Convert to Parquet format and remove loading script

Dataset loading scripts are no longer supported by the `datasets` library (v3+).

This PR:
- Converts all splits (train, test, validation) to Parquet format
- Removes the `ptb_text_only.py` loading script

After this change, the dataset can be loaded with:
```python
from datasets import load_dataset
ds = load_dataset('ptb-text-only/ptb_text_only')
```

No `trust_remote_code=True` needed.

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5078fcc161548ad940c63610099ebfc9af25eca7bf7ec274900d0aa153484cc1
+size 257503

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a86b8f1cdd524640b97b71da4a1ebdae60aaeb61132dc4c457db5f868d8e5db6
+size 2915174

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4373b0c0cafc169366b8f76e96a9c9917b9b1cd003fecdc82326c7da14c88b8f
+size 232009

ptb_text_only.py

@@ -1,146 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-    Load the Penn Treebank dataset.
-
-    This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall
-    Street Journal material.
-"""
-
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{marcus-etal-1993-building,
-    title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank",
-    author = "Marcus, Mitchell P.  and
-      Santorini, Beatrice  and
-      Marcinkiewicz, Mary Ann",
-    journal = "Computational Linguistics",
-    volume = "19",
-    number = "2",
-    year = "1993",
-    url = "https://www.aclweb.org/anthology/J93-2004",
-    pages = "313--330",
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. This corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC99T42"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "LDC User Agreement for Non-Members"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/"
-_TRAINING_FILE = "ptb.train.txt"
-_DEV_FILE = "ptb.valid.txt"
-_TEST_FILE = "ptb.test.txt"
-
-
-class PtbTextOnlyConfig(datasets.BuilderConfig):
-    """BuilderConfig for PtbTextOnly"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig PtbTextOnly.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PtbTextOnlyConfig, self).__init__(**kwargs)
-
-
-class PtbTextOnly(datasets.GeneratorBasedBuilder):
-    """Load the Penn Treebank dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        PtbTextOnlyConfig(
-            name="penn_treebank",
-            version=VERSION,
-            description="Load the Penn Treebank dataset",
-        ),
-    ]
-
-    def _info(self):
-        features = datasets.Features({"sentence": datasets.Value("string")})
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # 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.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        my_urls = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        data_dir = dl_manager.download_and_extract(my_urls)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["dev"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
-        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
-        # The key is not important, it's more here for legacy reason (legacy from tfds)
-        with open(filepath, encoding="utf-8") as f:
-            for id_, line in enumerate(f):
-                line = line.strip()
-                yield id_, {"sentence": line}