Source code for xpmir.text.adapters

from typing import List

from datamaestro.record import Record
from experimaestro import Param
from import TextItem
from xpmir.utils.convert import Converter

from .encoders import InputType, RepresentationOutput, TokenizedTextEncoderBase

[docs]class MeanTextEncoder(TokenizedTextEncoderBase[InputType, RepresentationOutput]): """Returns the mean of the word embeddings""" encoder: Param[TokenizedTextEncoderBase[InputType, RepresentationOutput]] def __initialize__(self, options): self.encoder.__initialize__(options) def static(self): return self.encoder.static() @property def dimension(self): return self.encoder.dimension def forward(self, texts: List[InputType], options=None) -> RepresentationOutput: emb_texts = self.encoder(texts, options=options) # Computes the mean over the time dimension (vocab output is batch x time x dim) return emb_texts.value.mean(1)
[docs]class TopicTextConverter(Converter[Record, str]): """Extracts the text from a topic""" def __call__(self, input: Record) -> str: return input[TextItem].text