MichaelR207
commited on
Commit
•
0b99a0b
1
Parent(s):
d220f97
Fix HuggingFace Download
Browse files- MultilingualSimplification.py +85 -64
MultilingualSimplification.py
CHANGED
@@ -17,6 +17,8 @@
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import pandas as pd
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import os
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from collections import defaultdict
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import datasets
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@@ -70,116 +72,124 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE."""
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_SUBCORPORA = {
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"NewselaEN": {
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-
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},
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"WikiAutoEN": {
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"path": "
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"language": "en"
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},
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"ASSET": {
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"path": "
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"language": "en"
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},
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"Simplext": {
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},
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"NewselaES": {
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},
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"Terence": {
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"path" : "
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"language": "it"
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},
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"Teacher": {
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"path": "
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"language": "it"
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},
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"SimpitikiWiki": {
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"path": "
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"language": "it"
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},
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"AdminIt": {
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"path": "
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"language": "it"
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},
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"PaCCSS-IT": {
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"path": "
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"language": "it"
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},
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"CLEAR" : {
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"path" : "
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"language": "fr"
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},
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"WikiLargeFR": {
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"path" : "
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"language": "fr"
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},
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"EasyJapanese": {
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"path": "
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"language": "ja"
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},
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"EasyJapaneseExtended": {
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"path": "
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"language": "ja"
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},
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"PorSimples" : {
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"path": "
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"language": "pt-br"
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},
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"TextComplexityDE" : {
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"path": "
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"language": "de"
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},
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"GEOLinoTest" : {
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"path" : "
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"language": "de"
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},
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"GermanNews" : {
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},
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"CBST": {
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},
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"DSim": {
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},
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"SimplifyUR": {
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},
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"RuWikiLarge": {
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"path" : "
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"language": "ru"
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},
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"RSSE" : {
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"path": "
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"language": "ru"
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},
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"RuAdaptLit" : {
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"path": "./data/Russian/RuAdapt Literature",
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"language": "ru"
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},
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"RuAdaptFairytales" : {
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"path": "
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"language": "ru"
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},
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"RuAdaptEncy" : {
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"path" : "
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"language": "ru"
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},
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"TSSlovene" : {
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"path" : "
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"language": "sl"
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}
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}
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_LANGUAGES = {
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"English":'en',
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"Spanish":'es',
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@@ -290,25 +300,34 @@ class MultilingualSimplification(datasets.GeneratorBasedBuilder):
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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-
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if (self.config.name == 'all'):
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for subcorpus in _SUBCORPORA:
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elif (self.config.name in _LANGUAGES):
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lang_code = _LANGUAGES[self.config.name]
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for subcorpus in _SUBCORPORA:
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if _SUBCORPORA[subcorpus]['language'] == lang_code:
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elif (self.config.name in _SUBCORPORA):
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else:
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print("Invalid configuration name: " + self.config.name + ". Try 'all', 'English', 'ASSET', etc.")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths":
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"split": "train",
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},
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),
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@@ -316,7 +335,7 @@ class MultilingualSimplification(datasets.GeneratorBasedBuilder):
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths":
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"split": "val",
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},
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),
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@@ -324,7 +343,7 @@ class MultilingualSimplification(datasets.GeneratorBasedBuilder):
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths":
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"split": "test"
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},
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),
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@@ -336,16 +355,18 @@ class MultilingualSimplification(datasets.GeneratorBasedBuilder):
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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df = pd.DataFrame()
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if (len(filepaths) > 1):
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for
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if os.path.exists(
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# shuffle the combined dataset
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df = df.sample(frac=1, random_state=3600).reset_index(drop=True)
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else:
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if len(df) > 0:
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for key, row in df.iterrows():
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import pandas as pd
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import os
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from collections import defaultdict
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import urllib.parse
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import datasets
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SOFTWARE."""
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_SUBCORPORA = {
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# "NewselaEN": {
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# "path": "data/English/Newsela EN",
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# "language": "en"
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# },
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"WikiAutoEN": {
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"path": "data/English/WikiAuto",
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"language": "en"
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},
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"ASSET": {
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"path": "data/English/ASSET",
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"language": "en"
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},
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# "Simplext": {
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# "path": "data/Spanish/Simplext",
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# "language": "es"
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# },
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# "NewselaES": {
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# "path": "data/Spanish/Newsela ES",
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# "language": "es"
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# },
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"Terence": {
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"path" : "data/Italian/Terence",
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"language": "it"
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},
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"Teacher": {
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"path": "data/Italian/Teacher",
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"language": "it"
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},
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"SimpitikiWiki": {
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"path": "data/Italian/Simpitiki Italian Wikipedia",
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"language": "it"
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},
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"AdminIt": {
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"path": "data/Italian/AdminIT",
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"language": "it"
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},
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"PaCCSS-IT": {
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"path": "data/Italian/PaCCSS-IT Corpus",
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"language": "it"
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},
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"CLEAR" : {
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"path" : "data/French/CLEAR Corpus",
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"language": "fr"
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},
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"WikiLargeFR": {
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"path" : "data/French/WikiLargeFR Corpus",
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"language": "fr"
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},
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"EasyJapanese": {
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"path": "data/Japanese/Easy Japanese Corpus",
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"language": "ja"
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},
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"EasyJapaneseExtended": {
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"path": "data/Japanese/Easy Japanese Extended",
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"language": "ja"
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},
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"PorSimples" : {
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"path": "data/Brazilian Portuguese/PorSimples",
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"language": "pt-br"
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},
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"TextComplexityDE" : {
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"path": "data/German/TextComplexityDE Parallel Corpus",
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"language": "de"
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},
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"GEOLinoTest" : {
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"path" : "data/German/GEOLino Corpus",
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"language": "de"
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},
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# "GermanNews" : {
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# "path" : "data/German/German News",
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# "language": "de"
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# },
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# "CBST": {
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# "path" : "data/Basque/CBST",
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# "language": "eu"
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# },
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# "DSim": {
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# "path": "data/Danish/DSim Corpus",
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# "language": "da"
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# },
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# "SimplifyUR": {
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# "path": "data/Urdu/SimplifyUR",
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# "language": "ur"
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# },
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"RuWikiLarge": {
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"path" : "data/Russian/RuWikiLarge",
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"language": "ru"
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},
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"RSSE" : {
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"path": "data/Russian/RSSE Corpus",
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"language": "ru"
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},
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# "RuAdaptLit" : {
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# "path": "data/Russian/RuAdapt Literature",
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# "language": "ru"
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# },
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"RuAdaptFairytales" : {
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"path": "data/Russian/RuAdapt Fairytales",
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"language": "ru"
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},
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"RuAdaptEncy" : {
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"path" : "data/Russian/RuAdapt Ency",
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"language": "ru"
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},
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"TSSlovene" : {
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"path" : "data/Slovene/Text Simplification Slovene",
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"language": "sl"
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}
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}
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_URL = "https://huggingface.co/datasets/MichaelR207/MultiSim/raw/main"
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_URLS = {
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dataset+"-"+split: urllib.parse.quote(os.path.join(_URL, _SUBCORPORA[dataset]["path"] + "_" + split + ".csv"), safe=':/')
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for split in ["train", "val", "test"]
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for dataset in _SUBCORPORA.keys()
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}
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_LANGUAGES = {
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"English":'en',
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"Spanish":'es',
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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download_urls = {}
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if (self.config.name == 'all'):
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for subcorpus in _SUBCORPORA:
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download_urls[subcorpus + "-train"] = _URLS[subcorpus+"-train"]
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download_urls[subcorpus + "-test"] = _URLS[subcorpus+"-test"]
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download_urls[subcorpus + "-val"] = _URLS[subcorpus+"-val"]
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elif (self.config.name in _LANGUAGES):
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lang_code = _LANGUAGES[self.config.name]
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for subcorpus in _SUBCORPORA:
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if _SUBCORPORA[subcorpus]['language'] == lang_code:
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download_urls[subcorpus + "-train"] = _URLS[subcorpus+"-train"]
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download_urls[subcorpus + "-test"] = _URLS[subcorpus+"-test"]
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download_urls[subcorpus + "-val"] = _URLS[subcorpus+"-val"]
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elif (self.config.name in _SUBCORPORA):
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download_urls[self.config.name + "-train"] = _URLS[self.config.name+"-train"]
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download_urls[self.config.name + "-test"] = _URLS[self.config.name+"-test"]
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download_urls[self.config.name + "-val"] = _URLS[self.config.name+"-val"]
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else:
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print("Invalid configuration name: " + self.config.name + ". Try 'all', 'English', 'ASSET', etc.")
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downloaded_files = dl_manager.download_and_extract(download_urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths": downloaded_files,
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"split": "train",
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},
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),
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths": downloaded_files,
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"split": "val",
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},
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),
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepaths": downloaded_files,
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"split": "test"
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},
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),
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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df = pd.DataFrame()
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if (len(filepaths.keys()) > 1):
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for dataset, path in filepaths.items():
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if os.path.exists(path):
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if dataset.endswith("-"+split):
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df = pd.concat([df, pd.read_csv(path)])
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# shuffle the combined dataset
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df = df.sample(frac=1, random_state=3600).reset_index(drop=True)
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else:
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dataset = list(filepaths.keys())[0]
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if os.path.exists(filepaths[dataset]):
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df = pd.read_csv(filepaths[dataset])
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if len(df) > 0:
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for key, row in df.iterrows():
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