from __future__ import annotations
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column the sentence pair and third column the score (0...1). Default config normalizes scores from 0...5 to 0...1
"""
def __init__(
self,
dataset_folder,
s1_col_idx=0,
s2_col_idx=1,
score_col_idx=2,
delimiter="\t",
quoting=csv.QUOTE_NONE,
normalize_scores=True,
min_score=0,
max_score=5,
):
self.dataset_folder = dataset_folder
self.score_col_idx = score_col_idx
self.s1_col_idx = s1_col_idx
self.s2_col_idx = s2_col_idx
self.delimiter = delimiter
self.quoting = quoting
self.normalize_scores = normalize_scores
self.min_score = min_score
self.max_score = max_score
def get_examples(self, filename, max_examples=0):
"""filename specified which data split to use (train.csv, dev.csv, test.csv)."""
filepath = os.path.join(self.dataset_folder, filename)
with (
gzip.open(filepath, "rt", encoding="utf8")
if filename.endswith(".gz")
else open(filepath, encoding="utf-8") as fIn
):
data = csv.reader(fIn, delimiter=self.delimiter, quoting=self.quoting)
examples = []
for id, row in enumerate(data):
score = float(row[self.score_col_idx])
if self.normalize_scores: # Normalize to a 0...1 value
score = (score - self.min_score) / (self.max_score - self.min_score)
s1 = row[self.s1_col_idx]
s2 = row[self.s2_col_idx]
examples.append(InputExample(guid=filename + str(id), texts=[s1, s2], label=score))
if max_examples > 0 and len(examples) >= max_examples:
break
return examples
class STSBenchmarkDataReader(STSDataReader):
"""Reader especially for the STS benchmark dataset. There, the sentences are in column 5 and 6, the score is in column 4.
Scores are normalized from 0...5 to 0...1
"""
def __init__(
self,
dataset_folder,
s1_col_idx=5,
s2_col_idx=6,
score_col_idx=4,
delimiter="\t",
quoting=csv.QUOTE_NONE,
normalize_scores=True,
min_score=0,
max_score=5,
):
super().__init__(
dataset_folder=dataset_folder,
s1_col_idx=s1_col_idx,
s2_col_idx=s2_col_idx,
score_col_idx=score_col_idx,
delimiter=delimiter,
quoting=quoting,
normalize_scores=normalize_scores,
min_score=min_score,
max_score=max_score,
)