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--- |
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license: mit |
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datasets: |
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- custom |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: text-classification |
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library_name: sklearn |
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tags: |
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- agriculture |
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- logistic-regression |
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- tfidf |
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- binary-classification |
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- crop-health |
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model_name: Agriculture Text Classifier |
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model_creator: PopeJohn |
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model_type: sklearn |
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model_description: A logistic regression classifier trained on agricultural text using TF-IDF features. |
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--- |
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🌱 Agriculture Text Classifier |
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**Model owner:** [PopeJohn](https://huggingface.co/PopeJohn) |
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**Repository:** [PopeJohn/agriculture-model](https://huggingface.co/PopeJohn/agriculture-model) |
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--- |
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## 📝 Overview |
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This model is a **Logistic Regression** classifier trained on agricultural text data, using **TF–IDF vectorization** for feature extraction. |
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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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--- |
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## 📂 Files in this repository |
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- `agriculture_model.pkl` — Trained Logistic Regression model |
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- `vectorizer.pkl` — Fitted TF–IDF vectorizer for text preprocessing |
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--- |
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## 🔍 Intended Use |
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This model is designed for: |
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- Classifying farmer questions into crop/disease categories |
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- Indexing or tagging agricultural content |
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- Supporting NLP pipelines in agriculture-focused applications |
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Not intended for: |
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- Real-time critical decision-making without human verification |
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- Non-agriculture domains without fine-tuning |
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--- |
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## ⚙️ How to Use |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib |
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# Download files from Hugging Face Hub |
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model_path = hf_hub_download("PopeJohn/agriculture-model", "agriculture_model.pkl") |
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vectorizer_path = hf_hub_download("PopeJohn/agriculture-model", "vectorizer.pkl") |
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# Load |
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model = joblib.load(model_path) |
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vectorizer = joblib.load(vectorizer_path) |
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# Predict |
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sample_text = ["Healthy maize crop after seasonal rains"] |
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prediction = model.predict(vectorizer.transform(sample_text)) |
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print(prediction[0]) |
