class_docstr: Text Pair Classification Model for tasks such as Recognizing Textual Entailment, build upon TextClassifier.
  
  The model takes document embeddings and puts resulting text representation(s) into a linear layer to get the
  actual class label. We provide two ways to embed the DataPairs: Either by embedding both DataPoints
  and concatenating the resulting vectors ("embed_separately=True") or by concatenating the DataPoints and embedding
  the resulting vector ("embed_separately=False").
class_docstr_tok: ["Text","Pair","Classification","Model","for","tasks","such","as","Recognizing","Textual","Entailment",",","build","upon","TextClassifier",".","The","model","takes","document","embeddings","and","puts","resulting","text","representation","(","s",")","into","a","linear","layer","to","get","the","actual","class","label",".","We","provide","two","ways","to","embed","the","DataPairs",":","Either","by","embedding","both","DataPoints","and","concatenating","the","resulting","vectors","(","``","embed_separately=True","''",")","or","by","concatenating","the","DataPoints","and","embedding","the","resulting","vector","(","``","embed_separately=False","''",")","."]
code: def _get_embedding_for_data_point(
      self, prediction_data_point: TextPair
  ) -> torch.Tensor:
      embedding_names = self.embeddings.get_names()
      if self.embed_separately:
          self.embeddings.embed(
              [
                  prediction_data_point.first,
                  prediction_data_point.second,
              ]
          )
          return torch.cat(
              [
                  prediction_data_point.first.get_embedding(
                      embedding_names
                  ),
                  prediction_data_point.second.get_embedding(
                      embedding_names
                  ),
              ],
              0,
          )
      else:
          concatenated_sentence = Sentence(
              prediction_data_point.first.to_tokenized_string()
              + self.sep
              + prediction_data_point.second.to_tokenized_string(),
              use_tokenizer=False,
          )
          self.embeddings.embed(concatenated_sentence)
          return concatenated_sentence.get_embedding(embedding_names)
  
file_path: flair/flair/models/pairwise_classification_model.py
imports: import typing
  from typing import List
  import torch
  import flair.embeddings
  import flair.nn
  from flair.data import Corpus, Sentence, TextPair, _iter_dataset
label_desc: Use image node_id 4 for calling the TextPairClassifier obj's underlying member method code with example usage: obj._get_embedding_for_data_point(prediction_data_point) and returns: torch, concatenated_sentence