File size: 2,924 Bytes
21d5444 |
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 87 88 89 90 91 92 93 94 95 |
"""Script to generate splits for benchmarking text embedding clustering.
Based on data from GermEval 2019 Shared Task on Hierarchical Tesk Classification (https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html)."""
import os
import random
import sys
from collections import Counter
import jsonlines
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
random.seed(42)
# path to "data" folder, can be retrieved from here: https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc/germeval2019t1-public-data-final.zip
DATA_PATH = sys.argv[1]
INCLUDE_BODY = (
True # True: combine title and article body (p2p), False: only title (s2s)
)
NUM_SPLITS = 10
SPLIT_RANGE = np.array([0.1, 1.0])
def get_samples(soup, include_body=INCLUDE_BODY):
d1_counter = Counter([d1.string for d1 in soup.find_all("topic", {"d": 1})])
samples = []
for book in soup.find_all("book"):
if book.title.string is None or book.body.string is None:
continue
d0_topics = list(set([d.string for d in book.find_all("topic", {"d": 0})]))
d1_topics = list(set([d.string for d in book.find_all("topic", {"d": 1})]))
if len(d0_topics) != 1:
continue
if len(d1_topics) < 1 or len(d1_topics) > 2:
continue
d0_label = d0_topics[0]
d1_label = sorted(d1_topics, key=lambda x: d1_counter[x])[0]
text = book.title.string
if include_body:
text += "\n" + book.body.string
samples.append([text, d0_label, d1_label])
return pd.DataFrame(samples, columns=["sentences", "d0_label", "d1_label"])
def get_split(frame, label="d0_label", split_range=SPLIT_RANGE):
samples = random.randint(*(split_range * len(frame)).astype(int))
return (
frame.sample(samples)[["sentences", label]]
.rename(columns={label: "labels"})[["sentences", "labels"]]
.to_dict("list")
)
def write_sets(name, sets):
with jsonlines.open(name, "w") as f_out:
f_out.write_all(sets)
train = open(os.path.join(DATA_PATH, "blurbs_train.txt"), encoding="utf-8").read()
dev = open(os.path.join(DATA_PATH, "blurbs_dev.txt"), encoding="utf-8").read()
test = open(os.path.join(DATA_PATH, "blurbs_test.txt"), encoding="utf-8").read()
soup = BeautifulSoup(train + "\n\n" + dev + "\n\n" + test, "html.parser")
samples = get_samples(soup)
sets = []
# coarse clustering
for _ in range(NUM_SPLITS):
sets.append(get_split(samples))
# fine grained clustering inside top-level category (d0)
for d0 in samples["d0_label"].unique():
sets.append(
(samples[samples.d0_label == d0])
.rename(columns={"d1_label": "labels"})[["sentences", "labels"]]
.to_dict("list")
)
# fine grained clustering
for _ in range(NUM_SPLITS):
sets.append(get_split(samples, label="d1_label"))
write_sets("test.jsonl", sets)
|