class_docstr: Semantic Similarity dataset from qcri.org (Semeval 2014).
  
  Arguments:
      path (str): path to SICK_data directory
class_docstr_tok: ["Semantic","Similarity","dataset","from","qcri.org","(","Semeval","2014",")",".","Arguments",":","path","(","str",")",":","path","to","SICK_data","directory"]
docstr: Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
  in the format: Sentence1        Sentence2       Score. Data is downloaded and extracted
  from zip files if not found in directory specified by self.path.
  
  Returns:
      tuple of tuples of np.array: three tuples containing A & B sentences
          for train, dev, and text, along with a fourth tuple containing
          the scores for each AB pair.
docstr_tok: ["Load","the","SICK","semantic-relatedness","dataset",".","Data","is","a","tab-delimited","txt","file",",","in","the","format",":","Sentence1","Sentence2","Score",".","Data","is","downloaded","and","extracted","from","zip","files","if","not","found","in","directory","specified","by","self.path",".","Returns",":","tuple","of","tuples","of","np.array",":","three","tuples","containing","A","&","B","sentences","for","train",",","dev",",","and","text",",","along","with","a","fourth","tuple","containing","the","scores","for","each","AB","pair","."]
code: def load_eval_data(self):
      """
      Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
      in the format: Sentence1\tSentence2\tScore. Data is downloaded and extracted
      from zip files if not found in directory specified by self.path.
  
      Returns:
          tuple of tuples of np.array: three tuples containing A & B sentences
              for train, dev, and text, along with a fourth tuple containing
              the scores for each AB pair.
      """
      if self.zip_paths is None:
          self.load_data()
  
      trainA, trainB, devA, devB, testA, testB = [], [], [], [], [], []
      trainS, devS, testS = [], [], []
  
      with open(self.path + self.gen_filename("train"), "rb") as f:
          for line in f:
              text = line.strip().split(b"\t")
              trainA.append(text[1])
              trainB.append(text[2])
              trainS.append(text[3])
  
      with open(self.path + self.gen_filename("trial"), "rb") as f:
          for line in f:
              text = line.strip().split(b"\t")
              devA.append(text[1])
              devB.append(text[2])
              devS.append(text[3])
  
      with open(
          self.path + self.gen_filename("test_annotated"), "rb"
      ) as f:
          for line in f:
              text = line.strip().split(b"\t")
              testA.append(text[1])
              testB.append(text[2])
              testS.append(text[3])
  
      trainS = [float(s) for s in trainS[1:]]
      devS = [float(s) for s in devS[1:]]
      testS = [float(s) for s in testS[1:]]
  
      return (
          (np.array(trainA[1:]), np.array(trainB[1:])),
          (np.array(devA[1:]), np.array(devB[1:])),
          (np.array(testA[1:]), np.array(testB[1:])),
          (np.array(trainS), np.array(devS), np.array(testS)),
      )
  
imports: import logging
  import numpy
  import os
  import h5py
  from neon.data.dataiterator import NervanaDataIterator, ArrayIterator
  from neon.data.datasets import Dataset
  from neon.data.text_preprocessing import pad_sentences, pad_data
label_desc: Use image node_id 5 for calling the SICK obj's underlying member method code with example usage: obj.load_eval_data() and returns: np,  np,  np,  np,  np,  np,  np,  np,  np