agriculture-model

🌱 Agriculture Text Classifier

Model owner: PopeJohn
Repository: PopeJohn/agriculture-model

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📝 Overview

This model is a Logistic Regression classifier trained on agricultural text data, using TF–IDF vectorization for feature extraction.
It predicts predefined agriculture-related categories from short text inputs, making it useful for tasks like farmer query routing, agronomic content tagging, and agricultural market analysis.

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📂 Files in this repository

• agriculture_model.pkl — Trained Logistic Regression model
• vectorizer.pkl — Fitted TF–IDF vectorizer for text preprocessing

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🔍 Intended Use

This model is designed for:

• Classifying farmer questions into crop/disease categories
• Indexing or tagging agricultural content
• Supporting NLP pipelines in agriculture-focused applications

Not intended for:

• Real-time critical decision-making without human verification
• Non-agriculture domains without fine-tuning

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⚙️ How to Use

from huggingface_hub import hf_hub_download
import joblib

# Download files from Hugging Face Hub
model_path = hf_hub_download("PopeJohn/agriculture-model", "agriculture_model.pkl")
vectorizer_path = hf_hub_download("PopeJohn/agriculture-model", "vectorizer.pkl")

# Load
model = joblib.load(model_path)
vectorizer = joblib.load(vectorizer_path)

# Predict
sample_text = ["Healthy maize crop after seasonal rains"]
prediction = model.predict(vectorizer.transform(sample_text))
print(prediction[0])