Initialize

OV_Text.py

@@ -0,0 +1,84 @@
+import os
+
+import datasets
+
+_DESCRIPTION = """\
+OVText
+"""
+
+_CITATION = """\
+"""
+
+_BASE_URL = "https://huggingface.co/datasets/duyhngoc/OV_Text/resolve/main/data/"
+TRAIN_FILE = "train.txt"
+VALIDATION_FILE = "validation.txt"
+TEST_FILE = "test.txt"
+
+
+class UTSTextConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        super(UTSTextConfig, self).__init__(**kwargs)
+
+
+class UTSText(datasets.GeneratorBasedBuilder):
+    """OV Word Tokenize datasets"""
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        UTSTextConfig(
+            name="small", version=VERSION, description="OV_Text Small"),
+        UTSTextConfig(
+            name="base", version=VERSION, description="OV_Text Base"),
+        UTSTextConfig(
+            name="large", version=VERSION, description="OV_Text Large")
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=None,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        subset_folder = self.config.name
+        train_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TRAIN_FILE))
+        validation_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, VALIDATION_FILE))
+        test_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TEST_FILE))
+
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_file}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": validation_file}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": test_file}
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        print(filepath)
+        guid = 0
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                print(line)
+                if line.strip() != "":
+                    item = {
+                        "text": line.strip()
+                    }
+                    yield guid, item
+                    guid += 1

README.md

@@ -1,3 +1,88 @@
 ---
-license: apache-2.0
+annotations_creators:
+- no-annotation
+language:
+- vi
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: OV_Text
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-generation
 ---
+
+# Dataset Card for OV_Text
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+The OV_Text dataset is a collection of 100,000 sentences sourced from various news articles.
+
+Out of the 10,000 sentences in the dataset, 5,000 sentences have a length ranging from 50 to 150, while the other 5,000 sentences have a length ranging from 20 to 50. This distribution of sentence lengths provides a diverse range of text samples that can be used to train and test natural language processing models.
+
+### Dataset Summary
+
+### Supported Tasks and Leaderboards
+
+
+### Languages
+
+## Dataset Structure
+
+### Data Instances
+
+### Data Fields
+
+
+### Data Splits
+
+| name    | train   | validation | test   |
+|---------|--------:|-----------:|-------:|
+| small   | 1600    | 200        | 200    | 
+| base    | 8000    | 1000       | 1000   | 
+| large   | 95000   | 2500       | 2500   | 
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+
+### Source Data
+
+
+### Annotations
+
+
+
+## Additional Information
+
+### Licensing Information
+
+The dataset is released under Apache 2.0.
+
+### Citation Information
+
+
+### Contributions

eval.py

@@ -0,0 +1,16 @@
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+from os.path import dirname, join
+
+dataset = load_dataset("duyhngoc/OV_Text")
+sentences = dataset["train"]["text"]
+
+# compute histogram of sentence lengths with bin size = 10
+pwd = dirname(__file__)
+tmp = join(pwd, "tmp")
+lengths = [len(s) for s in sentences]
+plt.hist(lengths, bins=range(0, max(lengths), 10))
+# add title of plot
+plt.title("Histogram of sentence lengths")
+plt.savefig(join(tmp, "histogram.png"))
+plt.show()

statistics.py

@@ -0,0 +1,26 @@
+from os.path import dirname, join
+
+
+def count_lines(filepath):
+    with open(filepath, encoding="utf-8") as f:
+        lines = f.read().splitlines()
+        return len(lines)
+
+
+def statistic(subset_folder):
+    print("Train\t\t", count_lines(join(subset_folder, "train.txt")))
+    print("Validation\t", count_lines(join(subset_folder, "validation.txt")))
+    print("Test\t\t", count_lines(join(subset_folder, "test.txt")))
+
+
+pwd = dirname(__file__)
+dataset_folder = join(pwd, "data")
+
+print("Subset Small")
+statistic(join(dataset_folder, "small"))
+
+print("Subset Base")
+statistic(join(dataset_folder, "base"))
+
+print("Subset Large")
+statistic(join(dataset_folder, "large"))