update links and fix typos
@@ -142,19 +142,21 @@ _credentialdigger_ in order to avoid errors in linking.
Refer to the Wiki for further information.
### File Path Model
+
The File Path Model classifies a discovery as false positive according to its file
-path when it indicates that the code portion is used for test or exmaple. A pre-trained Path Model [is available here](https://github.com/SAP/credential-digger/releases/download/v1.0.0/path_model-1.0.0.tar.gz).
+path when it indicates that the code portion is used for test or example. A pre-trained Path Model [is available here](https://github.com/SAP/credential-digger/releases/download/PM-v1.0.1/path_model-1.0.1.tar.gz).
After installing _credentialdigger_, our pre-trained Path Model can be installed as follows.
```bash
-export path_model=https://github.com/SAP/credential-digger/releases/download/v1.0.0/path_model-1.0.0.tar.gz
+export path_model=https://github.com/SAP/credential-digger/releases/download/PM-v1.0.1/path_model-1.0.1.tar.gz
python -m credentialdigger download path_model
```
+
### Code Snippet Model
The code Snippet model identifies the password based authentication in a code and differeciate between real and fake passwords. A pre-trained Code Snippet Model [is available here](https://github.com/SAP/credential-digger/releases/download/SM-v1.0.0/snippet_model-1.0.0.tar.gz)
-WARNING: This Model is pre-trained with synthetic data in order to protect privacy. It will help to reduce the False Positives related to password recongnition but with a lower precision compared to a Model pre-trained with real data.
+WARNING: This Model is pre-trained with synthetic data in order to protect privacy. It helps to reduce the False Positives related to password recongnition, but with a lower precision compared to a Model pre-trained with real data.
### Integrate ML Models in the UI
@@ -162,12 +164,12 @@ WARNING: This Model is pre-trained with synthetic data in order to protect priva
enabled with a little effort.
To use ML models in the docker container running the ui, set their address in
-the `.env` file (the Path Model is already set).
+the `.env` file (the pre-trained Path and Snippet Models are already set).
If the address is correct, the model will be installed at container building
time.
However, the current server is not executing any model during the scan of a
repository. To enable a model, it must be listed as an argument in the `scan`
-function (as would be done in the client).in the `ui/server.py` file.
+function (as would be done in the client) in the `ui/server.py` file.
## Usage (client)
