Upload README.md

README.md

@@ -41,22 +41,20 @@ A language model for detection toponyms (i.e. place names) from sentences. We pr
 - [Model Details](#model-details)
   - [Model Description](#model-description)
 - [Uses](#uses)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
 - [Training Details](#training-details)
   - [Training Data](#training-data)
   - [Training Procedure](#training-procedure)
     - [Preprocessing](#preprocessing)
     - [Speeds, Sizes, Times](#speeds-sizes-times)
 - [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
+  - [Testing Data, Metrics & Results](#testing-data-factors--metrics)
     - [Testing Data](#testing-data)
-    - [Factors](#factors)
     - [Metrics](#metrics)
-  - [Results](#results)
+    - [Results](#results)
 - [Technical Specifications [optional]](#technical-specifications-optional)
   - [Model Architecture and Objective](#model-architecture-and-objective)
   - [Compute Infrastructure](#compute-infrastructure)
+- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
 - [Citation](#citation)
 - [Model Card Authors [optional]](#model-card-authors-optional)
 - [Model Card Contact](#model-card-contact)
@@ -82,7 +80,7 @@ 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. -->
-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. 
+This is a fine-tuned GeoLM model for toponym detection task. The inputs are sentences and outputs are detected toponyms. 
 
 
 
@@ -90,17 +88,43 @@ This is a fine-tuned GeoLM model for toponym detection task. The inputs are sent
 <!-- 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." -->
 
 
+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"
 
-# Bias, Risks, and Limitations
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# 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."
 
-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.
+# Tokenize input sentence
+tokens = tokenizer.encode(input_sentence, truncation=True, padding=True, 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)
+```
+* Option 2: Load weights to a GeoLM model 
+
+To appear soon
 
 
 # Training Details
@@ -109,16 +133,14 @@ Significant research has explored bias and fairness issues with language models
 
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-More information on training data needed
-
+GeoWebNews (Credit to Gritta et al.)
+Download link: https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation/blob/master/data/GWN.xml 
 
 ## Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-### Preprocessing
 
-More information needed
 
 ### Speeds, Sizes, Times
 
@@ -130,7 +152,7 @@ More information needed
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-## Testing Data, Factors & Metrics
+## Testing Data & Metrics & Results
 
 ### Testing Data
 
@@ -139,19 +161,13 @@ More information needed
 More information needed
 
 
-### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-More information needed
-
 ### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
 More information needed
 
-## Results 
+### Results 
 
 More information needed
 
@@ -168,6 +184,16 @@ More information needed
 More information needed
 
 
+
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+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.
+
+
+
 # Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
@@ -182,23 +208,9 @@ More information needed
 
 
 
-# Model Card Authors [optional]
+# Model Card Author [optional]
 
 <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
 
-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.
-
-<details>
-<summary> Click to expand </summary>
-
-More information needed
+Zekun Li (li002666[Shift+2]umn.edu)
 
-</details>