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A framework to experiment with domain ordering for continually training intent classification models

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Effect of Domain Ordering while Continually Learning Intent Recognition

Instructions

The main module is run.py. An example execution looks something like this.

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Command Line Arguments

  • --mode: Mode to run in (type: str).

  • --cl_super_run_label: Superlabel of the CL run to use (type: str, optional).

  • --cl_lr_schedule: Learning rate schedule to use (type: str, default: "constant").

  • --lr: Learning rate to use for training (type: float, default: 1e-4).

  • --num_train_epochs: Number of epochs to train for (type: int, default: 9).

  • --cl_run_dir: Directory to store CL runs in (type: str, default: "../cl_runs").

  • --cl_checkpoint_dir: Directory to store CL checkpoints in (type: str, default: "../cl_checkpoints").

  • --results_dir: Directory to store results in (type: str, default: "../cl_results").

  • --cl_experience_replay_size: Number of samples to use for experience replay (type: int, default: 10).

  • --deepspeed_config: Path to deepspeed config file (type: str, default: "../ds_config.json").

  • --local_rank: Local rank for distributed training (type: int, default: -1).

  • --num_domains_per_run: Number of domains to use in a CL run (type: int, default: 5).

  • --num_runs: Number of CL runs to perform (type: int, default: 5).

  • --ordering_strategy: Domain ordering to use (type: str, default: "random").

  • --input_max_length: Maximum length of input sequence (type: int, default: 512).

  • --ctx_window_size: Number of previous turns in the context (type: int, default: 3).

  • --fp16: Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit (action: store_true).

  • --limit_n_samples: Limit the number of total samples per domain (type: int, default: 200).

  • --val_size_per_domain: Percentage of samples to use for validation (type: float, default: 0.10).

  • --test_size_per_domain: Percentage of samples to use for testing (type: float, default: 0.25).

  • --train_batch_size: Batch size for training (type: int, default: 64).

  • --eval_batch_size: Batch size for evaluation (type: int, default: 64).

  • --shuffle_within_domain: Whether to shuffle the data within a domain (type: bool, default: False).

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A framework to experiment with domain ordering for continually training intent classification models

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