graph

Query performance prediction using Bipartite graph and query-dependent features

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QPP4CS

Query Performance Prediction for Conversational Search (QPP4CS)

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rank_llm

Repository for prompt-decoding using LLMs (GPT3.5, GPT4, Vicuna, and Zephyr)

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RankGPT

Is ChatGPT Good at Search? LLMs as Re-Ranking Agent [EMNLP 2023 Outstanding Paper Award]

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searchsolutions2022-tutorial

IR, Ranker, NeuralRanker

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ULTRA_pytorch

Unbiased Learning To Rank Algorithms (ULTRA)

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yake

Single-document unsupervised keyword extraction

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