README.md

---
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:

```bash
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`