Commit
•
cccc887
1
Parent(s):
645dd0c
Upload filipino_gay_lang.py with huggingface_hub
Browse files- filipino_gay_lang.py +115 -0
filipino_gay_lang.py
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from pathlib import Path
|
2 |
+
from typing import Dict, List, Tuple
|
3 |
+
|
4 |
+
import datasets
|
5 |
+
import pandas as pd
|
6 |
+
|
7 |
+
from seacrowd.utils.configs import SEACrowdConfig
|
8 |
+
from seacrowd.utils.constants import SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, Tasks
|
9 |
+
|
10 |
+
# TODO: Add BibTeX citation
|
11 |
+
_CITATION = r"""\
|
12 |
+
@article{oco2015witchebelles,
|
13 |
+
author = {Oco, Nathaniel and Fajutagana, Raymart and Lim, Christine Mae and Mi{\~n}on, Judi Diane and Morano, Julie-Ann and Tinoco, Ryan Christian},
|
14 |
+
title = {Witchebelles Anata Magcharot kay Mudra na Nagsusuba si Akech: Developing a Rule-based Unidirectional Beki Lingo to Filipino Translator},
|
15 |
+
journal = {Journal of Sciences, Technology and Arts Research},
|
16 |
+
volume = {1},
|
17 |
+
number = {1},
|
18 |
+
year = {2015}
|
19 |
+
}
|
20 |
+
"""
|
21 |
+
|
22 |
+
_LOCAL = False
|
23 |
+
_LANGUAGES = ["fil"]
|
24 |
+
_DATASETNAME = "filipino_gay_lang"
|
25 |
+
_DESCRIPTION = """\
|
26 |
+
The dataset contains 4000+ Filipino tweets in gay language or lingo also called swardspeak in slang terminology.
|
27 |
+
The tweet dataset was collected from February 2013 to November 2014 using the following commonly used gay words as filters: jinet ("hot"), ditey ("here"), imbyerna ("annoying"), etc.
|
28 |
+
The original paper makes use of the corpus to develop a gay language translator to understand the meaning of phrases using gay words in Filipino.
|
29 |
+
"""
|
30 |
+
|
31 |
+
_HOMEPAGE = "https://github.com/imperialite/Philippine-Languages-Online-Corpora/tree/master/Tweets/Gay%20language"
|
32 |
+
_LICENSE = "CC-BY-SA 4.0"
|
33 |
+
_URLS = {
|
34 |
+
"gl_01": "https://github.com/imperialite/Philippine-Languages-Online-Corpora/raw/master/Tweets/Gay%20language/gl%20-%2001.xlsx",
|
35 |
+
"gl_02": "https://github.com/imperialite/Philippine-Languages-Online-Corpora/raw/master/Tweets/Gay%20language/gl%20-%2002.xlsx",
|
36 |
+
"gl_03": "https://github.com/imperialite/Philippine-Languages-Online-Corpora/raw/master/Tweets/Gay%20language/gl%20-%2003.xlsx",
|
37 |
+
}
|
38 |
+
|
39 |
+
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
|
40 |
+
_SOURCE_VERSION = "1.0.0"
|
41 |
+
_SEACROWD_VERSION = "2024.06.20"
|
42 |
+
|
43 |
+
|
44 |
+
class FilipinoGayLangDataset(datasets.GeneratorBasedBuilder):
|
45 |
+
"""This dataset contains 4000+ Filipino tweets in gay lingo/Beki/Swardspeak."""
|
46 |
+
|
47 |
+
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
|
48 |
+
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
|
49 |
+
|
50 |
+
SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
|
51 |
+
|
52 |
+
BUILDER_CONFIGS = [
|
53 |
+
SEACrowdConfig(
|
54 |
+
name=f"{_DATASETNAME}_source",
|
55 |
+
version=SOURCE_VERSION,
|
56 |
+
description=f"{_DATASETNAME} source schema",
|
57 |
+
schema="source",
|
58 |
+
subset_id=f"{_DATASETNAME}",
|
59 |
+
),
|
60 |
+
SEACrowdConfig(
|
61 |
+
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
|
62 |
+
version=SEACROWD_VERSION,
|
63 |
+
description=f"{_DATASETNAME} SEACrowd schema",
|
64 |
+
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
|
65 |
+
subset_id=f"{_DATASETNAME}",
|
66 |
+
),
|
67 |
+
]
|
68 |
+
|
69 |
+
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
|
70 |
+
|
71 |
+
def _info(self) -> datasets.DatasetInfo:
|
72 |
+
if self.config.schema == "source":
|
73 |
+
features = datasets.Features({"index": datasets.Value("string"), "text": datasets.Value("string")})
|
74 |
+
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
|
75 |
+
features = SCHEMA_TO_FEATURES[self.SEACROWD_SCHEMA_NAME.upper()]
|
76 |
+
|
77 |
+
return datasets.DatasetInfo(
|
78 |
+
description=_DESCRIPTION,
|
79 |
+
features=features,
|
80 |
+
homepage=_HOMEPAGE,
|
81 |
+
license=_LICENSE,
|
82 |
+
citation=_CITATION,
|
83 |
+
)
|
84 |
+
|
85 |
+
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
|
86 |
+
"""Returns SplitGenerators."""
|
87 |
+
|
88 |
+
data_files = {
|
89 |
+
"gl_01": Path(dl_manager.download(_URLS["gl_01"])),
|
90 |
+
"gl_02": Path(dl_manager.download(_URLS["gl_02"])),
|
91 |
+
"gl_03": Path(dl_manager.download(_URLS["gl_03"])),
|
92 |
+
}
|
93 |
+
|
94 |
+
return [
|
95 |
+
datasets.SplitGenerator(
|
96 |
+
name=datasets.Split.TRAIN,
|
97 |
+
gen_kwargs={"filepath": [data_files["gl_01"], data_files["gl_02"], data_files["gl_03"]], "split": "train"},
|
98 |
+
)
|
99 |
+
]
|
100 |
+
|
101 |
+
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
|
102 |
+
"""Yields examples as (key, example) tuples."""
|
103 |
+
|
104 |
+
df = pd.concat((pd.read_excel(file) for file in filepath), ignore_index=True).reset_index()
|
105 |
+
|
106 |
+
if self.config.schema == "source":
|
107 |
+
for row in df.itertuples():
|
108 |
+
ex = {"index": str(row.index), "text": row.message}
|
109 |
+
yield row.index, ex
|
110 |
+
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
|
111 |
+
for row in df.itertuples():
|
112 |
+
ex = {"id": str(row.index), "text": row.message}
|
113 |
+
yield row.index, ex
|
114 |
+
else:
|
115 |
+
raise ValueError(f"Invalid config: {self.config.name}")
|