File size: 3,227 Bytes
94011a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
# create__agreement_data.py
# Author: Julie Kallini
# For importing utils
import sys
sys.path.append("..")
import pandas as pd
from glob import glob
import re
from utils import gpt2_hop_tokenizer, BABYLM_DATA_PATH, marker_sg_token
from pluralizer import Pluralizer
if __name__ == "__main__":
get_vocab_dict = []
vocab = gpt2_hop_tokenizer.vocab
# Patterns for finding simple "The SUBJ VERB x x x x MARKER" sequences
control_pattern = f"(?:^| )(?:{vocab['The']}|{vocab['Ġthe']}) [1-9]+ [1-9]+ {marker_sg_token} [1-9]+ [1-9]+ [1-9]+ [1-9]+"
words4_pattern = f"(?:^| )(?:{vocab['The']}|{vocab['Ġthe']}) [1-9]+ [1-9]+ [1-9]+ [1-9]+ [1-9]+ [1-9]+ {marker_sg_token}"
# Get test file paths
test_file_path = f'{BABYLM_DATA_PATH}/babylm_data_perturbed/' + \
'babylm_hop_{}/babylm_test_affected/*'
control_files = sorted(glob(test_file_path.format("control")))
words4_files = sorted(glob(test_file_path.format("words4")))
# Iterate over files and get candidate sequences for interventions
candidate_sequences = []
for control_file_path, words4_file_path in zip(control_files, words4_files):
print(control_file_path.split("/")[-1])
assert control_file_path.split(
"/")[-1] == words4_file_path.split("/")[-1]
# Iterate over pairs of lines in file (control and words4)
control_lines = open(control_file_path, 'r').readlines()
words4_lines = open(words4_file_path, 'r').readlines()
for cl, wl in zip(control_lines, words4_lines):
# Find all matches to patterns
cseqs = re.findall(control_pattern, cl)
wseqs = re.findall(words4_pattern, wl)
# See if there is a shared pattern in control and words4 line
for cseq, wseq in zip(re.findall(control_pattern, cl), re.findall(words4_pattern, wl)):
if cseq.replace(" " + str(marker_sg_token), "") == wseq.replace(" " + str(marker_sg_token), ""):
candidate_sequences.append(
[int(s) for s in wseq.replace(
str(marker_sg_token), "").split()]
)
# Init pluralizer
pluralizer = Pluralizer()
# Iterate over candidate sequences
data = []
for seq in candidate_sequences:
# Get string version of sequence to get the subject
splitted = gpt2_hop_tokenizer.decode(seq).split()
# Get plural of subject (the word at index 1)
splitted[1] = pluralizer.pluralize(splitted[1], 2, False)
# Get GPT-2 tokens of plural version of sequence
plur_seq = gpt2_hop_tokenizer.encode(" ".join(splitted))
# If new subject form has a different number of tokens, skip
if len(plur_seq) != len(seq):
continue
# In case " the" has changed to "the", reset the first token
plur_seq[0] = seq[0]
# If singular and plural sequence are the same, skip
if seq == plur_seq:
continue
data.append([" ".join([str(s) for s in seq]),
" ".join([str(s) for s in plur_seq])])
df = pd.DataFrame(data, columns=["Singular", "Plural"])
df.to_csv("hop_agreement_data.csv")
|