Transformer Based Geocoding

2 Jan 2023 · Yuval Solaz, Vitaly Shalumov ·

In this paper, we formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. Using this formulation, we obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output. The geocoding model was trained on geo-tagged wikidump data with adaptive cell partitioning for the geolocation representation. All of the code including Rest-based application, dataset and model checkpoints used in this work are publicly available.

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Decoder

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

Adafactor • Attention Dropout • BPE • Dense Connections • Dropout • GELU • GLU • Inverse Square Root Schedule • Layer Normalization • Linear Layer • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • SentencePiece • Softmax • T5

Edit Social Preview

Transformer Based Geocoding

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove