pcyin

tranX

A general-purpose neural semantic parser for mapping natural language queries into machine executable code

NL2code

A syntactic neural model for parsing natural language to executable code

pytorch_nmt

A neural machine translation model in PyTorch

pytorch_basic_nmt

A simple yet strong implementation of neural machine translation in pytorch

PyRouge

A python library to compute rouge score for summarization

pytorch-gated-graph-neural-network

A simple Pytorch implementation of Gated Graph Neural Networks

pytorch_neural_symbolic_machines

A PyTorch Implementation of Neural Symbolic Machines by Liang et al. (2018)

structVAE

dire

Neural Variable Renaming for Decompiled Binaries

zeroshot_parser

A zero-shot neural semantic parser without using annotated parallel training data.

dynet_nmt

A neural machine translation model in DyNet

allennlp

An open-source NLP research library, built on PyTorch.

bilstm_crf

datasets

🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools

nn4nlp-code

Code Samples from Neural Networks for NLP

pynlpl

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

TaBERT

This repository contains source code for the TaBERT model, a pre-trained language model for learning joint representations of natural language utterances and (semi-)structured tables for semantic parsing. TaBERT is pre-trained on a massive corpus of 26M Web tables and their associated natural language context, and could be used as a drop-in replacement of a semantic parsers original encoder to compute representations for utterances and table schemas (columns).

codalab-worksheets

A collaborative platform for reproducible research (web interface and CLI).

dynet

DyNet: The Dynamic Neural Network Toolkit

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

improving-compgen-in-semparse

my_fairseq

neural-symbolic-machines

Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.

pbmt_assignment

11731 assignment 2

pytorch-generative-adversarial-networks

A very simple generative adversarial network (GAN) in PyTorch

scone-executor

Executor for the SCONE dataset

sempre

Semantic Parser with Execution

spaCy

💫 Industrial-strength Natural Language Processing (NLP) in Python

tir_submit_job

transformers-1

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