| | --- |
| | 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. As of the 2020 census the population was |
| | 429,954, making it the largest city in Minnesota and the 46th-most-populous in the |
| | United States. Nicknamed the "City of Lakes", Minneapolis is abundant in water, |
| | with thirteen lakes, wetlands, the Mississippi River, creeks, and waterfalls. |
| | - 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 |
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| | <!-- Provide a quick summary of what the model is/does. [Optional] --> |
| | 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 |
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| | [UPDATE]: Demo can be found here: https://huggingface.co/spaces/zekun-li/geolm-base-toponym-recognition-demo |
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| | # Model Details |
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| | ## Model Description |
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| | <!-- Provide a longer summary of what this model is/does. --> |
| | Pretrain the GeoLM model on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, then fine-tune it for Toponym Recognition task on GeoWebNews dataset |
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|
| | <!--- **Developed by:** UMN Knowledge Computing Lab & USC LUKA Lab --> |
| | - **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 |
| | <!---- **Resources for more information:** Zekun Li (li002666[Shift+2]umn.edu) --> |
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| | # Uses |
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| | <!-- 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. |
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| | <!-- 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." --> |
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| | To use this model, please refer to the code below. |
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| | * **Option 1:** Load weights to a BERT model (Same procedure as the demo on the right side panel) |
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| | ```python |
| | |
| | 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 |
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|
| | To appear soon |
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| | # Training Details |
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| | ## Training Data |
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| | <!-- 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. --> |
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| | **GeoWebNews** (Credit to [Gritta et al.](https://arxiv.org/pdf/1810.12368.pdf)) |
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| | Download link: https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation/blob/master/data/GWN.xml |
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| | ## Training Procedure |
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| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| | ### Speeds, Sizes, Times |
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| | <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| | More information needed |
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| | # Evaluation |
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| | <!-- This section describes the evaluation protocols and provides the results. --> |
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| | ## Testing Data & Metrics & Results |
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| | ### Testing Data |
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| | <!-- This should link to a Data Card if possible. --> |
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| | More information needed |
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| | ### Metrics |
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| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| | More information needed |
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| | ### Results |
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| | More information needed |
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| | # Technical Specifications [optional] |
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| | ## Model Architecture and Objective |
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| | More information needed |
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| | ## Compute Infrastructure |
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| | More information needed |
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| | # Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | 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. |
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| | # Citation |
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| | <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| | **BibTeX:** |
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| | More information needed |
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| | **APA:** |
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| | More information needed |
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| | # Model Card Author [optional] |
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| | <!-- 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. --> |
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| | <!--- Zekun Li (li002666[Shift+2]umn.edu) --> |
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