Added more info still missing training data

README.md

@@ -9,8 +9,7 @@ base_model:
 - answerdotai/ModernBERT-base
 ---
 
-
-# Model Card for NER_OCOD
+# 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.
 
@@ -19,7 +18,7 @@ This model is designed to perform Named Entity Recognition on The OCOD dataset o
 ### 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 "NER_OCOD" model is designed to extract a list of standardised elements which can be normalised to one property per row.
+As such the "OCOD_NER" model is designed to extract a list of standardised elements which can be normalised to one property per row.
 
 | Example | Address |
 |---------|---------|
@@ -46,7 +45,7 @@ The model has the following classes
 - **License:** GPL 3.0
 - **Finetuned from model :** modernBERT
 
-### Model Sources [optional]
+### Model Sources
 
 - **Repository:** https://huggingface.co/Jonnob/OCOD_NER
 - **Github:** https://github.com/JonnoB/enhance_ocod
@@ -59,16 +58,41 @@ The model is designed to be used as part of the enhance_ocod python library whic
 
 ### Direct Use
 
-Although it can be used to parse text string addresses the model is not designed to be used directly.
+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.
 
-### Downstream Use 
+**Primary Use Case:**
+- Parsing and extracting structured components from address strings
+- Identifying entities such as street numbers, street names, cities, postcodes, etc.
+
+**Example Usage:**
+
+```python
+from transformers import pipeline
+
+# Load the model
+nlp = pipeline(
+    "token-classification", 
+    model="Jonnob/OCOD_NER",
+    aggregation_strategy="simple",
+    device=0  # Use GPU if available
+)
 
-TIdeally the model should be used as part of the enhance OCOD library, where a variety of support functions and scripts are available. for example parsing the entire history of the OCOD dataset can be done by running
+# Parse a single address
+address = "Flat 14a, 14 Barnsbury Road, London N1 1JU"
+results = nlp(address)
+```
 
+### Downstream Use 
 
-`python parse_ocod_history.py`
+**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.
 
-from the 'scripts' folder of the repo.
+**OCOD-Specific Usage:**
+For users working with OCOD datasets, the complete processing pipeline can be executed using:
+```bash
+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
 
@@ -76,19 +100,31 @@ The model is specifically trained on OCOD data and is not designed to be a gener
 
 ## 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.
+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.
 
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use the code below to get started with the model.
 
-{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}
+```python
+from transformers import pipeline
 
-## How to Get Started with the Model
+# 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
+)
 
-Use the code below to get started with the model.
+# Parse a single address
+address = "Flat 14a, 14 Barnsbury Road, London N1 1JU"
+results = nlp(address)
 
-{{ get_started_code | default("[More Information Needed]", true)}}
+# Print extracted entities
+for entity in results:
+    print(f"{entity['entity_group']}: {entity['word']} (confidence: {entity['score']:.2f})")
+```
 
 ## Training Details
 
@@ -161,7 +197,17 @@ model performance is given below
 
 ### Model Architecture and Objective
 
-{{ model_specs | default("[More Information Needed]", true)}}
+**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