metadata
license: mit
datasets:
- custom
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: text-classification
library_name: sklearn
tags:
- agriculture
- logistic-regression
- tfidf
- binary-classification
- crop-health
model_name: Agriculture Text Classifier
model_creator: PopeJohn
model_type: sklearn
model_description: >-
A logistic regression classifier trained on agricultural text using TF-IDF
features.
🌱 Agriculture Text Classifier
Model owner: PopeJohn
Repository: PopeJohn/agriculture-model
📝 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.
📂 Files in this repository
agriculture_model.pkl— Trained Logistic Regression modelvectorizer.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
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])