---
library_name: transformers
license: apache-2.0
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model was fine-tuned on addresses from Canada open data portal to parse Canadian addresses into ["B-STREET_NO", "I-STREET_NO", "B-STREET_NAME", "I-STREET_NAME", "B-STREET_TYPE", "I-STREET_TYPE", "B-STREET_DIR","I-STREET_DIR", "B-CITY", "I-CITY"] 
The results with the same tag need to be concatenated to provide meaningful output; please see section "How to Get Started with the Model" for inference example.

- **Developed by:** [Juntao Zhang]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [BERT-based token classification model]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [bert-base-uncased]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## 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. -->

[This model can be used for token classification tasks, such as named entity recognition (NER) or address token classification.
]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[address matching, address auto-correction etc.]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[
```
import torch
from transformers import pipeline
import os
import json

class GeoLLMBertInference:
    def __init__(self, config_path='config.json'):
        with open(config_path, 'r') as config_file:
            config = json.load(config_file)
        
        self.project_path = config['project_path']
        self.tokenizer_path = os.path.join(self.project_path, config['tokenizer_path'])
        self.model_path = os.path.join(self.project_path, config['model_path'])

        # Check if a GPU is available and set the device accordingly
        self.device = 0 if torch.cuda.is_available() else -1
 
        self.ner_pipeline = pipeline("ner", model=self.model_path, tokenizer=self.tokenizer_path, device=self.device)
        self.result = None
        self.concatenate_result = None

    def get_ner_result(self, address):
        self.result = self.ner_pipeline(address.upper())
        return self.result

    def concatenate_entities(self):
        if self.result is None:
            raise ValueError("NER result is not available. Please run get_ner_result first.")
        
        concatenated_result = {}
        for entity in self.result:
            tag = entity['entity']
            word = entity['word'].replace('##', '').replace(',', '')
            if tag not in concatenated_result:
                concatenated_result[tag] = word.upper()
            else:
                concatenated_result[tag] += '' + word.upper()
        
        self.concatenate_result = concatenated_result
        return self.concatenate_result

    def get_json_result(self):
        if self.concatenate_result is None:
            raise ValueError("Concatenated result is not available. Please run concatenate_entities first.")
        
        return json.dumps(self.concatenate_result, indent=4)

# Example Usage
if __name__ == "__main__":
    geo_llm = GeoLLMBertInference('code/geo_llm/config.json')
    address = "16 ChSeAStREtST.CATHARINE"
    result = geo_llm.get_ner_result(address)
    print(result)

    concatenate_result = geo_llm.concatenate_entities()
    print(concatenate_result)

    # Get the concatenated result in JSON format
    json_result = geo_llm.get_json_result()
    data = json.loads(json_result)


    # Print the JSON string
    print(json_result)
```
]

## Training Details

### Training Data

<!-- This should link to a Dataset 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 Needed]

### 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 [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[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

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[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]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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