Instructions to use MoyAI/password-security with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MoyAI/password-security with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MoyAI/password-security") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- password
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This is a keras model that gives a binary response showing how secure is a password. The `start.py` file has a `clf` function that inputs a string of a password and responds with a 0-1 float value. 1 means secure and 0 insecure.
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I used this password list as a dataset + random password generation using the `random` library (I know, it's not very secure but this is for a dataset).
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- password
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# Password security classifier
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This is a keras model that gives a binary response showing how secure is a password. The `start.py` file has a `clf` function that inputs a string of a password and responds with a 0-1 float value. 1 means secure and 0 insecure.
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I used this password list as a dataset + random password generation using the `random` library (I know, it's not very secure but this is for a dataset).
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## Model & Training
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The model was trained on 4,2MiB of .csv data for 2 epochs on Adam with learning rate 0.00001, batch size 4 and mse loss.
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The model embeds every input character with the ord() builtin python function. The model has `128 128` dense layer parameters.
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## Evaluation
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The evaluation of the model on a test dataset shows:
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**loss** -
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**accuracy** -
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