File size: 4,087 Bytes
73cc8d2 | 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | from __future__ import annotations
import itertools
import numpy as np
from datasets import Dataset, DatasetDict
from mteb.abstasks.AbsTaskClustering import AbsTaskClustering
from mteb.abstasks.AbsTaskClusteringFast import AbsTaskClusteringFast
from mteb.abstasks.TaskMetadata import TaskMetadata
NUM_SAMPLES = 2048
class BlurbsClusteringP2P(AbsTaskClustering):
superseeded_by = "BlurbsClusteringP2P.v2"
metadata = TaskMetadata(
name="BlurbsClusteringP2P",
description="Clustering of book titles+blurbs. Clustering of 28 sets, either on the main or secondary genre.",
reference="https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html",
dataset={
"path": "slvnwhrl/blurbs-clustering-p2p",
"revision": "a2dd5b02a77de3466a3eaa98ae586b5610314496",
},
type="Clustering",
category="p2p",
eval_splits=["test"],
eval_langs=["deu-Latn"],
main_score="v_measure",
date=None,
form=None,
domains=None,
task_subtypes=None,
license=None,
socioeconomic_status=None,
annotations_creators=None,
dialect=None,
text_creation=None,
bibtex_citation=None,
n_samples={"test": 174637},
avg_character_length={"test": 664.09},
)
class BlurbsClusteringP2PFast(AbsTaskClusteringFast):
# a faster version of BlurbsClusteringP2P, since it does not sample from the same distribution we can't use the AbsTaskClusteringFast, instead we
# simply downsample each cluster.
metadata = TaskMetadata(
name="BlurbsClusteringP2P.v2",
description="Clustering of book titles+blurbs. Clustering of 28 sets, either on the main or secondary genre.",
reference="https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html",
dataset={
"path": "slvnwhrl/blurbs-clustering-p2p",
"revision": "a2dd5b02a77de3466a3eaa98ae586b5610314496",
},
type="Clustering",
category="p2p",
eval_splits=["test"],
eval_langs=["deu-Latn"],
main_score="v_measure",
date=(
"1900-01-01",
"2019-12-31",
), # since it is books it is likely to be from the 20th century -> paper from 2019
form=["written"],
domains=["Fiction"],
task_subtypes=["Thematic clustering"],
license="cc-by-nc-4.0",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{Remus2019GermEval2T,
title={GermEval 2019 Task 1: Hierarchical Classification of Blurbs},
author={Steffen Remus and Rami Aly and Chris Biemann},
booktitle={Conference on Natural Language Processing},
year={2019},
url={https://api.semanticscholar.org/CorpusID:208334484}
}""",
n_samples={"test": NUM_SAMPLES},
avg_character_length={"test": 664.09},
)
def dataset_transform(self):
ds = dict()
for split in self.metadata.eval_splits:
labels = list(itertools.chain.from_iterable(self.dataset[split]["labels"]))
sentences = list(
itertools.chain.from_iterable(self.dataset[split]["sentences"])
)
# Remove sentences and labels with only 1 label example.
unique_labels, counts = np.unique(labels, return_counts=True)
solo_label_idx = np.where(counts == 1)
solo_labels = unique_labels[solo_label_idx]
for solo_label in solo_labels:
loc = labels.index(solo_label)
labels.pop(loc)
sentences.pop(loc)
ds[split] = Dataset.from_dict({"labels": labels, "sentences": sentences})
self.dataset = DatasetDict(ds)
self.dataset = self.stratified_subsampling(
self.dataset,
self.seed,
self.metadata.eval_splits,
label="labels",
n_samples=NUM_SAMPLES,
)
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