OCOD_NER

Model Card for OCOD_NER

This model is designed to perform Named Entity Recognition on The OCOD dataset of offshore owned property in England and Wales.

Model Details

Model Description

The OCOD dataset is a record of all property in England and Wales owned by companies incorporated outside the UK, and is regularly released by the Land Registry and agency of the UK government. The issue with the OCOD dataset is that the property addresses are entered as free text, making getting important details of the addresses challenging. In addition a single entry can contain more than one property with some addresses containing hundreds of sub-properties, which adds to the challenge, see the below table for examples. As such the "OCOD_NER" model is designed to extract a list of standardised elements which can be normalised to one property per row.

The model has the following classes

• Developed by: Jonathan Bourne
• Model type: Named Entity Recognition
• Language(s) (NLP): English, Welsh
• License: GPL 3.0
• Finetuned from model : modernBERT

Model Sources

• Repository: https://huggingface.co/Jonnob/OCOD_NER
• Github: https://github.com/JonnoB/enhance_ocod
• Paper : What’s in the laundromat? Mapping and characterising offshore-owned residential property in London doi: https://doi.org/10.1177/23998083231155483

Uses

The model is designed to be used as part of the enhance_ocod python library which can be found at https://github.com/JonnoB/enhance_ocod

Direct Use

This model is designed for Named Entity Recognition (NER) on address data to extract and classify address components. The model can be used directly through HuggingFace's transformers library for token classification tasks.

Primary Use Case:

• Parsing and extracting structured components from address strings
• Identifying entities such as street numbers, street names, cities, postcodes, etc.

Example Usage:

from transformers import pipeline

# Load the model
nlp = pipeline(
    "token-classification", 
    model="Jonnob/OCOD_NER",
    aggregation_strategy="simple",
    device=0  # Use GPU if available
)

# Parse a single address
address = "Flat 14a, 14 Barnsbury Road, London N1 1JU".lower()
results = nlp(address)

Downstream Use

Primary Integration: OCOD Library This model is primarily designed to be used as part of the enhanced OCOD (Office for National Statistics Comparison of Overseas Property Datasets) library, where specialized functions and scripts are available for processing property address data.

OCOD-Specific Usage: For users working with OCOD datasets, the complete processing pipeline can be executed using:

python parse_ocod_history.py

from the 'scripts' folder of the repository. This handles the entire historical OCOD dataset with optimized batch processing.

Out-of-Scope Use

The model is specifically trained on OCOD data and is not designed to be a general purpose address parser. However, it is likely to work relatively well on UK addresses, although it has not been tested.

Bias, Risks, and Limitations

Whilst the model has been trained on both English and Welsh addresses, there were less Welsh addresses in the training data, in addition modernBERT was not pre-trained on Welsh, as such the model may under-perform on addresses written in Welsh. The model will almost certainly not work in any other language.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

# Load the model
nlp = pipeline(
    "token-classification",
    model="Jonnob/OCOD_NER",  # Replace with your actual model name
    aggregation_strategy="simple",
    device=0 if torch.cuda.is_available() else -1  # GPU if available
)

# Parse a single address
address = "Flat 14a, 14 Barnsbury Road, London N1 1JU".lower()
results = nlp(address)

# Print extracted entities
for entity in results:
    print(f"{entity['entity_group']}: {entity['word']} (confidence: {entity['score']:.2f})")

Training Details

Training Data

This model was trained on the hand labelled dataset not the weakly-lablled dataset. This is because despite being slightly outperformed by the weakl-learning model, the training set model is significantly easier to reproduce and faster to train.

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Training Procedure

The training procedure can be found in the 'mbert_train_configurable.py' script of the enhance OCOD repo.

Training Hyperparameters

Evaluation

The model was evaluated on 2000 hand-labelled addresses randomly sampled from the OCOD February 2022 release.

Testing Data, Factors & Metrics

Testing Data

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Metrics

The model was tested using the micro F1.

Results

model performance is given below

Model Architecture and Objective

Architecture:

• Base model: ModernBERT-base (answerdotai/ModernBERT-base)
• 22 transformer layers, 149 million parameters
• Bidirectional encoder-only architecture with modern improvements:
• Native context length: up to 8,192 tokens
• Additional token classification head for NER fine-tuning

Objective:

• Fine-tuned for Named Entity Recognition (NER)
• Training objective: Token-level classification with cross-entropy loss
• Designed to identify and classify named entities for address normalisation

Compute Infrastructure

The model was trained using the lightning.ai platform

Hardware

The model can be run on a L4 or T4 GPU and requires 16Gb VRAM.

Citation

Coming soon.

Model Card Contact

For queries please raise issues on the github repo