At the core of the problem MetaDis aims to solve is a common issue researchers encounter when studying historical texts - the identification of individuals sharing the same name. Are these instances referring to the same person or two different people? This is the question MetaDis seeks to answer.

MetaDis is based on the AutoModelForNextSentencePrediction architecture, a machine learning model that processes two sequences of data as its input. It then outputs a 0 or 1 - a binary representation indicating whether or not the two sequences refer to the same person. Here, 0 represents 'not the same person', and 1 indicates 'the same person'.

Input Data Formatting

In order to ensure the highest accuracy and performance of the MetaDis model, we've specifically designed an input format based on the data the model was originally trained on. This is crucial as it allows the model to accurately interpret and process your data.

To assist you in this process, we've provided a template Excel (.xlsx) file. We recommend downloading this template and inputting your data directly into it, ensuring your data matches the same format as the model's training data.

To download our Excel data template, please click here.

Code Demonstration: Loading and Using MetaDis Model

The following section demonstrates how to directly load the MetaDis model and use it for predicting whether two sets of biographical information refer to the same person or not.

Please ensure that you have the transformers library installed in your Python environment. If not, you can install it using pip:

pip install transformers

Now, let's load our model and make some predictions:

python

# Import necessary libraries from HuggingFace Transformers
from transformers import AutoTokenizer, AutoModelForNextSentencePrediction
import torch

# Load our tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("cbdb/MetaDis")
model = AutoModelForNextSentencePrediction.from_pretrained("cbdb/MetaDis")

# Define our sentences to compare
sentence1 = ['first biographical information of person name A', 'first biographical information of person name B']
sentence2 = ['second biographical information of person name A', 'first biographical information of person name B']

# Loop through each sentence pair
for s1, s2 in zip(sentence1, sentence2):
    # Prepare the inputs
    encoding = tokenizer(s1, s2, truncation=True, padding=True, return_tensors='pt')

    # Move the inputs to the device where the model is
    for key in encoding:
        encoding[key] = encoding[key].to(model.device)

    # Make the prediction
    outputs = model(**encoding)

    # Extract the prediction
    logits = outputs.logits
    preds = torch.argmax(logits, dim=-1)

    # Display the results
    if preds.item() == 1:
        print('Same person')
        print(s1, s2)
    else:
        print('Different person')
        print(s1, s2)

This code demonstration shows how you can load our MetaDis model, prepare inputs in the necessary format, and extract predictions to determine if the biographical details refer to the same person or different individuals. Remember to replace the example sentences with your own data.

Remember to include a link or instructions on how users can install the transformers library if they don't already have it installed.

Authors

Queenie Luo (queenieluo[at]g.harvard.edu)
Hongsu Wang
Peter Bol
CBDB Group

License

Except where otherwise noted, content on this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.