def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `source`, use :func:`gluonnlp.embedding.list_sources`. Parameters ---------- embedding_name : str The token embedding name (case-insensitive). Returns ------- An instance of :class:`gluonnlp.embedding.TokenEmbedding`: A token embedding instance that loads embedding vectors from an externally hosted pre-trained token embedding file. """ create_text_embedding = registry.get_create_func(TokenEmbedding, 'token embedding') return create_text_embedding(embedding_name, **kwargs)
def create(kind, name, **kwargs): """Creates an instance of a registered word embedding evaluation function. Parameters ---------- kind : ['similarity', 'analogy'] Return only valid names for similarity, analogy or both kinds of functions. name : str The evaluation function name (case-insensitive). Returns ------- An instance of :class:`gluonnlp.embedding.evaluation.WordEmbeddingAnalogyFunction`: or :class:`gluonnlp.embedding.evaluation.WordEmbeddingSimilarityFunction`: An instance of the specified evaluation function. """ if kind not in _REGSITRY_KIND_CLASS_MAP.keys(): raise KeyError( 'Cannot find `kind` {}. Use ' '`list_evaluation_functions(kind=None).keys()` to get' 'all the valid kinds of evaluation functions.'.format(kind)) create_ = registry.get_create_func( _REGSITRY_KIND_CLASS_MAP[kind], 'word embedding {} evaluation function'.format(kind)) return create_(name, **kwargs)
def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `source`, use :func:`gluonnlp.embedding.list_sources`. Parameters ---------- embedding_name : str The token embedding name (case-insensitive). kwargs : dict All other keyword arguments are passed to the initializer of token embedding class. For example `create(embedding_name='fasttext', source='wiki.simple', load_ngrams=True)` will return `FastText(source='wiki.simple', load_ngrams=True)`. Returns ------- An instance of :class:`gluonnlp.embedding.TokenEmbedding`: A token embedding instance that loads embedding vectors from an externally hosted pre-trained token embedding file. """ create_text_embedding = registry.get_create_func(TokenEmbedding, 'token embedding') return create_text_embedding(embedding_name, **kwargs)
def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `source`, use :func:`gluonnlp.embedding.list_sources`. Parameters ---------- embedding_name : str The token embedding name (case-insensitive). kwargs : dict All other keyword arguments are passed to the initializer of token embedding class. For example `create(embedding_name='fasttext', source='wiki.simple', load_ngrams=True)` will return `FastText(source='wiki.simple', load_ngrams=True)`. Returns ------- An instance of :class:`gluonnlp.embedding.TokenEmbedding`: A token embedding instance that loads embedding vectors from an externally hosted pre-trained token embedding file. """ create_text_embedding = registry.get_create_func(TokenEmbedding, 'token embedding') return create_text_embedding(embedding_name, **kwargs)
def create(kind, name, **kwargs): """Creates an instance of a registered word embedding evaluation function. Parameters ---------- kind : ['similarity', 'analogy'] Return only valid names for similarity, analogy or both kinds of functions. name : str The evaluation function name (case-insensitive). Returns ------- An instance of :class:`gluonnlp.embedding.evaluation.WordEmbeddingAnalogyFunction`: or :class:`gluonnlp.embedding.evaluation.WordEmbeddingSimilarityFunction`: An instance of the specified evaluation function. """ if kind not in _REGSITRY_KIND_CLASS_MAP.keys(): raise KeyError( 'Cannot find `kind` {}. Use ' '`list_evaluation_functions(kind=None).keys()` to get' 'all the valid kinds of evaluation functions.'.format(kind)) create_ = registry.get_create_func( _REGSITRY_KIND_CLASS_MAP[kind], 'word embedding {} evaluation function'.format(kind)) return create_(name, **kwargs)
def create(name, **kwargs): """Creates an instance of a registered dataset. Parameters ---------- name : str The dataset name (case-insensitive). Returns ------- An instance of :class:`mxnet.gluon.data.Dataset` constructed with the keyword arguments passed to the create function. """ create_ = registry.get_create_func(Dataset, 'dataset') return create_(name, **kwargs)
def create(name, **kwargs): """Creates an instance of a registered dataset. Parameters ---------- name : str The dataset name (case-insensitive). Returns ------- An instance of :class:`mxnet.gluon.data.Dataset` constructed with the keyword arguments passed to the create function. """ create_ = registry.get_create_func(Dataset, 'dataset') return create_(name, **kwargs)
def create_subword_function(subword_function_name, **kwargs): """Creates an instance of a subword function.""" create_ = registry.get_create_func(SubwordFunction, 'token embedding') return create_(subword_function_name, **kwargs)
Returns ------- list of tuples A (name, value) tuple list. """ name, value = self.get() if not isinstance(name, list): name = [name] if not isinstance(value, list): value = [value] return list(zip(name, value)) # pylint: disable=invalid-name register = registry.get_register_func(EvalMetric, 'metric') alias = registry.get_alias_func(EvalMetric, 'metric') _create = registry.get_create_func(EvalMetric, 'metric') # pylint: enable=invalid-name def create(metric, *args, **kwargs): """Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function. Parameters ---------- metric : str or callable Specifies the metric to create. This argument must be one of the below: - Name of a metric. - An instance of `EvalMetric`. - A list, each element of which is a metric or a metric name. - An evaluation function that computes custom metric for a given batch of