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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- th
-license:
-- cc-by-nc-sa-3.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- token-classification
-task_ids: []
-paperswithcode_id: null
-pretty_name: best2009
-tags:
-- word-tokenization
-dataset_info:
-  features:
-  - name: fname
-    dtype: string
-  - name: char
-    sequence: string
-  - name: char_type
-    sequence:
-      class_label:
-        names:
-          0: b_e
-          1: c
-          2: d
-          3: n
-          4: o
-          5: p
-          6: q
-          7: s
-          8: s_e
-          9: t
-          10: v
-          11: w
-  - name: is_beginning
-    sequence:
-      class_label:
-        names:
-          0: neg
-          1: pos
-  config_name: best2009
-  splits:
-  - name: train
-    num_bytes: 483129998
-    num_examples: 148995
-  - name: test
-    num_bytes: 10498726
-    num_examples: 2252
-  download_size: 13891260
-  dataset_size: 493628724
----
-
-# Dataset Card for `best2009`
-
-## 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)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://aiforthai.in.th/
-- **Repository:** https://aiforthai.in.th/corpus.php
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:** https://aiforthai.in.th/
-
-### Dataset Summary
-
-`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly.
-
-### Supported Tasks and Leaderboards
-
-word tokenization
-
-### Languages
-
-Thai
-
-## Dataset Structure
-
-### Data Instances
-
-```
-{'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]}
-{'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]}
-```
-
-### Data Fields
-
-- `fname`: file name; also marks if article is articles, news, encyclopedia or novels
-- `char`: characters
-- `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut)
-- `is_beginning`: is beginning of word
-
-### Data Splits
-
-|                         | train      | test    |
-|-------------------------|------------|---------|
-| # lines                 | 148,995    | 2,252   |
-| avg words per line      | 39.05      | NA      |
-| total words             | 5,818,521  | NA      |
-| avg characters per line | 140.39     | 202.79  |
-| total characters        | 20,918,132 | 456,684 |
-| # lines articles        | 16,990     | NA      |
-| # lines encyclopedia    | 50,631     | NA      |
-| # lines novels          | 50,140     | NA      |
-| # lines news            | 31,234     | NA      |
-
-## Dataset Creation
-
-### Curation Rationale
-
-The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/).
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-Respective authors of the articles, news, encyclopedia and novels
-
-### Annotations
-
-#### Annotation process
-
-Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf)
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-All data are curated from public sources. No personal and sensitive information is expected to be included.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-- word tokenization dataset from articles, news, encyclopedia and novels
-
-### Discussion of Biases
-
-- texts are relatively formal ones from articles, news, encyclopedia and novels.
-- word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf).
-
-### Other Known Limitations
-
-- some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out.
-- no word boundary provdied for the test set
-
-## Additional Information
-
-### Dataset Curators
-
-[NECTEC](https://www.nectec.or.th/)
-
-### Licensing Information
-
-CC-BY-NC-SA 3.0
-
-### Citation Information
-
-Dataset:
-```
-@inproceedings{kosawat2009best,
-  title={BEST 2009: Thai word segmentation software contest},
-  author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
-  booktitle={2009 Eighth International Symposium on Natural Language Processing},
-  pages={83--88},
-  year={2009},
-  organization={IEEE}
-}
-@inproceedings{boriboon2009best,
-  title={Best corpus development and analysis},
-  author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
-  booktitle={2009 International Conference on Asian Language Processing},
-  pages={322--327},
-  year={2009},
-  organization={IEEE}
-}
-```
-
-Character type features:
-```
-@inproceedings{haruechaiyasak2009tlex,
-  title={TLex: Thai lexeme analyser based on the conditional random fields},
-  author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot},
-  booktitle={Proceedings of 8th International Symposium on Natural Language Processing},
-  year={2009}
-}
-```
-
-### Contributions
-
-Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

best2009.py

@@ -1,138 +0,0 @@
-import os
-from functools import reduce
-from pathlib import Path
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{kosawat2009best,
-  title={BEST 2009: Thai word segmentation software contest},
-  author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
-  booktitle={2009 Eighth International Symposium on Natural Language Processing},
-  pages={83--88},
-  year={2009},
-  organization={IEEE}
-}
-@inproceedings{boriboon2009best,
-  title={Best corpus development and analysis},
-  author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
-  booktitle={2009 International Conference on Asian Language Processing},
-  pages={322--327},
-  year={2009},
-  organization={IEEE}
-}
-"""
-
-_LICENSE = "CC-BY-NC-SA 3.0"
-
-_DESCRIPTION = """\
-`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by
-[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for
-[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10).
-The test set answers are not provided publicly.
-"""
-
-
-class Best2009Config(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        """BuilderConfig
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Best2009Config, self).__init__(**kwargs)
-
-
-class Best2009(datasets.GeneratorBasedBuilder):
-
-    _DOWNLOAD_URL = "https://archive.org/download/best_dataset/data.zip"
-    _TRAIN_FOLDER = "train"
-    _TEST_FOLDER = "test"
-
-    _USELESS_TAGS = {"<NE>": "", "</NE>": "", "<AB>": "", "</AB>": ""}
-    # character type mapping from https://github.com/rkcosmos/deepcut/blob/master/deepcut/utils.py
-    _CHAR_TYPES_DICT = {
-        "กขฃคฆงจชซญฎฏฐฑฒณดตถทธนบปพฟภมยรลวศษสฬอ": "c",
-        "ฅฉผฟฌหฮ": "n",
-        "ะาำิีืึุู": "v",  # า ะ ำ ิ ี ึ ื ั ู ุ
-        "เแโใไ": "w",
-        "่้๊๋": "t",  # วรรณยุกต์ ่ ้ ๊ ๋
-        "์ๆฯ.": "s",  # ์  ๆ ฯ .
-        "0123456789๑๒๓๔๕๖๗๘๙": "d",
-        '"': "q",
-        "‘": "q",
-        "’": "q",
-        "'": "q",
-        " ": "p",
-        "abcdefghijklmnopqrstuvwxyz": "s_e",
-        "ABCDEFGHIJKLMNOPQRSTUVWXYZ": "b_e",
-    }
-    _CHAR_TYPE_FLATTEN = {}
-    for ks, v in _CHAR_TYPES_DICT.items():
-        for k in ks:
-            _CHAR_TYPE_FLATTEN[k] = v
-    _CHAR_TYPES = ["b_e", "c", "d", "n", "o", "p", "q", "s", "s_e", "t", "v", "w"]
-
-    BUILDER_CONFIGS = [
-        Best2009Config(
-            name="best2009",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "fname": datasets.Value("string"),
-                    "char": datasets.Sequence(datasets.Value("string")),
-                    "char_type": datasets.Sequence(datasets.features.ClassLabel(names=self._CHAR_TYPES)),
-                    "is_beginning": datasets.Sequence(datasets.features.ClassLabel(names=["neg", "pos"])),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://aiforthai.in.th/",
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
-        data_dir = os.path.join(arch_path, "data")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FOLDER), "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FOLDER), "split": "train"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        for file_idx, fname in enumerate(sorted(Path(filepath).rglob("*.txt"))):
-            with open(fname, encoding="utf-8") as f:
-                for line_idx, line in enumerate(f):
-                    chars = []
-                    char_types = []
-                    is_beginnings = []
-                    # replace useless tokens
-                    line = reduce(lambda a, kv: a.replace(*kv), self._USELESS_TAGS.items(), line)
-                    # tokens are pipe separated
-                    splits = line.split("|")
-                    for token in splits:
-                        for i in range(len(token)):
-                            chars.append(token[i])
-                            char_types.append(self._CHAR_TYPE_FLATTEN.get(token[i], "o"))
-                            is_beginning = 1 if i == 0 else 0
-                            is_beginnings.append(is_beginning)
-                    yield f"{file_idx}_{line_idx}", {
-                        "fname": fname.name,
-                        "char": chars,
-                        "char_type": char_types,
-                        "is_beginning": is_beginnings if split == "train" else [0 for i in range(len(chars))],
-                    }

best2009/best2009-test.parquet

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dataset_infos.json

@@ -1 +0,0 @@
-{"best2009": {"description": "`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by\n[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for\n[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10).\nThe test set answers are not provided publicly.\n", "citation": "@inproceedings{kosawat2009best,\n  title={BEST 2009: Thai word segmentation software contest},\n  author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},\n  booktitle={2009 Eighth International Symposium on Natural Language Processing},\n  pages={83--88},\n  year={2009},\n  organization={IEEE}\n}\n@inproceedings{boriboon2009best,\n  title={Best corpus development and analysis},\n  author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},\n  booktitle={2009 International Conference on Asian Language Processing},\n  pages={322--327},\n  year={2009},\n  organization={IEEE}\n}\n", "homepage": "https://aiforthai.in.th/", "license": "CC-BY-NC-SA 3.0", "features": {"fname": {"dtype": "string", "id": null, "_type": "Value"}, "char": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "char_type": {"feature": {"num_classes": 12, "names": ["b_e", "c", "d", "n", "o", "p", "q", "s", "s_e", "t", "v", "w"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "is_beginning": {"feature": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "best2009", "config_name": "best2009", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 483129998, "num_examples": 148995, "dataset_name": "best2009"}, "test": {"name": "test", "num_bytes": 10498726, "num_examples": 2252, "dataset_name": "best2009"}}, "download_checksums": {"https://archive.org/download/best_dataset/data.zip": {"num_bytes": 13891260, "checksum": "009386ea03aab2abd194bcb3b86c01b81038f460296c447ce2c0e561d3eca64f"}}, "download_size": 13891260, "post_processing_size": null, "dataset_size": 493628724, "size_in_bytes": 507519984}}