update

README.md

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-version https://git-lfs.github.com/spec/v1
-oid sha256:9153f692584fe7ad587cdf6ab168be0785ede5db07f7f31a7ed40bd969b2ff40
-size 1789
+---
+annotations_creators:
+- no-annotation
+language:
+- vi
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: UTS_Text_v1
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-generation
+---
+
+# Dataset Card for UTS_Text_v1
+
+## 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 UTS_Text_v1 dataset is a collection of 10,000 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
+
+## Dataset Creation
+
+### Curation Rationale
+
+
+### Source Data
+
+
+### Annotations
+
+
+
+## Additional Information
+
+### Licensing Information
+
+The dataset is released under Apache 2.0.
+
+### Citation Information
+
+
+### Contributions
+

UTS_Text_v1.py

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-version https://git-lfs.github.com/spec/v1
-oid sha256:1bcf654c0b873b2ec89b8af9fc2560c28893d683dc824691efd3b8d556b0a01f
-size 1717
+import os
+import datasets
+
+_DESCRIPTION = """\
+UTS_Text_v1
+"""
+
+_CITATION = """\
+"""
+
+_BASE_URL = "https://huggingface.co/datasets/undertheseanlp/UTS_Text_v1/raw/main/data/"
+TRAIN_FILE = "UTS_Text_v1.txt"
+
+
+class UTS_Text_v1(datasets.GeneratorBasedBuilder):
+    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."""
+        train_file = dl_manager.download(os.path.join(_BASE_URL, TRAIN_FILE))
+        # dev_file = dl_manager.download(os.path.join(DATA_PATH, "dev.txt"))
+        # test_file = dl_manager.download(os.path.join(DATA_PATH, "test.txt"))
+        data_files = {
+            "train": train_file,
+            # "dev": dev_file,
+            # "test": test_file,
+        }
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}
+            ),
+            # datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
+            # datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+        ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            for line in f:
+                text = line.strip()
+                yield guid, {"text": text}
+                guid += 1

data/UTS_Text_v1.txt

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-size 905068

data/small/validation.txt

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-size 24631

eval.py

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-oid sha256:3383996333606968884d9705d06807f095a6d8e4de04170dc9e1439bfb4cdc51
-size 501
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+from os.path import dirname, join
+
+dataset = load_dataset("undertheseanlp/UTS_Text_v1")
+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()

generate_dataset.py

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-oid sha256:9de029f85cabeed131dcdcb77ea883ad63673b3646af90b6a5b8bacaaaddea57
-size 2403
+from os.path import join, dirname
+from underthesea.file_utils import DATASETS_FOLDER
+import random
+
+random.seed(10)
+text_file = join(DATASETS_FOLDER, "VNESES", "VNESEScorpus.txt")
+with open(text_file) as f:
+    lines = f.read().splitlines()
+NUM_LONG_TOKENS = 50
+NUM_SHORT_TOKENS = 20
+NUM_MAX_TOKENS = 150
+
+
+def longline_conditions(line):
+    if len(line) < NUM_LONG_TOKENS or len(line) > NUM_MAX_TOKENS:
+        return False
+    if not (line[0].isupper() and line[-1] == "."):
+        return False
+    return True
+
+
+long_lines = [line for line in lines if longline_conditions(line)]
+# get random 1000 lines
+random_long_lines = random.sample(long_lines, 5000)
+for line in random_long_lines[:20]:
+    print(line)
+
+
+def shortline_conditions(line):
+    if len(line) < NUM_SHORT_TOKENS:
+        return False
+    if len(line) > NUM_LONG_TOKENS:
+        return False
+    if not line[0].isupper():
+        return False
+    return True
+
+
+short_lines = [line for line in lines if shortline_conditions(line)]
+random_short_lines = random.sample(short_lines, 5000)
+for line in random_short_lines[:20]:
+    print(line)
+
+print("Long lines", len(random_long_lines))
+print("Short lines", len(random_short_lines))
+
+pwd = dirname(__file__)
+tmp = join(pwd, "data")
+corpus_file = join(tmp, "UTS_Text_v1.txt")
+
+with open(corpus_file, "w") as f:
+    lines = random_long_lines + random_short_lines
+    content = "\n".join(lines)
+    f.write(content)

statistics.py

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-oid sha256:4ff4285ba8a2a4ef12590894ae22a52a7499b684f54ca579892d07ed41715d32
-size 775
+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)
+
+pwd = dirname(__file__)
+folder = join(pwd, "data", "base") 
+
+print(count_lines(join(folder, "train.txt")))
+print(count_lines(join(folder, "validation.txt")))
+print(count_lines(join(folder, "test.txt")))

tmp/.gitignore

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+<<<<<<< HEAD
 version https://git-lfs.github.com/spec/v1
 oid sha256:5c89a8a9c5af258d02d0bfd3582c75963bea53baa1f5f5132662b80020a63452
 size 13
+=======
+*
+!.gitignore
+>>>>>>> origin/main