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README.md

@@ -31,22 +31,16 @@ widget:
 # Model Card for GeoLM model for Toponym Recognition
 
 <!-- Provide a quick summary of what the model is/does. [Optional] -->
-Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, then fine-tune it for Toponym Recognition task on GeoWebNews dataset
+A language model for detection 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
 
 
 
 
 #  Table of Contents
 
-- [Model Card for GeoLM model for Toponym Recognition](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
 - [Model Details](#model-details)
   - [Model Description](#model-description)
 - [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
 - [Bias, Risks, and Limitations](#bias-risks-and-limitations)
   - [Recommendations](#recommendations)
 - [Training Details](#training-details)
@@ -60,16 +54,10 @@ Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikiped
     - [Factors](#factors)
     - [Metrics](#metrics)
   - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
 - [Technical Specifications [optional]](#technical-specifications-optional)
   - [Model Architecture and Objective](#model-architecture-and-objective)
   - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
 - [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
 - [Model Card Authors [optional]](#model-card-authors-optional)
 - [Model Card Contact](#model-card-contact)
 - [How to Get Started with the Model](#how-to-get-started-with-the-model)
@@ -94,27 +82,14 @@ Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikiped
 # Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
+This is a fine-tuned GeoLM model for toponym detection task. The inputs are sentences and outputs are detected toponyms. Please refer to the demo on the right-side pannel for examples. 
 
 
 
-## Downstream Use [Optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
- 
-
 
 
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 
 
 
@@ -126,12 +101,6 @@ Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikiped
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 
 
-## Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-
-
 
 
 # Training Details
@@ -186,21 +155,7 @@ More information needed
 
 More information needed
 
-# Model Examination
 
-More information needed
-
-# Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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]
 
@@ -212,13 +167,6 @@ More information needed
 
 More information needed
 
-### Hardware
-
-More information needed
-
-### Software
-
-More information needed
 
 # Citation
 
@@ -232,15 +180,7 @@ More information needed
 
 More information needed
 
-# Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-More information needed
-
-# More Information [optional]
-
-More information needed
 
 # Model Card Authors [optional]