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The dataset preview is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ServerDisconnectedError
Message:      Server disconnected
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/utils.py", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
                  for key, example in self._iter():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
                  yield from ex_iterable
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/best2009/f794327515ddde43166d2e256099e1f6a3d63f13b3197d8242bc80ef2cdb2dcb/best2009.py", line 117, in _generate_examples
                  for file_idx, fname in enumerate(sorted(Path(filepath).rglob("*.txt"))):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 589, in glob
                  fs, *_ = fsspec.get_fs_token_paths(xjoin(posix_path, pattern), storage_options=storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 606, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 268, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 60, in __init__
                  self.zip = zipfile.ZipFile(self.fo, mode=mode)
                File "/usr/local/lib/python3.9/zipfile.py", line 1257, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1343, in _RealGetContents
                  data = fp.read(size_cd)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 574, in read
                  return super().read(length)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1575, in read
                  out = self.cache._fetch(self.loc, self.loc + length)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/caching.py", line 384, in _fetch
                  new = self.fetcher(start, self.start)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 111, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 96, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 53, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 608, in async_fetch_range
                  r = await self.session.get(self.url, headers=headers, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client.py", line 559, in _request
                  await resp.start(conn)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 898, in start
                  message, payload = await protocol.read()  # type: ignore[union-attr]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/streams.py", line 616, in read
                  await self._waiter
              aiohttp.client_exceptions.ServerDisconnectedError: Server disconnected

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Dataset Card for best2009

Dataset Summary

best2009 is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by NECTEC (148,995/2,252 lines of train/test). It was created for BEST 2010: Word Tokenization Competition. 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
  • 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 by NECTEC.

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

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.

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

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 for adding this dataset.

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