Hythcliff/canadian-address-checker-on

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Juntao Zhang]
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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

[

import torch
from transformers import pipeline
import json

# Use a pipeline as a high-level helper
from transformers import pipeline

class GeoLLMBertSpellingInference:
    def __init__(self):
        # Check if a GPU is available and set the device accordingly
        self.device = 0 if torch.cuda.is_available() else -1
 
        self.fillmask_pipeline = pipeline("fill-mask", model="Hythcliff/canadian-address-checker-on", tokenizer="Hythcliff/canadian-address-checker-on", device=self.device)
        self.result = None
    
    def _create_prompt(self, incorrect_word):
        return f"The misspelled word is {incorrect_word}. The correct word is {self.fillmask_pipeline.tokenizer.mask_token}."

    def get_fillmask_result(self, incorrect_word):
        prompt_text = self._create_prompt(incorrect_word)
        self.result = self.fillmask_pipeline(prompt_text, top_k=1)
        return self.result

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


# Example usage
geo_llm = GeoLLMBertSpellingInference()
address = "ChSeA"
result = geo_llm.get_fillmask_result(address)


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

# Print the JSON string
print(json_result)

]

Training Details

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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