language:
- en
thumbnail: url to a thumbnail used in social sharing
tags:
- toponym detection
- language model
- geospatial understanding
- geolm
license: cc-by-nc-2.0
datasets:
- GeoWebNews
metrics:
- f1
pipeline_tag: token-classification
widget:
- text: >-
Minneapolis, officially the City of Minneapolis, is a city in the state of
Minnesota and the county seat of Hennepin County.
- text: >-
Los Angeles, often referred to by its initials L.A., is the most populous
city in California, the most populous U.S. state. It is the commercial,
financial, and cultural center of Southern California. Los Angeles is the
second-most populous city in the United States after New York City, with a
population of roughly 3.9 million residents within the city limits as of
2020.
Model Card for GeoLM model for Toponym Recognition
Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, then fine-tune it for Toponym Recognition task on GeoWebNews dataset
Table of Contents
- Model Card for GeoLM model for Toponym Recognition
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
Model Details
Model Description
Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, then fine-tune it for Toponym Recognition task on GeoWebNews dataset
- Developed by: Zekun Li
- Model type: Language model for geospatial understanding
- Language(s) (NLP): en
- License: cc-by-nc-2.0
- Parent Model: https://huggingface.co/bert-base-cased
- Resources for more information: li002666[Shift+2]umn.edu
Uses
Direct Use
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
More information on training data needed
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed
Citation
BibTeX:
More information needed
APA:
More information needed
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Zekun Li
Model Card Contact
li002666[Shift+2]umn.edu
How to Get Started with the Model
Use the code below to get started with the model.
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