sarwar187

multilingual-event-retrieval

Given a event description as a search query in a source language this project aims to find similar events in a target language.

electra_pytorch

Pretrain and finetune ELECTRA with fastai and huggingface. (Results of the paper replicated !)

sremit

anserini

A Lucene toolkit for replicable information retrieval research

awesome-bert-nlp

A curated list of NLP resources focused on BERT, attention mechanism, Transformer networks, and transfer learning.

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

BERT-NER

Pytorch-Named-Entity-Recognition-with-BERT

BERT-related-papers

BERT-related papers

CloserLookFewShot

source code to ICLR'19, 'A Closer Look at Few-shot Classification'

CommonsenseERL_EMNLP_2019

Code for the EMNLP 2019 paper: Event Representation Learning Enhanced with External Commonsense Knowledge.

deeplearning-models

A collection of various deep learning architectures, models, and tips

ecg_framenet

Package for reading in FrameNet data and performing operations on it, such as creating ECG grammars.

eflomal

Efficient Low-Memory Aligner

Few-Shot-Learning

kb

KnowBert -- Knowledge Enhanced Contextual Word Representations

Keras-FewShotLearning

Some State-of-the-Art few shot learning algorithms in tensorflow 2

Low_Resource_KBP

knowledge graph population in low resource conditions

Multilingual-Model-Transfer

In this project we develop new deep learning models for bootstrapping language understanding models for languages with no labeled data using labeled data from other languages.

NCRFpp

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Neural-Query-Translation

Implementation of ACL '19 paper "A multi-task architecture for relevance based neural query translation"

OKELE

Open Knowledge Enrichment for Long-tail Entities, WWW 2020

OpenKP

Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

pytorch-rl

Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]

pytorch-seq2seq

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

sarwar187.github.io

sense2vec

🦆 Contextually-keyed word vectors

transformers

🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.

TREC_COVID

tripletloss

One Shot learning, Siamese networks and Triplet Loss with Keras

unsupervised_NER

Prototype unsupervised NER using BERT's MLM and wrapper around Dat Quoc Nguyen's POS tagger/Dependency parser