Maryam-Nasseri
Applied and Quantitative Linguist (PhD) focusing on Natural Language Processing (NLP), machine learning and LLMs, statistical modelling, and Python/R.
UK
Pinned Repositories
AI-concepts
AI concepts, papers, algorithms, tutorials
CrewAI-Local-Agents-1
Local LLM set-up
Fine-tuning-LLMs-Locally
Fine-tuning LLMs Locally
Fine-tuning-with-QLoRA
Fine-tuning Large Language Models (LLMs) with Quantised Low-Ranl Adaptation
LCA-AW-Lexical-Complexity-Analyzer-for-Academic-Writing
LCA-AW (Lexical Complexity Analyzer for Academic Writing, Nasseri and Lu, 2019); version 2.1. This code is a modified version of the LCA (lexical complexity analyzer, described in Lu, 2012). The modified version integrated the BAWE (British Academic Written English) corpus' word list, the bawe_list.txt, that is a list of most frequently-used academic writing words in linguistics-related disciplines and language studies. The BNC-British National Corpus (or an option to use ANC- American National Corpus,) and the BAWE word lists act as filters for calculating lexical sophistication indices (see Lu, 2012). LCA-AW, will be suitable for analysing lexical complexity of academic writing in linguistics-related disciplines.
lex-syn-modelling
MoA-local
A simplified agentic workflow process based on the Mixture of Agents (MoA) system for Large Language Models (LLMs)
RAG-based-AI-Agents
Retrieval Augmented Generation-based Agentic CrewAI
SFA-Lexical-Complexity
Supplementary materials for the journal article Structural Factor Analysis of Lexical Complexity Constructs and Measures
Tweet-bot
Auto tweet bot with the Selenium webdriver
Maryam-Nasseri's Repositories
Maryam-Nasseri/RAG-based-AI-Agents
Retrieval Augmented Generation-based Agentic CrewAI
Maryam-Nasseri/CrewAI-Local-Agents-1
Local LLM set-up
Maryam-Nasseri/LCA-AW-Lexical-Complexity-Analyzer-for-Academic-Writing
LCA-AW (Lexical Complexity Analyzer for Academic Writing, Nasseri and Lu, 2019); version 2.1. This code is a modified version of the LCA (lexical complexity analyzer, described in Lu, 2012). The modified version integrated the BAWE (British Academic Written English) corpus' word list, the bawe_list.txt, that is a list of most frequently-used academic writing words in linguistics-related disciplines and language studies. The BNC-British National Corpus (or an option to use ANC- American National Corpus,) and the BAWE word lists act as filters for calculating lexical sophistication indices (see Lu, 2012). LCA-AW, will be suitable for analysing lexical complexity of academic writing in linguistics-related disciplines.
Maryam-Nasseri/AI-concepts
AI concepts, papers, algorithms, tutorials
Maryam-Nasseri/MoA-local
A simplified agentic workflow process based on the Mixture of Agents (MoA) system for Large Language Models (LLMs)
Maryam-Nasseri/Fine-tuning-with-QLoRA
Fine-tuning Large Language Models (LLMs) with Quantised Low-Ranl Adaptation
Maryam-Nasseri/SFA-Lexical-Complexity
Supplementary materials for the journal article Structural Factor Analysis of Lexical Complexity Constructs and Measures
Maryam-Nasseri/Tweet-bot
Auto tweet bot with the Selenium webdriver
Maryam-Nasseri/Fine-tuning-LLMs-Locally
Fine-tuning LLMs Locally
Maryam-Nasseri/lex-syn-modelling
Maryam-Nasseri/lex-syn-SVM
Maryam-Nasseri/syn-CUP
Supplementary materials for the CUP book chapter statistical modelling of syntactic complexity
Maryam-Nasseri/syn-JEAP
Supplementary materials for the article: Nasseri, M. (in press). Is postgraduate English academic writing more clausal or phrasal? Syntactic complexification at the crossroads of genre, proficiency, and statistical modelling, Journal of English for Academic Purposes. https://doi.org/10.1016/j.jeap.2020.100940 1475-1585/© 2020 Elsevier Ltd.
Maryam-Nasseri/AI-Agents-Systems-and-Research
New advances in AI Agents research and systems