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.

📂 Files in this repository

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

🔍 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

⚙️ How to Use

python

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])