Model Card for GeoLM model for Toponym Recognition
A language model for detecting toponyms (i.e. place names) from sentences. We pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, then fine-tune it for Toponym Recognition task on GeoWebNews dataset
[UPDATE]: Demo can be found here: https://huggingface.co/spaces/zekun-li/geolm-base-toponym-recognition-demo
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
- Model type: Language model for geospatial understanding
- Language(s) (NLP): en
- License: cc-by-nc-2.0
- Parent Model: https://huggingface.co/zekun-li/geolm-base-cased
Uses
This is a fine-tuned GeoLM model for toponym detection task. The inputs are sentences and outputs are detected toponyms.
To use this model, please refer to the code below.
- Option 1: Load weights to a BERT model (Same procedure as the demo on the right side panel)
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
# Model name from Hugging Face model hub
model_name = "zekun-li/geolm-base-toponym-recognition"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
# Example input sentence
input_sentence = "Minneapolis, officially the City of Minneapolis, is a city in the state of Minnesota and the county seat of Hennepin County."
# Tokenize input sentence
tokens = tokenizer.encode(input_sentence, return_tensors="pt")
# Pass tokens through the model
outputs = model(tokens)
# Retrieve predicted labels for each token
predicted_labels = torch.argmax(outputs.logits, dim=2)
predicted_labels = predicted_labels.detach().cpu().numpy()
# Decode predicted labels
predicted_labels = [model.config.id2label[label] for label in predicted_labels[0]]
# Print predicted labels
print(predicted_labels)
# ['O', 'B-Topo', 'O', 'O', 'O', 'O', 'O', 'B-Topo', 'O', 'O', 'O', 'O', 'O', 'O',
# 'O', 'O', 'B-Topo', 'O', 'O', 'O', 'O', 'O', 'B-Topo', 'I-Topo', 'I-Topo', 'O', 'O', 'O']
- Option 2: Load weights to a GeoLM model
To appear soon
Training Details
Training Data
GeoWebNews (Credit to Gritta et al.)
Download link: https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation/blob/master/data/GWN.xml
Training Procedure
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data & Metrics & Results
Testing Data
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Metrics
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Results
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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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.
Citation
BibTeX:
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APA:
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