address-large-text-classifier

LogicSpine/address-large-text-classifier

Model Description

LogicSpine/address-large-text-classifier is a fine-tuned version of the cross-encoder/nli-roberta-base model, specifically designed for address classification tasks using zero-shot learning. It allows you to classify text related to addresses and locations without the need for direct training on every possible label.

Model Usage

Installation

To use this model, you need to install the transformers library:

pip install transformers torch

Loading the Model

You can easily load and use this model for zero-shot classification using Hugging Face's pipeline API.

from transformers import pipeline

# Load the zero-shot classification pipeline with the custom model
classifier = pipeline("zero-shot-classification", 
                      model="LogicSpine/address-large-text-classifier")

# Define your input text and candidate labels
text = "Delhi, India"
candidate_labels = ["Country", "Department", "Laboratory", "College", "District", "Academy"]

# Perform classification
result = classifier(text, candidate_labels)

# Print the classification result
print(result)

Example Output

{'labels': ['Country',
            'District',
            'Academy',
            'College',
            'Department',
            'Laboratory'],
 'scores': [0.19237062335014343,
            0.1802321970462799,
            0.16583585739135742,
            0.16354037821292877,
            0.1526614874601364,
            0.14535939693450928],
 'sequence': 'Delhi, India'}

Validation Metrics

loss: 1.3794080018997192 f1_macro: 0.21842933805832918 f1_micro: 0.4551574223406493 f1_weighted: 0.306703002026862 precision_macro: 0.19546905037281545 precision_micro: 0.4551574223406493 precision_weighted: 0.2510467302490216 recall_macro: 0.2811753463927377 recall_micro: 0.4551574223406493 recall_weighted: 0.4551574223406493 accuracy: 0.4551574223406493

Colab Notebook

Checkout this example of google Colab