Mo Arvan (mo-arvan)

mo-arvan

Geek Repo

Company:University of Illinois at Chicago

Location:Chicago

Home Page:https://mo-arvan.github.io/

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Mo Arvan's repositories

local-llm

docker compose configuration file for running Llama-2 or any other language model using huggingface text generation inference, and huggingface chat ui.

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phd-thesis-template

PhD Thesis template rebuilding Aaron Turon's thesis

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Bi-SimCut

Code for NAACL 2022 main conference paper "Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation"

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ClassKG

Weakly-supervised Text Classification Based on Keyword Graph

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data2text-human-evaluation-reproduction

Contains files used for the reproduction of data2text-human-evaluation

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GCN-text-class-reproduced

Graph Convolutional Networks for Text Classification. AAAI 2019

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label-studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format

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label-studio-ml-backend

Configs and boilerplates for Label Studio's Machine Learning backend

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label_rationale_association

Code for EMNLP 2021 paper "Measuring Association Between Labels and Free-Text Rationales"

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mo-arvan.github.io

A beautiful, simple, clean, and responsive Jekyll theme for academics

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modest-email

A markdown CSS module that is rather modest.

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NeuralTextSimplification

Exploring Neural Text Simplification

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ReasonBERT

Code and pre-trained models for "ReasonBert: Pre-trained to Reason with Distant Supervision", EMNLP'2021

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recommender-system

recommendation generation and ranking model

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reprohum-prolific-webapp

WebApp for running MTurk style HITs on a web server, with Prolific integration

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rescience-submission-73

[Re] Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress

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rethink_perturbations

Rethinking Perturbations in Encoder-Decoders for Fast Training

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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XLAnalysis5K

Code and data for EMNLP 2021 paper "A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space."

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