prompt-analyzer

Model Card for Model ID

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.

Model Details

Model Description

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]}")

• Developed by: [More Information Needed]
• Model type: Text classification
• Language(s) (NLP): english
• License: GPL v 3.0

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Uses

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Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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.

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

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Environmental Impact

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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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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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