|
|
| --- |
| license: apache-2.0 |
| library_name: sklearn |
| pipeline_tag: text-classification |
| tags: |
| - keyword-intent |
| - search-intent |
| - seo |
| --- |
| |
| # Keyword Intent Classifier |
|
|
| A search-intent classification model trained on **1,000,000 keyword records (10 lakh rows)** for SEO keyword analysis. |
|
|
| The model classifies keywords into four categories: |
|
|
| | Label | Meaning | Example | |
| |---|---|---| |
| | informational | Seeking information or answers | what is artificial intelligence | |
| | navigational | Looking for a specific website or page | facebook login | |
| | commercial | Researching or comparing before buying | best crm software | |
| | transactional | Intending to purchase or take action | buy laptop online | |
|
|
| ## Installation |
|
|
| Use Python 3.12. Download `requirements.txt` from this repository and install the dependencies: |
|
|
| ```bash |
| python -m pip install -r requirements.txt |
| ``` |
|
|
| ## Usage |
|
|
| Download the model and its required supporting module, then predict keyword intent: |
|
|
| ```python |
| import sys |
| from pathlib import Path |
| |
| import joblib |
| from huggingface_hub import snapshot_download |
| |
| folder = snapshot_download( |
| repo_id="Egrove/keyword-intent-classifier", |
| allow_patterns=[ |
| "keyword_intent_model.pkl", |
| "intent_preprocess.py", |
| ], |
| ) |
| |
| sys.path.insert(0, folder) |
| |
| model = joblib.load( |
| Path(folder) / "keyword_intent_model.pkl" |
| ) |
| |
| keywords = [ |
| "facebook login", |
| "buy laptop online", |
| "what is artificial intelligence", |
| "best crm software", |
| ] |
| |
| predictions = model.predict(keywords) |
| |
| for keyword, intent in zip(keywords, predictions): |
| print(f"{keyword} -> {intent}") |
| ``` |
|
|
| ### Example output |
|
|
| ```text |
| facebook login -> navigational |
| buy laptop online -> transactional |
| what is artificial intelligence -> informational |
| best crm software -> commercial |
| ``` |
|
|
| Keep `intent_preprocess.py` available when loading the model. |
|
|
| ## Limitations |
|
|
| Keywords can have multiple or ambiguous meanings. Evaluate predictions on your own keywords before production use. No benchmark accuracy or F1 score is claimed. |
|
|
| ## Security |
|
|
| Only load this model and its supporting Python module if you trust their source. Loading pickle/joblib files and importing Python modules can execute code. |
|
|
| ## License |
|
|
| This model is released under the **Apache License, Version 2.0**. See [LICENSE.txt](LICENSE.txt) for the full terms. |
|
|
|
|
