File size: 3,829 Bytes
83d24b2 | 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 | from __future__ import annotations
import ast
import datasets
import numpy as np
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class IndonesianMongabayConservationClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="IndonesianMongabayConservationClassification",
description="Conservation dataset that was collected from mongabay.co.id contains topic-classification task (multi-label format) and sentiment classification. This task only covers sentiment analysis (positive, neutral negative)",
reference="https://aclanthology.org/2023.sealp-1.4/",
dataset={
"path": "Datasaur/mongabay-experiment",
"revision": "c9e9f2c09836bfec57c543ab65983f3398e9657a",
},
type="Classification",
category="s2s",
date=("2012-01-01", "2023-12-31"),
eval_splits=["validation", "test"],
eval_langs=["ind-Latn"],
main_score="f1",
form=["written"],
domains=["Web"],
task_subtypes=["Sentiment/Hate speech"],
license="Not specified",
socioeconomic_status="medium",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{fransiska-etal-2023-utilizing,
title = "Utilizing Weak Supervision to Generate {I}ndonesian Conservation Datasets",
author = "Fransiska, Mega and
Pitaloka, Diah and
Saripudin, Saripudin and
Putra, Satrio and
Sutawika*, Lintang",
editor = "Wijaya, Derry and
Aji, Alham Fikri and
Vania, Clara and
Winata, Genta Indra and
Purwarianti, Ayu",
booktitle = "Proceedings of the First Workshop in South East Asian Language Processing",
month = nov,
year = "2023",
address = "Nusa Dua, Bali, Indonesia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sealp-1.4",
doi = "10.18653/v1/2023.sealp-1.4",
pages = "30--54",
}
""",
n_samples={"validation": 984, "test": 970},
avg_character_length={"validation": 1675.8, "test": 1675.5},
)
def dataset_transform(self):
splits = self.metadata_dict["eval_splits"]
class_labels = ["positif", "netral", "negatif"]
ds = {}
# Include training because the classification task requires it
train_split = self.dataset["train"]
train_docs: list = []
train_labels: list = []
for text, label in zip(train_split["text"], train_split["softlabel"]):
soft_label = ast.literal_eval(label)
if len(soft_label) == len(class_labels):
train_docs.append(text)
hard_label = np.argmax(soft_label)
train_labels.append(hard_label)
ds["train"] = datasets.Dataset.from_dict(
{
"text": train_docs,
"label": train_labels,
}
)
documents: list = []
labels: list = []
# For evaluation
for split in splits:
ds_split = self.dataset[split]
for text, label in zip(ds_split["text"], ds_split["softlabel"]):
if label in class_labels:
documents.append(text)
labels.append(class_labels.index(label))
assert len(documents) == len(labels)
ds[split] = datasets.Dataset.from_dict(
{
"text": documents,
"label": labels,
}
)
self.dataset = datasets.DatasetDict(ds)
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