Point it at a Python file with a slow function. It reads the function, generates real benchmark inputs, asks an LLM for a vectorized version, validates correctness against your original output, and ...
Based on a YouTube tutorial I implemented this project to get hands-on experience with ML text classification and deployment using Streamlit.: 💡 What I learned from this project: • Feature ...
Instead of doing everything manually, we can use Python’s scikit-learn library, specifically the CountVectorizer class to do all this in just one line of code! 🤗 1️⃣ Installing pip install scipy pip ...
X_train_dict = pandas.DataFrame(X_train[:, 1:]).T.to_dict().values() X_test_dict = pandas.DataFrame(X_test[:, 1:]).T.to_dict().values() # We create a pipeline.
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