
# Password security classifier
This is a keras model that gives a binary response showing how secure is a password.
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).
This model has a [huggignface space](https://huggingface.co/spaces/MoyAI/password-security). You can visit the link to try using the model online.
## Model & Training
The model was trained on 4,2MiB (`200 000` lines) of .csv data for 2 epochs on Adam with learning rate 0.00001, batch size 4 and mse loss.
The model embeds every input character with the ord() builtin python function. The model has `128 969` dense layer parameters.
## Evaluation
On training the model had:
**loss** - 0.0025
**accuracy** - 0.9972
The evaluation of the model on a test dataset shows:
**loss** - 0.0023
**accuracy** - 0.9972
## Model usage
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.
To train the model, create a `dataset.csv` file. Here's an example:
```csv
0,qwerty
0,123456
1,ISOdvsjs8r8
1,F(SEsDLxc__
```
After the `dataset.csv` file is created, now you can adjust the settings in the `net.py` file and run it.
{"library_name": "keras", "tags": ["security", "password"], "pipeline_tag": "text-classification"}
keras security password text-classification region:us
TAGS
#keras #security #password #text-classification #region-us 


# Password security classifier
This is a keras model that gives a binary response showing how secure is a password.
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).
This model has a huggignface space. You can visit the link to try using the model online.
## Model & Training
The model was trained on 4,2MiB ('200 000' lines) of .csv data for 2 epochs on Adam with learning rate 0.00001, batch size 4 and mse loss.
The model embeds every input character with the ord() builtin python function. The model has '128 969' dense layer parameters.
## Evaluation
On training the model had:
loss - 0.0025
accuracy - 0.9972
The evaluation of the model on a test dataset shows:
loss - 0.0023
accuracy - 0.9972
## Model usage
The 'URL' 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.
To train the model, create a 'URL' file. Here's an example:

After the 'URL' file is created, now you can adjust the settings in the 'URL' file and run it.
# Password security classifier
This is a keras model that gives a binary response showing how secure is a password.
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).
This model has a huggignface space. You can visit the link to try using the model online. ## Model & Training
The model was trained on 4,2MiB ('200 000' lines) of .csv data for 2 epochs on Adam with learning rate 0.00001, batch size 4 and mse loss.
The model embeds every input character with the ord() builtin python function. The model has '128 969' dense layer parameters. ## Evaluation
On training the model had:
loss - 0.0025
accuracy - 0.9972
The evaluation of the model on a test dataset shows:
loss - 0.0023
accuracy - 0.9972 ## Model usage
The 'URL' 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.
To train the model, create a 'URL' file. Here's an example:

After the 'URL' file is created, now you can adjust the settings in the 'URL' file and run it.
TAGS
#keras #security #password #text-classification #region-us 
 # Password security classifier
This is a keras model that gives a binary response showing how secure is a password.
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).
This model has a huggignface space. You can visit the link to try using the model online. ## Model & Training
The model was trained on 4,2MiB ('200 000' lines) of .csv data for 2 epochs on Adam with learning rate 0.00001, batch size 4 and mse loss.
The model embeds every input character with the ord() builtin python function. The model has '128 969' dense layer parameters. ## Evaluation
On training the model had:
loss - 0.0025
accuracy - 0.9972
The evaluation of the model on a test dataset shows:
loss - 0.0023
accuracy - 0.9972 ## Model usage
The 'URL' 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.
To train the model, create a 'URL' file. Here's an example:

After the 'URL' file is created, now you can adjust the settings in the 'URL' file and run it.