Double click the tNLPModel component to open its
Basic settings view and define its properties.
- Click the [+] button under the Feature template table to add rows to the table.
- Click in the Features column to select the features to be generated.
For each feature, specify the relative position.
For example -2,-1,0,1,2 means that you use the current token, the preceding two and the following two context tokens as features.
- From the NLP Library list, select the same library you used for preprocessing the training text data.
To evaluate the model, select the Run cross validation
evaluation check box and enter 2 in the
This means the training data is partitioned into two pieces: the training data set and the test data set. The validation process is repeated twice.
Press F6 to save and execute the
The results from the K-fold cross-validation process are displayed on the Run view:
Precisionis the ratio of correctly predicted named entities to the total number of predicted named entities.
Recallis the ratio of correctly predicted named entities to the total number of named entities.
F1 scoreis the harmonic mean between
- Clear the Run cross validation evaluation check box.
- Select the Save the model on file system check box to save the model locally in the folder specified in the Folder field.
- Press F6 to save and execute the Job.
The model files are stored in the specified folder. You can now use the generated model with the tNLPPredict component to predict named entities and label text data automatically.