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isroych/prompt-analyzer
prompt-analyzer is a machine learning model from isroych. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as gpl-3.0.
This model is meant to serve as a basic analyzer that can classify the input to an AI assistant to determine what the user wants the assistant to do. The result of this classification can be used, along with further a…
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Updated Oct 24, 2023
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From the Hugging Face model README
This model is meant to serve as a basic analyzer that can classify the input to an AI assistant to determine what the user wants the assistant to do. The result of this classification can be used, along with further analyzing of the text, to get the user what they want.
Use it with the following code:
def classify(text): classifier = pickle.load(open('classifier.bin', 'rb')) vectorizer = pickle.load(open('vectorizer.pkl', 'rb')) # New text you want to classify new_text = [text]
# Preprocess and convert new text into numerical features using the same vectorizer
new_text_features = vectorizer.transform(new_text)
# Use the trained classifier to predict the label
predicted_label = classifier.predict(new_text_features)
print(f"Predicted Label: {predicted_label[0]}")
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It's not super accurate(though it is fairly so), and can only be used to classify the intent behind a text. It cannot be used to generate a reply to a text or anything of that sort. It is not a full AI assistant, and is only part of an assistant.
[More Information Needed]
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Use the code below to get started with the model. def classify(text): classifier = pickle.load(open('classifier.bin', 'rb')) vectorizer = pickle.load(open('vectorizer.pkl', 'rb')) # New text you want to classify new_text = [text]
# Preprocess and convert new text into numerical features using the same vectorizer
new_text_features = vectorizer.transform(new_text)
# Use the trained classifier to predict the label
predicted_label = classifier.predict(new_text_features)
print(f"Predicted Label: {predicted_label[0]}")
text = "Insert text here" classify(text)
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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BibTeX:
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APA:
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