File size: 3,938 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 | from __future__ import annotations
import itertools
import numpy as np
from datasets import Dataset, DatasetDict
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskClustering import AbsTaskClustering
from ....abstasks.AbsTaskClusteringFast import AbsTaskClusteringFast
class StackExchangeClusteringP2PFast(AbsTaskClusteringFast):
metadata = TaskMetadata(
name="StackExchangeClusteringP2P.v2",
description="Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs.",
reference="https://arxiv.org/abs/2104.07081",
dataset={
"path": "mteb/stackexchange-clustering-p2p",
"revision": "815ca46b2622cec33ccafc3735d572c266efdb44",
},
type="Clustering",
category="p2p",
eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="v_measure",
date=("2021-01-01", "2021-04-14"),
form=["written"],
domains=["Web"],
task_subtypes=["Thematic clustering"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""@article{geigle:2021:arxiv,
author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
journal = {arXiv preprint},
volume = {abs/2104.07081},
year = {2021},
url = {http://arxiv.org/abs/2104.07081},
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
n_samples={"test": 16000},
avg_character_length={"test": 1090.7},
)
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=16000,
)
class StackExchangeClusteringP2P(AbsTaskClustering):
superseeded_by = "StackExchangeClusteringP2P.v2"
metadata = TaskMetadata(
name="StackExchangeClusteringP2P",
description="Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs.",
reference="https://arxiv.org/abs/2104.07081",
dataset={
"path": "mteb/stackexchange-clustering-p2p",
"revision": "815ca46b2622cec33ccafc3735d572c266efdb44",
},
type="Clustering",
category="p2p",
eval_splits=["test"],
eval_langs=["eng-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": 75000},
avg_character_length={"test": 1090.7},
)
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