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To complete these goals you must go through all subpages in this section starting with Ontology.

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Repository Overview

Note

Find the Intent and Slot Classifier files under /pca-agent-2025/social-interaction-cloud/sic_framework/services/nlu/utils

Code Block
breakoutModewide
The repository is structured as follows:
utils/
├── checkpoints/          # Directory for saving trained model checkpoints
├── data/                 # Directory containing data files (ontology, train/test datasets)
│   ├── ontology.json     # Ontology file containing intents, slots, and synonyms
│   ├── train.json        # Training dataset
│   ├── test.json         # Test dataset
│   └── synonyms.json     # Synonyms for slot normalization
├── data_processing.py    # Utilities for additional data preprocessing (if needed)
├── dataset.py            # Dataset preparation and preprocessing module
├── evaluation.py         # Model evaluation and metrics generation
├── run_train_test.py     # Main script to run training, evaluation, and inference
├── model.py              # Defines the BERT-based model architecture
├── predict.py            # Inference module for predicting intents and slots
├── requirements.txt      # Python dependencies for the project
├── train.py              # Training module for the intent-slot classifier
└── utils.py              # Helper functions for argument parsing, slot normalization, and synonym resolution

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Argument

Type

Default

Description

--ontology_path

str

./data/ontology.json

Path to the ontology JSON file.

--train_data

str

./data/train.json

Path to the training dataset.

--test_data

str

./data/test.json

Path to the test dataset.

--model_save_path

str

checkpoints/model_checkpoint.pt

Path to save/load the trained model weights.

--train_model

bool

False

Train the model when this flag is set.

--evaluate

bool

False

Evaluate the model on the test dataset when this flag is set.

--num_epochs

int

2

Number of epochs for training.

--batch_size

int

16

Batch size for training.

--learning_rate

float

5e-5

Learning rate for the optimizer.

--max_length

int

16

Maximum sequence length for tokenization.

--seed

int

42

Random seed for reproducibility.

--inference_text

str

None

Text input for running inference.

--show_dist

bool

False

Show the intent and slot distribution in the dataset.

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Use

Info

Due to how the imports are set up and because the intent and slot classifier is part of the social-interaction-cloud python package when you make changes you need to make sure to reinstall the social interaction cloud via pip install .

1. Viewing Dataset Distribution

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Code Block
python run_train_test.py --inference_text "Find me a Japanese recipe for lunch."

5. Run with ASR

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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

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