...
Argument | Type | Default | Description |
---|---|---|---|
|
|
| Path to the ontology JSON file. |
|
|
| Path to the training dataset. |
|
|
| Path to the test dataset. |
|
|
| Path to save/load the trained model weights. |
|
|
| Train the model when this flag is set. |
|
|
| Evaluate the model on the test dataset when this flag is set. |
|
|
| Number of epochs for training. |
|
|
| Batch size for training. |
|
|
| Learning rate for the optimizer. |
|
|
| Maximum sequence length for tokenization. |
|
|
| Random seed for reproducibility. |
|
|
| Text input for running inference. |
|
|
| Show the intent and slot distribution in the dataset. |
...
Use
21. Viewing Dataset Distribution
...
Code Block |
---|
python run_train_test.py --show_dist |
...
2. Training the Model
To train the model using the training dataset:
Code Block |
---|
python mainrun_train_test.py --train_model |
...
3. Evaluating the Model
To evaluate a pre-trained model on the test dataset:
Code Block |
---|
python mainrun_train_test.py --evaluate |
...
4. Running Inference
To predict the intent and slots for a given input text:
Code Block |
---|
python mainrun_train_test.py --inference_text "Find me a Japanese recipe for lunch." |
5. Run with ASR.
When complete with this section read https://socialrobotics.atlassian.net/wiki/spaces/PCA2/pages/2709488567/Run+your+Conversational+Agent#Run-your-Intent-and-Slot-Classifier-with-WHISPER to connect your intent and slot classifier with WHISPER