Model NLP

User Guide (Latest Version)

The config file for NLP models contain three main sections:

  • trainer: contains the configs for PTL training. For more information, refer to NeMo Models and PTL Trainer class API <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-class-api>.

  • exp_manager: the configs of the experiment manager. For more information, refer to NeMo Models.

  • model: contains the configs of the datasets, model architecture, tokenizer, optimizer, scheduler, etc.

The following sub-sections of the model section are shared among most of the NLP models.

  • tokenizer: specifies the tokenizer

  • language_model: specifies the underlying model to be used as the encoder

  • optim: the configs of the optimizer and scheduler NeMo Models

The tokenizer and language_model sections have the following parameters:

Parameter Data Type Description
model.tokenizer.tokenizer_name string Tokenizer name will be filled automatically based on model.language_model.pretrained_model_name.
model.tokenizer.vocab_file string Path to tokenizer vocabulary.
model.tokenizer.tokenizer_model string Path to tokenizer model (only for sentencepiece tokenizer).
model.language_model.pretrained_model_name string Pre-trained language model name, for example: bert-base-cased or bert-base-uncased.
model.language_model.lm_checkpoint string Path to the pre-trained language model checkpoint.
model.language_model.config_file string Path to the pre-trained language model config file.
model.language_model.config dictionary Config of the pre-trained language model.

The parameter model.language_model.pretrained_model_name can be one of the following:

  • megatron-bert-345m-uncased, megatron-bert-345m-cased, biomegatron-bert-345m-uncased, biomegatron-bert-345m-cased, bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased

  • distilbert-base-uncased, distilbert-base-cased

  • roberta-base, roberta-large, distilroberta-base

  • albert-base-v1, albert-large-v1, albert-xlarge-v1, albert-xxlarge-v1, albert-base-v2, albert-large-v2, albert-xlarge-v2, albert-xxlarge-v2

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