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nlp-longformer
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837 B
837 B
Operator: nlp-longformer
Author: Kyle He, Jael Gu
Overview
Interface
__init__(self, model_name: str, framework: str = 'pytorch')
Args:
- model_name:
- the model name for embedding
- supported types: str, for example 'xxx' or 'xxx'
- framework:
- the framework of the model
- supported types: str, default is 'pytorch'
__call__(self, call_arg_1: xxx)
Args:
- txt:
- input text in words, sentences, or paragraphs
- supported types: str
Returns:
The Operator returns a tuple Tuple[('feature_vector', numpy.ndarray)] containing following fields:
- feature_vector:
- the embedding of the text
- data type: numpy.ndarray
- shape: (x, dim) where x is number of vectors and dim is dimension of vector depending on model_name
Requirements
How it works
Reference
837 B
837 B
Operator: nlp-longformer
Author: Kyle He, Jael Gu
Overview
Interface
__init__(self, model_name: str, framework: str = 'pytorch')
Args:
- model_name:
- the model name for embedding
- supported types: str, for example 'xxx' or 'xxx'
- framework:
- the framework of the model
- supported types: str, default is 'pytorch'
__call__(self, call_arg_1: xxx)
Args:
- txt:
- input text in words, sentences, or paragraphs
- supported types: str
Returns:
The Operator returns a tuple Tuple[('feature_vector', numpy.ndarray)] containing following fields:
- feature_vector:
- the embedding of the text
- data type: numpy.ndarray
- shape: (x, dim) where x is number of vectors and dim is dimension of vector depending on model_name