tags:
- text-classification
base_model: cross-encoder/nli-roberta-base
widget:
- text: I love AutoTrain
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: zero-shot-classification
library_name: transformers
LogicSpine/address-base-text-classifier
Model Description
LogicSpine/address-base-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-base-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: 0.28241145610809326
f1_macro: 0.8093855588593053
f1_micro: 0.9515418502202643
f1_weighted: 0.949198754683482
precision_macro: 0.8090277777777778
precision_micro: 0.9515418502202643
precision_weighted: 0.9473201174743024
recall_macro: 0.8100845864661653
recall_micro: 0.9515418502202643
recall_weighted: 0.9515418502202643
accuracy: 0.9515418502202643