Smartshot Zeroshot Finetuned V0.2.0

🚀 Fine-tuned Zero-Shot Classification Model

A fine-tuned zero-shot classification model based on a pre - trained base model, suitable for natural language inference tasks.

🚀 Quick Start

To use this fine - tuned zero - shot classification model, you can follow the code example below:

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="gincioks/smartshot-zeroshot-finetuned-v0.2.0")
text = "Hello world."
labels = ["Hello", "World"]
results = classifier(text, labels)
print(results)

✨ Features

• Base Model: Utilizes the MoritzLaurer/deberta-v3-base-zeroshot-v2.0-c as the base model.
• Training Data: Trained on synthetic data created for natural language inference tasks.
• Fine - tuning Method: Employs the SmartShot approach with NLI framing for fine - tuning.

📦 Installation

The installation process mainly involves setting up the transformers library. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="gincioks/smartshot-zeroshot-finetuned-v0.2.0")
text = "Hello world."
labels = ["Hello", "World"]
results = classifier(text, labels)
print(results)

🔧 Technical Details

Model Details

Training Procedure

This model was fine - tuned with the following parameters:

• Learning rate: 2e - 05
• Epochs: 2
• Batch size: 16
• Warmup ratio: 0.06