learning_about_configs.md

Learning about Configs

AdaSeq uses configuration file to control model assembling, training and evaluation. The configuration file supports yaml json jsonline format.

1. Configurate File Organization

Let's take resume.yaml as an example. A configuration file usually consists of the following fields:

experiment: ...
task: ...
dataset: ...
preprocessor: ...
data_collator: ...
model: ...
train: ...
evaluation: ...

2. Introduction to Global Parameters

Notice: Default = / means this parameter is compulsory.

2.1 experiment

Parameter	Description	Type	Default
exp_dir	experiment directory	str	experiments
exp_name	experiment name. all outputs will be saved to ./${exp_dir}/${exp_name}/${datetime}/	str	unknown
seed	random seed	int	42

2.2 task

task supports the following values (see metainfo):

• word-segmentation
• part-of-speech
• named-entity-recognition
• relation-extraction
• entity-typing

2.3 dataset

Please refer to Customizing Dataset as the combination of dataset parameters is complex.

Parameter	Description	Type	Default
task	task of the dataset	str	None
name	modelscope dataset name, for example damo/resume_ner	str	None
path	huggingface dataset name, for example conll2003	str	None
data_file	data files, it can be an url, local directory or archive, it can be a dict containing train valid test as well	str/dict	None
data_type	used to specify data loading method	str	None
transform	dataset post processing, usually containing name key scheme	dict	None
labels	label set, it can be a list labels: ['O', 'B-ORG', ...], file or urllabels: PATH_OR_URL, or a function counting labels from dataset	str/list/dict	None
access_token	used to access private repos from modelscope or huggingface	str	None

2.4 preprocessor

Parameter	Description	Type	Default
type	preprocessor type	str	/
model_dir	tokenizer name or directory	str	/
is_word2vec	whether to use Lookup Table	bool	False
tokenizer_kwargs	other parameters for tokenizer	dict	None
max_length	maximum sentence length (subtoken-level)	int	512

2.5 data_collator

data_collator supports the following values (see metainfo):

• DataCollatorWithPadding
• SequenceLabelingDataCollatorWithPadding
• SpanExtractionDataCollatorWithPadding
• MultiLabelSpanTypingDataCollatorWithPadding
• MultiLabelConcatTypingDataCollatorWithPadding

2.6 model

Parameter	Child-Parameter	Description	Type	Default
type		model type	str	/
embedder		used to embed input ids to vectors, usually a pretrained model	dict	None
└	type	embedder type, optional when using modelscope or huggingface model	str	None
└	model_name_or_path	pretrained model name or path, supporting both modelscope or huggingface models	str	/
encoder		encode the sentence vector, such as LSTM	dict	None
└	type	encoder type	str	/
decoder		not available, under construction	dict	None

2.7 train

Parameter	Child-Parameter	Description	Type	Default
trainer		trainer type	str	None
max_epochs		maximum number of epochs in training	int	/
dataloader		used to load data	dict	/
└	batch_size_per_gpu	batch size per gpu	int	/
└	workers_per_gpu	data loading workers per gpu	int	0
optimizer		optimizer	dict	None
└	type	optimizer type	str	/
└	lr	learning rate for all parameters except specific param_groups	float	/
└	options	options used in optimizer, for example grad_clip: max_norm: 2.0	dict	None
└	param_groups	param_groups can have different learning rates	list	None
└	└ regex	regex expression to specify parameter group	str	/
└	└ lr	learning rate for specific parameter group	float	/
lr_scheduler		used to adjust learning rate uding training	dict	None
└	type	supporting all lr_scheduler from pytorch (check if your pytorch version includes them)	str	/
└	options	options used in the lr_scheduler	dict	None
hooks		also callbacks see ModelScope documentation	list	None

2.8 evaluation

Parameter	Child-Parameter	Description	Type	Default
dataloader		used to load data	dict	/
└	batch_size_per_gpu	batch size per gpu	int	/
└	workers_per_gpu	data loading workers per gpu	int	0
metrics		evaluation metrics	list	None
└	type	metric type	str	/