Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- th
+licenses:
+- cc-by-nc-sa-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 100k<n<1M
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- structure-prediction-other-word-tokenization
+---
+
+# Dataset Card for `best2009`
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#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)
+
+## 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}
+}
+```

best2009.py

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+from __future__ import absolute_import, division, print_function
+
+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 fname in sorted(Path(filepath).rglob("*.txt")):
+            with open(fname, encoding="utf-8") as f:
+                for _id, 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 _id, {
+                        "fname": fname.name,
+                        "char": chars,
+                        "char_type": char_types,
+                        "is_beginning": is_beginnings if split == "train" else [0 for i in range(len(chars))],
+                    }

dataset_infos.json

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+{"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}}

dummy/best2009/1.0.0/dummy_data.zip

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+size 18586