Toponym 19thC En

Table of Contents

Model Overview

The toponym-19thC-en model is a special type of AI designed to recognize places, buildings, and streets in old English texts from the 19th century. It’s like a super smart detective that can find and identify locations in historical documents.

Tasks and rank

Text Classification 1836th

Natural Language Understanding 5397th

Named Entity Recognition (NER) 1304th

Named Entity Disambiguation 153rd

Information Extraction 474th

Learn More About the Ranking System

Capabilities

What can it do?

• Recognize places (LOC), buildings (BUILDING), and streets (STREET) in 19th-century English texts
• Work with digitized newspaper texts from that time period
• Use a special format called BIO to understand how words relate to each other in a sentence

Strengths

The toponym-19thC-en model has several strengths:

• Fine-tuned for historical texts: Trained on a large dataset of 19th-century English texts, this model is well-suited for analyzing historical documents.
• High accuracy: The model has been trained to recognize toponyms with high accuracy, making it a valuable tool for researchers and historians.
• Flexibility: The model can be used with a named entity recognition pipeline, allowing for easy integration into a variety of applications.

Example Use Cases

Here are a few examples of how you can use the toponym-19thC-en model:

• Historical research: Use the model to analyze historical documents and identify mentions of locations, helping you to better understand the context and geography of the time period.
• Text analysis: Use the model to analyze large datasets of text and identify patterns and trends in the way locations are mentioned.
• Geographic information systems: Use the model to extract geographic information from historical texts and create detailed maps of the past.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

All MiniLM L6 V2 2389 Sentence Embeddings

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Galactica 120b 149 Scientific language model

Performance

The toponym-19thC-en model is a powerful tool for recognizing toponyms in 19th-century English texts. But how does it perform?

Speed

How fast can the toponym-19thC-en model process text? Well, it’s built on top of a bert-base-uncased model fine-tuned on a large historical dataset of books in English. This means it can handle a significant amount of text data quickly and efficiently.

Accuracy

But how accurate is the toponym-19thC-en model? It has been trained on a large dataset of annotated examples, and has achieved impressive results in recognizing entities such as LOC, BUILDING, and STREET.

Efficiency

The toponym-19thC-en model is designed to be efficient in its use of computational resources. It uses a named entity recognition pipeline, which allows it to process text quickly and accurately.

Examples

Identify the toponyms in the following text: 'The Lord Mayor of London attended the ceremony at the Royal Exchange in Threadneedle Street.' {'entity_group': 'LOC', 'score': 0.999, 'word': 'london', 'start': 23, 'end': 29}, {'entity_group': 'BUILDING', 'score': 0.998, 'word': 'royal exchange', 'start': 45, 'end': 58}, {'entity_group': 'STREET', 'score': 0.995, 'word': 'threadneedle street', 'start': 63, 'end': 79}

Extract the toponyms from the sentence: 'The company is located at 123 Oxford Street in Manchester.' {'entity_group': 'STREET', 'score': 0.999, 'word': 'oxford street', 'start': 25, 'end': 39}, {'entity_group': 'LOC', 'score': 0.997, 'word': 'manchester', 'start': 44, 'end': 54}

Identify the toponyms in the following text: 'The train departed from King's Cross station in London and arrived at Paddington station.' {'entity_group': 'BUILDING', 'score': 0.998, 'word':

Limitations

While the toponym-19thC-en model is a powerful tool, it’s not perfect. Here are some of its limitations:

Historical Context

The model is based on a historical dataset of digitised books in English, published between 1760 and 1900. This means that its predictions should be understood in their historical context.

Dataset Limitations

The dataset used to fine-tune the model is not representative of all 19th-century English texts. It’s biased towards texts from four specific locations in England and may not perform well on texts from other regions.

Hyphenated Entities

The model can struggle with hyphenated entities, such as “Ashton-under-Lyne”. This is because the model may assign incorrect B- and I- prefix tags to these entities.

Format

The toponym-19thC-en model is a bert-base-uncased model fine-tuned on a large historical dataset of books in English. It uses a transformer architecture, which is a type of neural network designed for natural language processing tasks.

Architecture

The model is based on a transformer architecture, which is a type of neural network designed for natural language processing tasks. It uses self-attention mechanisms to weigh the importance of different words in the input text.

Data Formats

The model accepts input in the form of tokenized text sequences. This means that the input text needs to be broken down into individual words or tokens before it can be processed by the model.

Special Requirements

The model is designed to work with 19th-century English texts, particularly digitised newspaper texts. It has been trained to recognize the following types of entities:

• LOC (locations)
• BUILDING (buildings)
• STREET (streets, roads, and other odonyms)

Input and Output

To use the model, you can create a named entity recognition pipeline using the transformers library. Here’s an example:

from transformers import pipeline

model = "Livingwithmachines/toponym-19thC-en"
ner_pipe = pipeline("ner", model=model)

results = ner_pipe("MANUFACTURED ONLY AT 7S, NEW OXFORD-STREET, LONDON.")

This will output a list of entities recognized in the input text, along with their scores and indices.