Saeed Torabzadeh (zhn1010)

zhn1010

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Saeed Torabzadeh's repositories

Aspect-Based-Sentiment-Analysis

đź’­ Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

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BERT-Relation-Extraction

PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper

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BLINK

Entity Linker solution

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efficient-autoregressive-EL

Pytorch implementation of Highly Parallel Autoregressive Entity Linking with Discriminative Correction

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emoji-regex

A regular expression to match all Emoji-only symbols as per the Unicode Standard.

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EntQA

Pytorch implementation of EntQA paper

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epubReader

epubReader is a standalone fluid infusion component. This project is about implementing a web based ePub reader component based on open web technologies for infusion framework

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material-ui

MUI Core: Ready-to-use foundational React components, free forever. It includes Material UI, which implements Google's Material Design.

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mem_absa

Aspect Based Sentiment Analysis using End-to-End Memory Networks

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news-clustering

News clustering algorithm. Implementation of the "Multilingual Clustering of Streaming News" paper submitted to EMNLP 2018

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NumPyTutorial

This project is built to host NumPy Tutorial presentation file.

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rebel

REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).

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rnnlib

RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/

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SciREX

Data/Code Repository for https://api.semanticscholar.org/CorpusID:218470122

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scribe

Realistic Handwriting with Tensorflow

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spert

PyTorch code for SpERT: Span-based Entity and Relation Transformer

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SSAN

Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

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tensorflow-mnist-tutorial

Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.

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tensorflow-rnn-shakespeare

Code from the "Tensorflow and deep learning - without a PhD, Part 2" session on Recurrent Neural Networks.

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